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Johnsen KA, Cruzado NA, Menard ZC, Willats AA, Charles AS, Markowitz JE, Rozell CJ. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.27.525963. [PMID: 39026717 PMCID: PMC11257437 DOI: 10.1101/2023.01.27.525963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.
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Affiliation(s)
- Kyle A. Johnsen
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Zachary C. Menard
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam A. Willats
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam S. Charles
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey E. Markowitz
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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2
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Ji Z, Guo S, Qiao Y, McDougal RA. Automating literature screening and curation with applications to computational neuroscience. J Am Med Inform Assoc 2024; 31:1463-1470. [PMID: 38722233 PMCID: PMC11187430 DOI: 10.1093/jamia/ocae097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/19/2024] [Accepted: 04/16/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVE ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). MATERIALS AND METHODS Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. RESULTS SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. DISCUSSION Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. CONCLUSION Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.
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Affiliation(s)
- Ziqing Ji
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
| | - Siyan Guo
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
| | - Yujie Qiao
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
- Integrative Genomics, Princeton University, Princeton, NJ 08540, United States
| | - Robert A McDougal
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
- Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT 06510, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
- Wu Tsai Institute, Yale University, New Haven, CT 06510, United States
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3
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Nicola W, Newton TR, Clopath C. The impact of spike timing precision and spike emission reliability on decoding accuracy. Sci Rep 2024; 14:10536. [PMID: 38719897 PMCID: PMC11078995 DOI: 10.1038/s41598-024-58524-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/01/2024] [Indexed: 05/12/2024] Open
Abstract
Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.
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Affiliation(s)
- Wilten Nicola
- University of Calgary, Calgary, Canada.
- Department of Cell Biology and Anatomy, Calgary, Canada.
- Hotchkiss Brain Institute, Calgary, Canada.
| | | | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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4
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Guarina L, Le JT, Griffith TN, Santana LF, Cudmore RH. SanPy: Software for the analysis and visualization of whole-cell current-clamp recordings. Biophys J 2024; 123:759-769. [PMID: 38419330 PMCID: PMC10995421 DOI: 10.1016/j.bpj.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/16/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
The analysis of action potentials and other membrane voltage fluctuations provides a powerful approach for interrogating the function of excitable cells. However, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed-upon software tools for its analysis. Here, we present SanPy, an open-source and freely available software package for the analysis and exploration of whole-cell current-clamp recordings written in Python. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform desktop application with a graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials, including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured, including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked, allowing simultaneous data visualization from different dimensions with the goal of obtaining ground-truth analysis. We provide documentation for all aspects of SanPy, including several use cases and examples. To test SanPy, we performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.
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Affiliation(s)
- Laura Guarina
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Johnson Tran Le
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Theanne N Griffith
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Luis Fernando Santana
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Robert H Cudmore
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California.
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5
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Fitz H, Hagoort P, Petersson KM. Neurobiological Causal Models of Language Processing. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:225-247. [PMID: 38645618 PMCID: PMC11025648 DOI: 10.1162/nol_a_00133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/18/2023] [Indexed: 04/23/2024]
Abstract
The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.
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Affiliation(s)
- Hartmut Fitz
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Peter Hagoort
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Karl Magnus Petersson
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Faculty of Medicine and Biomedical Sciences, University of Algarve, Faro, Portugal
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6
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Verardo C, Mele LJ, Selmi L, Palestri P. Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes. J Neural Eng 2024; 21:026002. [PMID: 38306702 DOI: 10.1088/1741-2552/ad2581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration, computational frameworks capturing the corresponding impact on neuronal activity are still missing.Approach. We present a finite-element model describing a PEDOT:PSS-coated microelectrode (herein, simplyionic actuator) able to release potassium and thus modulate the activity of a cortical neuron in anin-vitro-like setting. The dynamics of ions in the ionic actuator, the neural membrane, and the cellular fluids are solved self-consistently.Main results. We showcase the capability of the model to describe on a physical basis the modulation of the intrinsic excitability of the cell and of the synaptic transmission following the electro-ionic stimulation produced by the actuator. We consider three case studies for the ionic actuator with different levels of selectivity to potassium: ideal selectivity, no selectivity, and selectivity achieved by embedding ionophores in the polymer.Significance. This work is the first step toward a comprehensive computational framework aimed to investigate novel neuromodulation devices targeting specific ionic species, as well as to optimize their design and performance, in terms of the induced modulation of neural activity.
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Affiliation(s)
- Claudio Verardo
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Leandro Julian Mele
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States of America
| | - Luca Selmi
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | - Pierpaolo Palestri
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
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7
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. eLife 2024; 12:RP90597. [PMID: 38345923 PMCID: PMC10942544 DOI: 10.7554/elife.90597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024] Open
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W Wheeler
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Jeffrey D Kopsick
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Alexander O Komendantov
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
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8
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Nicola W. Rapid changes in synchronizability in conductance-based neuronal networks with conductance-based coupling. CHAOS (WOODBURY, N.Y.) 2024; 34:023123. [PMID: 38377288 DOI: 10.1063/5.0176956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/14/2024] [Indexed: 02/22/2024]
Abstract
Real neurons connect to each other non-randomly. These connectivity graphs can potentially impact the ability of networks to synchronize, along with the dynamics of neurons and the dynamics of their connections. How the connectivity of networks of conductance-based neuron models like the classical Hodgkin-Huxley model or the Morris-Lecar model impacts synchronizability remains unknown. One powerful tool to resolve the synchronizability of these networks is the master stability function (MSF). Here, we apply and extend the MSF approach to networks of Morris-Lecar neurons with conductance-based coupling to determine under which parameters and for which graphs the synchronous solutions are stable. We consider connectivity graphs with a constant non-zero row sum, where the MSF approach can be readily extended to conductance-based synapses rather than the more well-studied diffusive connectivity case, which primarily applies to gap junction connectivity. In this formulation, the synchronous solution is a single, self-coupled, or "autaptic" neuron. We find that the primary determining parameter for the stability of the synchronous solution is, unsurprisingly, the reversal potential, as it largely dictates the excitatory/inhibitory potential of a synaptic connection. However, the change between "excitatory" and "inhibitory" synapses is rapid, with only a few millivolts separating stability and instability of the synchronous state for most graphs. We also find that for specific coupling strengths (as measured by the global synaptic conductance), islands of synchronizability in the MSF can emerge for inhibitory connectivity. We verified the stability of these islands by direct simulation of pairs of neurons coupled with eigenvalues in the matching spectrum.
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9
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Tolley N, Rodrigues PLC, Gramfort A, Jones SR. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. PLoS Comput Biol 2024; 20:e1011108. [PMID: 38408099 PMCID: PMC10919875 DOI: 10.1371/journal.pcbi.1011108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 03/07/2024] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | | | | | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
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10
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Groden M, Moessinger HM, Schaffran B, DeFelipe J, Benavides-Piccione R, Cuntz H, Jedlicka P. A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites. PLoS Comput Biol 2024; 20:e1011267. [PMID: 38394339 PMCID: PMC10917450 DOI: 10.1371/journal.pcbi.1011267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/06/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org).
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Affiliation(s)
- Moritz Groden
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
| | - Hannah M. Moessinger
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
| | - Barbara Schaffran
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Spain
- Instituto Cajal (CSIC), Madrid, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Spain
- Instituto Cajal (CSIC), Madrid, Spain
| | - Hermann Cuntz
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Peter Jedlicka
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt am Main, Germany
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.12.540597. [PMID: 37425693 PMCID: PMC10327012 DOI: 10.1101/2023.05.12.540597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W. Wheeler
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Jeffrey D. Kopsick
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Alexander O. Komendantov
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
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12
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Schoeters R, Tarnaud T, Martens L, Tanghe E. Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging. J Neural Eng 2024; 20:066039. [PMID: 38109769 DOI: 10.1088/1741-2552/ad169c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 12/18/2023] [Indexed: 12/20/2023]
Abstract
Objective.Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested.Approach.This is done by calculating the forward electroencephalography response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density.Results.It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within Food and Drug Administration (FDA)-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.Significance.Our results show that mechanical vibration is a possible underlying mechanism of acousto-electrophyisological neuroimaging. This paper is a first step towards improved understanding of the conditions under which AENI is feasible.
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Affiliation(s)
- Ruben Schoeters
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Thomas Tarnaud
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Luc Martens
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Emmeric Tanghe
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
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13
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Pechmann S. Single-cell expression predicts neuron-specific protein homeostasis networks. Open Biol 2024; 14:230386. [PMID: 38262604 PMCID: PMC10805596 DOI: 10.1098/rsob.230386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 11/17/2023] [Indexed: 01/25/2024] Open
Abstract
The protein homeostasis network keeps proteins in their correct shapes and avoids unwanted aggregation. In turn, the accumulation of aberrantly misfolded proteins has been directly associated with the onset of ageing-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. However, a detailed and rational understanding of how protein homeostasis is achieved in health, and how it can be targeted for therapeutic intervention in diseases remains missing. Here, large-scale single-cell expression data from the Allen Brain Map are analysed to investigate the transcription regulation of the core protein homeostasis network across the human brain. Remarkably, distinct expression profiles suggest specialized protein homeostasis networks with systematic adaptations in excitatory neurons, inhibitory neurons and non-neuronal cells. Moreover, several chaperones and Ubiquitin ligases are found transcriptionally coregulated with genes important for synapse formation and maintenance, thus linking protein homeostasis to the regulation of neuronal function. Finally, evolutionary analyses highlight the conservation of an elevated interaction density in the chaperone network, suggesting that one of the most exciting aspects of chaperone action may yet be discovered in their collective action at the systems level. More generally, our work highlights the power of computational analyses for breaking down complexity and gaining complementary insights into fundamental biological problems.
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14
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Füzesi T, Rasiah NP, Rosenegger DG, Rojas-Carvajal M, Chomiak T, Daviu N, Molina LA, Simone K, Sterley TL, Nicola W, Bains JS. Hypothalamic CRH neurons represent physiological memory of positive and negative experience. Nat Commun 2023; 14:8522. [PMID: 38129411 PMCID: PMC10739955 DOI: 10.1038/s41467-023-44163-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Recalling a salient experience provokes specific behaviors and changes in the physiology or internal state. Relatively little is known about how physiological memories are encoded. We examined the neural substrates of physiological memory by probing CRHPVN neurons of mice, which control the endocrine response to stress. Here we show these cells exhibit contextual memory following exposure to a stimulus with negative or positive valence. Specifically, a negative stimulus invokes a two-factor learning rule that favors an increase in the activity of weak cells during recall. In contrast, the contextual memory of positive valence relies on a one-factor rule to decrease activity of CRHPVN neurons. Finally, the aversive memory in CRHPVN neurons outlasts the behavioral response. These observations provide information about how specific physiological memories of aversive and appetitive experience are represented and demonstrate that behavioral readouts may not accurately reflect physiological changes invoked by the memory of salient experiences.
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Affiliation(s)
- Tamás Füzesi
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
- CSM Optogenetics Core Facility, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Neilen P Rasiah
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - David G Rosenegger
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Mijail Rojas-Carvajal
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Taylor Chomiak
- CSM Optogenetics Core Facility, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Núria Daviu
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Leonardo A Molina
- CSM Optogenetics Core Facility, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Kathryn Simone
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Toni-Lee Sterley
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Wilten Nicola
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Jaideep S Bains
- Hotchkiss Brain Institute & Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada.
- Krembil Research Institute, University Health Network, Toronto, Canada.
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15
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Bologna LL, Tocco A, Smiriglia R, Romani A, Schürmann F, Migliore M. Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub. Front Neuroinform 2023; 17:1271059. [PMID: 38025966 PMCID: PMC10646550 DOI: 10.3389/fninf.2023.1271059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.
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Affiliation(s)
| | - Antonino Tocco
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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16
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Cudone E, Lower AM, McDougal RA. Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories. PLoS Comput Biol 2023; 19:e1011548. [PMID: 37824576 PMCID: PMC10597496 DOI: 10.1371/journal.pcbi.1011548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/24/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both a point and morphologically-detailed cell model, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a moderate number of recent events alone. Knowledge of initial conditions or full synaptic input history is not required. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases as the number of known input events increases, to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.
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Affiliation(s)
- Evan Cudone
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Amelia M. Lower
- Yale College, Yale University, New Haven, Connecticut, United States of America
| | - Robert A. McDougal
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, United States of America
- Wu Tsai Institute, Yale University, New Haven, Connecticut, United States of America
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17
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Zhang Y, He G, Ma L, Liu X, Hjorth JJJ, Kozlov A, He Y, Zhang S, Kotaleski JH, Tian Y, Grillner S, Du K, Huang T. A GPU-based computational framework that bridges neuron simulation and artificial intelligence. Nat Commun 2023; 14:5798. [PMID: 37723170 PMCID: PMC10507119 DOI: 10.1038/s41467-023-41553-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/08/2023] [Indexed: 09/20/2023] Open
Abstract
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.
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Affiliation(s)
- Yichen Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Gan He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Lei Ma
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
| | - Xiaofei Liu
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China
| | - J J Johannes Hjorth
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
| | - Alexander Kozlov
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yutao He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Shenjian Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yonghong Tian
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Electrical and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Tiejun Huang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China
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18
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Patel B, Soundarajan S, Ménager H, Hu Z. Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool. Sci Data 2023; 10:557. [PMID: 37612312 PMCID: PMC10447492 DOI: 10.1038/s41597-023-02463-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles tailored for research software have been proposed by the FAIR for Research Software (FAIR4RS) Working Group. They provide a foundation for optimizing the reuse of research software. The FAIR4RS principles are, however, aspirational and do not provide practical instructions to the researchers. To fill this gap, we propose in this work the first actionable step-by-step guidelines for biomedical researchers to make their research software compliant with the FAIR4RS principles. We designate them as the FAIR Biomedical Research Software (FAIR-BioRS) guidelines. Our process for developing these guidelines, presented here, is based on an in-depth study of the FAIR4RS principles and a thorough review of current practices in the field. To support researchers, we have also developed a workflow that streamlines the process of implementing these guidelines. This workflow is incorporated in FAIRshare, a free and open-source software application aimed at simplifying the curation and sharing of FAIR biomedical data and software through user-friendly interfaces and automation. Details about this tool are also presented.
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Affiliation(s)
- Bhavesh Patel
- FAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, 92121, USA.
| | - Sanjay Soundarajan
- FAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, 92121, USA
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 75015, Paris, France
| | - Zicheng Hu
- Computational Health Science, University of California San Francisco, San Francisco, CA, 94158, USA
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19
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Fietkiewicz C, McDougal RA, Corrales Marco D, Chiel HJ, Thomas PJ. Tutorial: using NEURON for neuromechanical simulations. Front Comput Neurosci 2023; 17:1143323. [PMID: 37583894 PMCID: PMC10424731 DOI: 10.3389/fncom.2023.1143323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/20/2023] [Indexed: 08/17/2023] Open
Abstract
The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models. Code available at https://github.com/fietkiewicz/PointerBuilder.
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Affiliation(s)
- Chris Fietkiewicz
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Section for Biomedical Informatics, Yale School of Medicine, New Haven, CT, United States
| | - David Corrales Marco
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Hillel J. Chiel
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Peter J. Thomas
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States
- Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, United States
- Department of Electrical, Control, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Data and Computer Science, Case Western Reserve University, Cleveland, OH, United States
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20
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Newton AJH, Chartash D, Kleinstein SH, McDougal RA. A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures. BMC Bioinformatics 2023; 24:292. [PMID: 37474900 PMCID: PMC10357743 DOI: 10.1186/s12859-023-05397-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 06/23/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes. IMPLEMENTATION We built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form. RESULTS We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. CONCLUSIONS Our results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.
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Affiliation(s)
- Adam J H Newton
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, 11203, USA
- Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, 06511, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA
| | - David Chartash
- Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, 06511, USA
- School of Medicine, University College Dublin - National University of Ireland, Dublin, Co. Dublin, Republic of Ireland
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA
- Department of Immunobiology, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA
| | - Robert A McDougal
- Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, CT, 06511, USA.
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, 06511, USA.
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA.
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21
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Trpevski D, Khodadadi Z, Carannante I, Hellgren Kotaleski J. Glutamate spillover drives robust all-or-none dendritic plateau potentials-an in silico investigation using models of striatal projection neurons. Front Cell Neurosci 2023; 17:1196182. [PMID: 37469606 PMCID: PMC10352111 DOI: 10.3389/fncel.2023.1196182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/31/2023] [Indexed: 07/21/2023] Open
Abstract
Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg2+ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg2+ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.
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Affiliation(s)
- Daniel Trpevski
- Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Zahra Khodadadi
- Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ilaria Carannante
- Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
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22
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Shin J, Porubsky V, Carothers J, Sauro HM. Standards, dissemination, and best practices in systems biology. Curr Opin Biotechnol 2023; 81:102922. [PMID: 37004298 PMCID: PMC10435326 DOI: 10.1016/j.copbio.2023.102922] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/14/2023] [Accepted: 02/24/2023] [Indexed: 04/03/2023]
Abstract
The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
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Affiliation(s)
- Janis Shin
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Veronica Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - James Carothers
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
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23
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Filipis L, Canepari M. Can neuron modeling constrained by ultrafast imaging data extract the native function of ion channels? Front Comput Neurosci 2023; 17:1192421. [PMID: 37293354 PMCID: PMC10244549 DOI: 10.3389/fncom.2023.1192421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Affiliation(s)
- Luiza Filipis
- Univ Grenoble Alpes, CNRS, LIPhy, Grenoble, France
- Laboratories of Excellence, Ion Channel Science and Therapeutics, Valbonne, France
| | - Marco Canepari
- Univ Grenoble Alpes, CNRS, LIPhy, Grenoble, France
- Laboratories of Excellence, Ion Channel Science and Therapeutics, Valbonne, France
- Institut National de la Santé et Recherche Médicale, Paris, France
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24
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Tikidji-Hamburyan RA, Govindaiah G, Guido W, Colonnese MT. Synaptic and circuit mechanisms prevent detrimentally precise correlation in the developing mammalian visual system. eLife 2023; 12:e84333. [PMID: 37211984 PMCID: PMC10202458 DOI: 10.7554/elife.84333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 04/25/2023] [Indexed: 05/23/2023] Open
Abstract
The developing visual thalamus and cortex extract positional information encoded in the correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of connectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit refinement period to explore the role of synaptic and circuit properties in the regulation of such neural correlations. We find that the NMDA receptor dominance, combined with weak recurrent excitation and inhibition characteristic of this age, prevents the emergence of spike-correlations between thalamocortical neurons on the millisecond timescale. Such precise correlations, which would emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the spatial information contained by thalamic spikes, and therefore we term them 'parasitic' correlations. Our results suggest that developing synapses and circuits evolved mechanisms to compensate for such detrimental parasitic correlations arising from the unrefined and immature circuit.
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Affiliation(s)
| | - Gubbi Govindaiah
- Department of Anatomical Sciences and Neurobiology, University of LouisvilleLouisvilleUnited States
| | - William Guido
- Department of Anatomical Sciences and Neurobiology, University of LouisvilleLouisvilleUnited States
| | - Matthew T Colonnese
- Department of Pharmacology and Physiology, The George Washington UniversityWashingtonUnited States
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25
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Haggie L, Schmid L, Röhrle O, Besier T, McMorland A, Saini H. Linking cortex and contraction-Integrating models along the corticomuscular pathway. Front Physiol 2023; 14:1095260. [PMID: 37234419 PMCID: PMC10206006 DOI: 10.3389/fphys.2023.1095260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.
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Affiliation(s)
- Lysea Haggie
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Schmid
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
| | - Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Angus McMorland
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Exercise Sciences, University of Auckland, Auckland, New Zealand
| | - Harnoor Saini
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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26
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Tolley N, Rodrigues PLC, Gramfort A, Jones S. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.537118. [PMID: 37131818 PMCID: PMC10153146 DOI: 10.1101/2023.04.17.537118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, RI, United States
| | | | | | - Stephanie Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
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27
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Nair SS, Muddapu VR, Vigneswaran C, Balasubramani PP, Ramanathan DS, Mishra J, Chakravarthy VS. A generalized reinforcement learning based deep neural network agent model for diverse cognitive constructs. Sci Rep 2023; 13:5928. [PMID: 37045887 PMCID: PMC10097685 DOI: 10.1038/s41598-023-32234-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/24/2023] [Indexed: 04/14/2023] Open
Abstract
Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modeled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.
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Affiliation(s)
- Sandeep Sathyanandan Nair
- Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Room 505, Block 1, Sardar Patel Road, Adyar, Chennai, Tamil Nadu, 600036, India
| | - Vignayanandam Ravindernath Muddapu
- Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Room 505, Block 1, Sardar Patel Road, Adyar, Chennai, Tamil Nadu, 600036, India
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - C Vigneswaran
- Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Room 505, Block 1, Sardar Patel Road, Adyar, Chennai, Tamil Nadu, 600036, India
| | - Pragathi P Balasubramani
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, Indian Institute of Technology, Kanpur, Kanpur, India
| | - Dhakshin S Ramanathan
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA, USA
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - V Srinivasa Chakravarthy
- Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Room 505, Block 1, Sardar Patel Road, Adyar, Chennai, Tamil Nadu, 600036, India.
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28
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Chen Z, Carroll M, Nair SS. Inferring Pyramidal Neuron Morphology using EAP Data. INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING : [PROCEEDINGS]. INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2023; 2023:10.1109/ner52421.2023.10123903. [PMID: 37309450 PMCID: PMC10259830 DOI: 10.1109/ner52421.2023.10123903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.
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Affiliation(s)
- Ziao Chen
- Electrical Engineering and Computer Science, University of Missouri, Columbia MO 65211
| | - Matthew Carroll
- Electrical Engineering and Computer Science, University of Missouri, Columbia MO 65211
| | - Satish S Nair
- Electrical Engineering and Computer Science, University of Missouri, Columbia MO 65211
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29
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Zhang Y, Du K, Huang T. Heuristic Tree-Partition-Based Parallel Method for Biophysically Detailed Neuron Simulation. Neural Comput 2023; 35:627-644. [PMID: 36746142 DOI: 10.1162/neco_a_01565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/20/2022] [Indexed: 02/08/2023]
Abstract
Biophysically detailed neuron simulation is a powerful tool to explore the mechanisms behind biological experiments and bridge the gap between various scales in neuroscience research. However, the extremely high computational complexity of detailed neuron simulation restricts the modeling and exploration of detailed network models. The bottleneck is solving the system of linear equations. To accelerate detailed simulation, we propose a heuristic tree-partition-based parallel method (HTP) to parallelize the computation of the Hines algorithm, the kernel for solving linear equations, and leverage the strong parallel capability of the graphic processing unit (GPU) to achieve further speedup. We formulate the problem of how to get a fine parallel process as a tree-partition problem. Next, we present a heuristic partition algorithm to obtain an effective partition to efficiently parallelize the equation-solving process in detailed simulation. With further optimization on GPU, our HTP method achieves 2.2 to 8.5 folds speedup compared to the state-of-the-art GPU method and 36 to 660 folds speedup compared to the typical Hines algorithm.
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Affiliation(s)
- Yichen Zhang
- School of Computer Science, Peking University, Beijing 100871, China
| | - Kai Du
- School of Computer Science and Institute for Artificial Intelligence, Peking University, Beijing 100871, China
| | - Tiejun Huang
- School of Computer Science and Institute for Artificial Intelligence, Peking University, Beijing 100871, China
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30
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Zaletel I, Nowakowski RS, Ness TV. Editorial: Open-access data, models and resources in neuroscience research. Front Neurosci 2023; 17:1142317. [PMID: 36866334 PMCID: PMC9972088 DOI: 10.3389/fnins.2023.1142317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/03/2023] [Indexed: 02/16/2023] Open
Affiliation(s)
- Ivan Zaletel
- Institute of Histology and Embryology “Aleksandar Ð. Kostić”, Faculty of Medicine, University of Belgrade, Belgrade, Serbia,*Correspondence: Ivan Zaletel ✉
| | - Richard S. Nowakowski
- Department of Biomedical Sciences, Florida State University, College of Medicine, Tallahassee, FL, United States
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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31
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Denizot A, Arizono M, Nägerl UV, Berry H, De Schutter E. Control of Ca 2+ signals by astrocyte nanoscale morphology at tripartite synapses. Glia 2022; 70:2378-2391. [PMID: 36097958 PMCID: PMC9825906 DOI: 10.1002/glia.24258] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 01/11/2023]
Abstract
Much of the Ca2+ activity in astrocytes is spatially restricted to microdomains and occurs in fine processes that form a complex anatomical meshwork, the so-called spongiform domain. A growing body of literature indicates that those astrocytic Ca2+ signals can influence the activity of neuronal synapses and thus tune the flow of information through neuronal circuits. Because of technical difficulties in accessing the small spatial scale involved, the role of astrocyte morphology on Ca2+ microdomain activity remains poorly understood. Here, we use computational tools and idealized 3D geometries of fine processes based on recent super-resolution microscopy data to investigate the mechanistic link between astrocytic nanoscale morphology and local Ca2+ activity. Simulations demonstrate that the nano-morphology of astrocytic processes powerfully shapes the spatio-temporal properties of Ca2+ signals and promotes local Ca2+ activity. The model predicts that this effect is attenuated upon astrocytic swelling, hallmark of brain diseases, which we confirm experimentally in hypo-osmotic conditions. Upon repeated neurotransmitter release events, the model predicts that swelling hinders astrocytic signal propagation. Overall, this study highlights the influence of the complex morphology of astrocytes at the nanoscale and its remodeling in pathological conditions on neuron-astrocyte communication at so-called tripartite synapses, where astrocytic processes come into close contact with pre- and postsynaptic structures.
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Affiliation(s)
- Audrey Denizot
- Computational Neuroscience UnitOkinawa Institute of Science and TechnologyOnna‐SonJapan
| | - Misa Arizono
- Interdisciplinary Institute for NeuroscienceUniversité de BordeauxBordeauxFrance,Interdisciplinary Institute for NeuroscienceCNRS UMR 5297BordeauxFrance,Department of PharmacologyKyoto University Graduate School of MedicineKyotoJapan
| | - U. Valentin Nägerl
- Interdisciplinary Institute for NeuroscienceUniversité de BordeauxBordeauxFrance,Interdisciplinary Institute for NeuroscienceCNRS UMR 5297BordeauxFrance
| | - Hugues Berry
- LIRIS, UMR5205 CNRSUniv LyonVilleurbanneFrance,INRIAVilleurbanneFrance
| | - Erik De Schutter
- Computational Neuroscience UnitOkinawa Institute of Science and TechnologyOnna‐SonJapan
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32
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Zavalis EA, Ioannidis JPA. A meta-epidemiological assessment of transparency indicators of infectious disease models. PLoS One 2022; 17:e0275380. [PMID: 36206207 PMCID: PMC9543956 DOI: 10.1371/journal.pone.0275380] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/15/2022] [Indexed: 01/04/2023] Open
Abstract
Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
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Affiliation(s)
- Emmanuel A. Zavalis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Solna, Stockholm, Sweden
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, California, United States of America
- * E-mail:
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33
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Schulte to Brinke T, Duarte R, Morrison A. Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model. Front Integr Neurosci 2022; 16:923468. [PMID: 36310713 PMCID: PMC9615567 DOI: 10.3389/fnint.2022.923468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022] Open
Abstract
The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.
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Affiliation(s)
- Tobias Schulte to Brinke
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
- *Correspondence: Tobias Schulte to Brinke
| | - Renato Duarte
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
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34
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Bologna LL, Smiriglia R, Lupascu CA, Appukuttan S, Davison AP, Ivaska G, Courcol JD, Migliore M. The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models. Front Neuroinform 2022; 16:991609. [PMID: 36225653 PMCID: PMC9549939 DOI: 10.3389/fninf.2022.991609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/06/2022] [Indexed: 11/27/2022] Open
Abstract
In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.
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Affiliation(s)
- Luca Leonardo Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
- *Correspondence: Luca Leonardo Bologna,
| | | | | | - Shailesh Appukuttan
- Centre National de la Recherche Scientifique, Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, Saclay, France
| | - Andrew P. Davison
- Centre National de la Recherche Scientifique, Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, Saclay, France
| | - Genrich Ivaska
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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35
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Connectivity concepts in neuronal network modeling. PLoS Comput Biol 2022; 18:e1010086. [PMID: 36074778 PMCID: PMC9455883 DOI: 10.1371/journal.pcbi.1010086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022] Open
Abstract
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
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36
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Aussel A, Ranta R, Aron O, Colnat-Coulbois S, Maillard L, Buhry L. Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations. J Comput Neurosci 2022; 50:519-535. [PMID: 35971033 DOI: 10.1007/s10827-022-00829-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 07/03/2022] [Accepted: 07/12/2022] [Indexed: 10/15/2022]
Abstract
The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.
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Affiliation(s)
- Amélie Aussel
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France. .,Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.
| | - Radu Ranta
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France
| | - Olivier Aron
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Sophie Colnat-Coulbois
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Louise Maillard
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Laure Buhry
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France
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37
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Castro EV, Shepherd JW, Guggenheim RS, Sengvoravong M, Hall BC, Chappell MK, Hearn JA, Caraccio ON, Bissman C, Lantow S, Buehner D, Costlow HR, Prather DM, Zonza AM, Witt M, Zahratka JA. ChanFAD: A Functional Annotation Database for Ion Channels. FRONTIERS IN BIOINFORMATICS 2022; 2:835805. [PMID: 36304304 PMCID: PMC9580856 DOI: 10.3389/fbinf.2022.835805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are integral membrane protein complexes critical for regulation of membrane potential, cell volume, and other signaling events. As complex molecular assemblies with many interacting partners, ion channels have multiple structural and functional domains. While channel sequence and functional data are readily available across multiple online resources, there is an unmet need for functional annotation directly relating primary sequence information, 2D interactions, and three-dimensional protein structure. To this end, we present ChanFAD (Channel Functional Annotation Database), to provide the research community with a centralized resource for ion channel structure and functional data. ChanFAD provides functional annotation of PDB structures built on the National Center for Biotechnology Information’s iCn3D structure viewing tool while providing additional information such as primary sequence, organism, and relevant links to other databases. Here we provide a brief tour of ChanFAD functionality while showing example use cases involving drug-channel interactions and structural changes based on mutation. ChanFAD is freely available and can be accessed at https://www.chanfad.org/.
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Affiliation(s)
- Elizabeth V. Castro
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Psychology, Baldwin Wallace University, Berea, OH, United States
| | - John W. Shepherd
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - Ryan S. Guggenheim
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Psychology, Baldwin Wallace University, Berea, OH, United States
| | | | - Bailey C. Hall
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - McKenzie K. Chappell
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Biology and Geology, Baldwin Wallace University, Berea, OH, United States
| | - Jessica A. Hearn
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Biology and Geology, Baldwin Wallace University, Berea, OH, United States
| | - Olivia N. Caraccio
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - Cora Bissman
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Biology and Geology, Baldwin Wallace University, Berea, OH, United States
| | - Sydney Lantow
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - Damian Buehner
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - Harry R. Costlow
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - David M. Prather
- Department of Chemistry, Baldwin Wallace University, Berea, OH, United States
| | - Abigail M. Zonza
- Department of Biology and Geology, Baldwin Wallace University, Berea, OH, United States
| | - Mallory Witt
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
| | - Jeffrey A. Zahratka
- Department of Neuroscience, Baldwin Wallace University, Berea, OH, United States
- Department of Biology and Geology, Baldwin Wallace University, Berea, OH, United States
- *Correspondence: Jeffrey A. Zahratka,
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38
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Exact mean-field models for spiking neural networks with adaptation. J Comput Neurosci 2022; 50:445-469. [PMID: 35834100 DOI: 10.1007/s10827-022-00825-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/15/2022] [Indexed: 10/17/2022]
Abstract
Networks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field models derived from spiking neural networks are extremely valuable, as such models can be used to determine how individual neurons and the network they reside within interact to produce macroscopic network behaviours. In the paper, we derive and analyze a set of exact mean-field equations for the neural network with spike frequency adaptation. Specifically, our model is a network of Izhikevich neurons, where each neuron is modeled by a two dimensional system consisting of a quadratic integrate and fire equation plus an equation which implements spike frequency adaptation. Previous work deriving a mean-field model for this type of network, relied on the assumption of sufficiently slow dynamics of the adaptation variable. However, this approximation did not succeed in establishing an exact correspondence between the macroscopic description and the realistic neural network, especially when the adaptation time constant was not large. The challenge lies in how to achieve a closed set of mean-field equations with the inclusion of the mean-field dynamics of the adaptation variable. We address this problem by using a Lorentzian ansatz combined with the moment closure approach to arrive at a mean-field system in the thermodynamic limit. The resulting macroscopic description is capable of qualitatively and quantitatively describing the collective dynamics of the neural network, including transition between states where the individual neurons exhibit asynchronous tonic firing and synchronous bursting. We extend the approach to a network of two populations of neurons and discuss the accuracy and efficacy of our mean-field approximations by examining all assumptions that are imposed during the derivation. Numerical bifurcation analysis of our mean-field models reveals bifurcations not previously observed in the models, including a novel mechanism for emergence of bursting in the network. We anticipate our results will provide a tractable and reliable tool to investigate the underlying mechanism of brain function and dysfunction from the perspective of computational neuroscience.
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39
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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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40
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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41
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Panagiotou S, Sidiropoulos H, Soudris D, Negrello M, Strydis C. EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator. Front Neuroinform 2022; 16:724336. [PMID: 35669596 PMCID: PMC9167055 DOI: 10.3389/fninf.2022.724336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modeling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML-v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs from one to nearly two orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available.
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Affiliation(s)
- Sotirios Panagiotou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Harry Sidiropoulos
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Dimitrios Soudris
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Mario Negrello
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Christos Strydis
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- Quantum and Computer Engineering Department, Delft University of Technology, Delft, Netherlands
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42
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Shaikh B, Smith LP, Vasilescu D, Marupilla G, Wilson M, Agmon E, Agnew H, Andrews SS, Anwar A, Beber ME, Bergmann FT, Brooks D, Brusch L, Calzone L, Choi K, Cooper J, Detloff J, Drawert B, Dumontier M, Ermentrout G, Faeder J, Freiburger A, Fröhlich F, Funahashi A, Garny A, Gennari J, Gleeson P, Goelzer A, Haiman Z, Hasenauer J, Hellerstein J, Hermjakob H, Hoops S, Ison J, Jahn D, Jakubowski H, Jordan R, Kalaš M, König M, Liebermeister W, Sheriff RM, Mandal S, McDougal R, Medley J, Mendes P, Müller R, Myers C, Naldi A, Nguyen TVN, Nickerson D, Olivier B, Patoliya D, Paulevé L, Petzold L, Priya A, Rampadarath A, Rohwer JM, Saglam A, Singh D, Sinha A, Snoep J, Sorby H, Spangler R, Starruß J, Thomas P, van Niekerk D, Weindl D, Zhang F, Zhukova A, Goldberg A, Schaff J, Blinov M, Sauro H, Moraru I, Karr J. BioSimulators: a central registry of simulation engines and services for recommending specific tools. Nucleic Acids Res 2022; 50:W108-W114. [PMID: 35524558 PMCID: PMC9252793 DOI: 10.1093/nar/gkac331] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/07/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.
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Affiliation(s)
- Bilal Shaikh
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Dan Vasilescu
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Michael Wilson
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Eran Agmon
- Stanford University, Stanford, CA 94305, USA
| | | | | | - Azraf Anwar
- New York University, Brooklyn, NY 11201, USA
| | | | | | - David Brooks
- University of Auckland, 1010 Auckland, New Zealand
| | - Lutz Brusch
- Technical University of Dresden, 01187 Dresden, Germany
| | | | - Kiri Choi
- Korea Institute for Advanced Study, 02455 Seoul, South Korea
| | - Joshua Cooper
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | - Brian Drawert
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | | | | | | | | | | | - Alan Garny
- University of Auckland, 1010 Auckland, New Zealand
| | | | | | - Anne Goelzer
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Zachary Haiman
- University of California, San Diego, La Jolla, CA 92093, USA
| | | | | | - Henning Hermjakob
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Stefan Hoops
- University of Virginia, Charlottesville, VA 22904, USA
| | - Jon C Ison
- CNRS, UMS 3601, Institut Français de Bioinformatique, IFB-core, 91000 Évry-Courcouronnes, France
| | - Diego Jahn
- Technical University of Dresden, 01187 Dresden, Germany
| | - Henry V Jakubowski
- College of Saint Benedict and Saint John’s University, St. Joseph, MN 56374, USA
| | - Ryann Jordan
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | | | - Rahuman S Malik Sheriff
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | | | | | - Pedro Mendes
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Robert Müller
- Technical University of Dresden, 01187 Dresden, Germany
| | - Chris J Myers
- University of Colorado at Boulder, Boulder CO, 80309, USA
| | - Aurelien Naldi
- Inria Saclay - Île-de-France Research Centre, 91120 Palaiseau, France
| | - Tung V N Nguyen
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | - Brett G Olivier
- Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, Netherlands
| | - Drashti Patoliya
- Sarvajanik College of Engineering & Technology, Surat, Gujarat 395001, India
| | - Loïc Paulevé
- Centre National de la Recherche Scientifique, 33400 Talence, France
| | - Linda R Petzold
- University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Ankita Priya
- Birla Institute of Technology, Mesra, Jharkhand 835215, India
| | | | | | - Ali S Saglam
- University of Pittsburgh, Pittsburgh, PA 15260, USA
| | | | - Ankur Sinha
- University College London, London, WC1E 6BT, UK
| | - Jacky Snoep
- Stellenbosch University, Stellenbosch, 7600, South Africa
| | - Hugh Sorby
- University of Auckland, 1010 Auckland, New Zealand
| | - Ryan Spangler
- Allen Institute for Cell Science, Seattle, WA 98109, USA
| | - Jörn Starruß
- Technical University of Dresden, 01187 Dresden, Germany
| | | | | | - Daniel Weindl
- Helmholtz Zentrum München GmbH and German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Fengkai Zhang
- National Institutes of Health, Bethesda, MD 20892, USA
| | | | | | - James C Schaff
- University of Connecticut School of Medicine, Farmington, CT 06030, USA,Applied BioMath LLC, Concord, MA 01742, USA
| | - Michael L Blinov
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Ion I Moraru
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
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43
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Huertas MA, Newton AJH, McDougal RA, Sacktor TC, Shouval HZ. Conditions for Synaptic Specificity during the Maintenance Phase of Synaptic Plasticity. eNeuro 2022; 9:ENEURO.0064-22.2022. [PMID: 35443991 PMCID: PMC9087736 DOI: 10.1523/eneuro.0064-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/31/2022] [Indexed: 01/21/2023] Open
Abstract
Activity-dependent modifications of synaptic efficacies are a cellular substrate of learning and memory. Experimental evidence shows that these modifications are synapse specific and that the long-lasting effects are associated with the sustained increase in concentration of specific proteins like PKMζ However, such proteins are likely to diffuse away from their initial synaptic location and spread out to neighboring synapses, potentially compromising synapse specificity. In this article, we address the issue of synapse specificity during memory maintenance. Assuming that the long-term maintenance of synaptic plasticity is accomplished by a molecular switch, we carry out analytical calculations and perform simulations using the reaction-diffusion package in NEURON to determine the limits of synapse specificity during maintenance. Moreover, we explore the effects of the diffusion and degradation rates of proteins and of the geometrical characteristics of dendritic spines on synapse specificity. We conclude that the necessary conditions for synaptic specificity during maintenance require that molecular switches reside in dendritic spines. The requirement for synaptic specificity when the molecular switch resides in spines still imposes strong limits on the diffusion and turnover of rates of maintenance molecules, as well as on the morphologic properties of synaptic spines. These constraints are quite general and apply to most existing models suggested for maintenance. The parameter values can be experimentally evaluated, and if they do not fit the appropriate predicted range, the validity of this class of maintenance models would be challenged.
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Affiliation(s)
- Marco A Huertas
- Department of Neurobiology and Anatomy, University of Texas Medical School, Houston, TX 77030
| | - Adam J H Newton
- Yale Center for Medical Informatics, New Haven, CT 06520
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY 11203
| | - Robert A McDougal
- Yale Center for Medical Informatics, New Haven, CT 06520
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520
| | - Todd Charlton Sacktor
- Department of Physiology, Pharmacology, Anesthesiology, and Neurology, SUNY Downstate Health Sciences University, Brooklyn, NY 11203
| | - Harel Z Shouval
- Department of Neurobiology and Anatomy, University of Texas Medical School, Houston, TX 77030
- Department Electrical and Computer Engineering, Rice University, Houston, TX 77005
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44
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Cao F, Ralph MR, Stinchcombe AR. A Phenomenological Mouse Circadian Pacemaker Model. J Biol Rhythms 2022; 37:329-342. [PMID: 35485260 PMCID: PMC9160958 DOI: 10.1177/07487304221085455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Mathematical models have been used extensively in chronobiology to explore characteristics of biological clocks. In particular, for human circadian studies, the Kronauer model has been modified multiple times to describe rhythm production and responses to sensory input. This phenomenological model comprises a single set of parameters which can simulate circadian responses in humans under a variety of environmental conditions. However, corresponding models for nocturnal rodents commonly used in circadian rhythm studies are not available and may require new parameter values for different species and even strains. Moreover, due to a considerable variation in experimental data collected from mice of the same strain, within and across laboratories, a range of valid parameters is essential. This study develops a Kronauer-like model for mice by re-fitting relevant parameters to published phase response curve and period data using total least squares. Local parameter sensitivity analysis and parameter distributions determine the parameter ranges that give a near-identical model and data distribution of periods. However, the model required further parameter adjustments to match characteristics of other mouse strains, implying that the model itself detects changes in the core processes of rhythm generation and control. The model is a useful tool to understand and interpret future mouse circadian clock experiments.
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Affiliation(s)
- Federico Cao
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
| | - Martin R Ralph
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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45
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Linne ML, Aćimović J, Saudargiene A, Manninen T. Neuron-Glia Interactions and Brain Circuits. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:87-103. [PMID: 35471536 DOI: 10.1007/978-3-030-89439-9_4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.
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Affiliation(s)
- Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Jugoslava Aćimović
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ausra Saudargiene
- Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania.,Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Sinha N, Joshi RB, Sandhu MRS, Netoff TI, Zaveri HP, Lehnertz K. Perspectives on Understanding Aberrant Brain Networks in Epilepsy. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:868092. [PMID: 36926081 PMCID: PMC10013006 DOI: 10.3389/fnetp.2022.868092] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 01/21/2023]
Abstract
Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.
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Affiliation(s)
- Nishant Sinha
- Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Rasesh B. Joshi
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Theoden I. Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Hitten P. Zaveri
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Effects of transcranial alternating current stimulation on spiking activity in computational models of single neocortical neurons. Neuroimage 2022; 250:118953. [PMID: 35093517 PMCID: PMC9087863 DOI: 10.1016/j.neuroimage.2022.118953] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 11/24/2022] Open
Abstract
Neural oscillations are a key mechanism for information transfer in brain circuits. Rhythmic fluctuations of local field potentials control spike timing through cyclic membrane de- and hyperpolarization. Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation method which can directly interact with brain oscillatory activity by imposing an oscillating electric field on neurons. Despite its increasing use, the basic mechanisms of tACS are still not fully understood. Here, we investigate in a computational study the effects of tACS on morphologically realistic neurons with ongoing spiking activity. We characterize the membrane polarization as a function of electric field strength and subsequent effects on spiking activity in a set of 25 neurons from different neocortical layers. We find that tACS does not affect the firing rate of investigated neurons for electric field strengths applicable to human studies. However, we find that the applied electric fields entrain the spiking activity of large pyramidal neurons and large basket neurons at < 1 mV/mm field strengths. Our model results are in line with recent experimental studies and can provide a mechanistic framework to understand the effects of oscillating electric fields on single neuron activity. They highlight the importance of neuron morphology and biophysics in responsiveness to electrical stimulation.
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Ben-Shalom R, Ladd A, Artherya NS, Cross C, Kim KG, Sanghevi H, Korngreen A, Bouchard KE, Bender KJ. NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs. J Neurosci Methods 2022; 366:109400. [PMID: 34728257 PMCID: PMC9887806 DOI: 10.1016/j.jneumeth.2021.109400] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 10/09/2021] [Accepted: 10/27/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW METHOD Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING METHODS NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. CONCLUSIONS Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.
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Affiliation(s)
- Roy Ben-Shalom
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States,MIND Institute University of California, Davis, CA, United States,Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States,Correspondence to: University of California, Davis MIND Institute Wet Lab 2805 50th Street, Room 2460 Sacramento, CA 95817, United States., (R. Ben-Shalom), (K.J. Bender)
| | - Alexander Ladd
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Nikhil S. Artherya
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Christopher Cross
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Kyung Geun Kim
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Hersh Sanghevi
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Alon Korngreen
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Kristofer E. Bouchard
- Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States,Hellen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States,Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, CA, United States
| | - Kevin J. Bender
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
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Voina D, Recanatesi S, Hu B, Shea-Brown E, Mihalas S. Single Circuit in V1 Capable of Switching Contexts during Movement Using an Inhibitory Population as a Switch. Neural Comput 2022; 34:541-594. [PMID: 35016220 DOI: 10.1162/neco_a_01472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 09/21/2021] [Indexed: 11/04/2022]
Abstract
As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.
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Affiliation(s)
- Doris Voina
- Applied Mathematics, University of Washington, Seattle, WA 98195 U.S.A.
| | - Stefano Recanatesi
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A.
| | - Brian Hu
- Allen Institute for Brain Science, Seattle, WA 98109 U.S.A
| | - Eric Shea-Brown
- Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A., and Allen Institute for Brain Science, Seattle, WA 98109, U.S.A.
| | - Stefan Mihalas
- Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A., and Allen Institute for Brain Science, Seattle, WA 98109, U.S.A.
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Nair SS, Muddapu VR, Chakravarthy VS. A Multiscale, Systems-Level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching Under Normal, Parkinsonian, and Levodopa Medication Conditions. Front Comput Neurosci 2022; 15:756881. [PMID: 35046787 PMCID: PMC8762321 DOI: 10.3389/fncom.2021.756881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 11/30/2021] [Indexed: 12/13/2022] Open
Abstract
In order to understand the link between substantia nigra pars compacta (SNc) cell loss and Parkinson's disease (PD) symptoms, we developed a multiscale computational model that can replicate the symptoms at the behavioural level by incorporating the key cellular and molecular mechanisms underlying PD pathology. There is a modelling tradition that links dopamine to reward and uses reinforcement learning (RL) concepts to model the basal ganglia. In our model, we replace the abstract representations of reward with the realistic variable of extracellular DA released by a network of SNc cells and incorporate it in the RL-based behavioural model, which simulates the arm reaching task. Our results successfully replicated the impact of SNc cell loss and levodopa (L-DOPA) medication on reaching performance. It also shows the side effects of medication, such as wearing off and peak dosage dyskinesias. The model demonstrates how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD. It was able to predict the optimum L-DOPA medication dosage for varying degrees of cell loss. The proposed model has a potential clinical application where drug dosage can be optimised as per patient characteristics.
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Affiliation(s)
- Sandeep Sathyanandan Nair
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Vignayanandam Ravindernath Muddapu
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - V. Srinivasa Chakravarthy
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Center for Complex Systems and Dynamics, Indian Institute of Technology Madras, Chennai, India
- *Correspondence: V. Srinivasa Chakravarthy
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