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Guo Y, Lv M, Wang C, Ma J. Energy controls wave propagation in a neural network with spatial stimuli. Neural Netw 2024; 171:1-13. [PMID: 38091753 DOI: 10.1016/j.neunet.2023.11.042] [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: 09/07/2023] [Revised: 10/16/2023] [Accepted: 11/19/2023] [Indexed: 01/29/2024]
Abstract
Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with energy diversity. By using a feasible model, energy function is obtained to predict the growth of synaptic connections of these neurons. Distribution of average value of the Hamilton energy function vs. intensity of noisy disturbance can predict the occurrence of coherence resonance, which the neural activities show high regularity by applying noisy disturbance with moderate intensity. From physical viewpoint, the average energy value has similar role average power for the neuron. Non-uniform spatial disturbance is applied and energy is injected into the neural network, statistical synchronization factor is calculated to predict the network synchronization stability and wave propagation. The intensity for field coupling is adaptively controlled by energy diversity between adjacent neurons. Local energy balance will terminate further growth of the coupling intensity; otherwise, heterogeneity is formed in the network due to energy diversity. Furthermore, memristive channel current is introduced into the neuron model for perceiving the effect of electromagnetic induction and radiation, and a memristive neuron is obtained. The circuit implement of memristive circuit depends on the connection to a magnetic flux-controlled memristor into the mentioned neural circuit in an additive branch circuit. The connection and activation of this memristive neural network are controlled under external spatial electromagnetic radiation by capturing enough field energy. Continuous energy collection and exchange generate energy diversity and synaptic connection is created to regulate the synchronous firing patterns and energy balance.
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Affiliation(s)
- Yitong Guo
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
| | - Mi Lv
- Faculty of Engineering, China University of Petroleum-Beijing at Karamay, Karamay, 834000, Xinjiang, PR China
| | - Chunni Wang
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China; Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
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2
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Ma X, Miraucourt LS, Qiu H, Sharif-Naeini R, Khadra A. Modulation of SK Channels via Calcium Buffering Tunes Intrinsic Excitability of Parvalbumin Interneurons in Neuropathic Pain: A Computational and Experimental Investigation. J Neurosci 2023; 43:5608-5622. [PMID: 37451982 PMCID: PMC10401647 DOI: 10.1523/jneurosci.0426-23.2023] [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: 03/07/2023] [Revised: 05/11/2023] [Accepted: 05/18/2023] [Indexed: 07/18/2023] Open
Abstract
Parvalbumin-expressing interneurons (PVINs) play a crucial role within the dorsal horn of the spinal cord by preventing touch inputs from activating pain circuits. In both male and female mice, nerve injury decreases PVINs' output via mechanisms that are not fully understood. In this study, we show that PVINs from nerve-injured male mice change their firing pattern from tonic to adaptive. To examine the ionic mechanisms responsible for this decreased output, we used a reparametrized Hodgkin-Huxley type model of PVINs, which predicted (1) the firing pattern transition is because of an increased contribution of small conductance calcium-activated potassium (SK) channels, enabled by (2) impairment in intracellular calcium buffering systems. Analyzing the dynamics of the Hodgkin-Huxley type model further demonstrated that a generalized Hopf bifurcation differentiates the two types of state transitions observed in the transient firing of PVINs. Importantly, this predicted mechanism holds true when we embed the PVIN model within the neuronal circuit model of the spinal dorsal horn. To experimentally validate this hypothesized mechanism, we used pharmacological modulators of SK channels and demonstrated that (1) tonic firing PVINs from naive male mice become adaptive when exposed to an SK channel activator, and (2) adapting PVINs from nerve-injured male mice return to tonic firing on SK channel blockade. Our work provides important insights into the cellular mechanism underlying the decreased output of PVINs in the spinal dorsal horn after nerve injury and highlights potential pharmacological targets for new and effective treatment approaches to neuropathic pain.SIGNIFICANCE STATEMENT Parvalbumin-expressing interneurons (PVINs) exert crucial inhibitory control over Aβ fiber-mediated nociceptive pathways at the spinal dorsal horn. The loss of their inhibitory tone leads to neuropathic symptoms, such as mechanical allodynia, via mechanisms that are not fully understood. This study identifies the reduced intrinsic excitability of PVINs as a potential cause for their decreased inhibitory output in nerve-injured condition. Combining computational and experimental approaches, we predict a calcium-dependent mechanism that modulates PVINs' electrical activity following nerve injury: a depletion of cytosolic calcium buffer allows for the rapid accumulation of intracellular calcium through the active membranes, which in turn potentiates SK channels and impedes spike generation. Our results therefore pinpoint SK channels as potential therapeutic targets for treating neuropathic symptoms.
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Affiliation(s)
- Xinyue Ma
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Loïs S Miraucourt
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Haoyi Qiu
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Reza Sharif-Naeini
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Anmar Khadra
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
- Department of Quantitative Life Sciences, McGill University, Montreal, Quebec H3G 1Y6, Canada
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3
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Berecki G, Bryson A, Polster T, Petrou S. Biophysical characterization and modelling of SCN1A gain-of-function predicts interneuron hyperexcitability and a predisposition to network instability through homeostatic plasticity. Neurobiol Dis 2023; 179:106059. [PMID: 36868483 DOI: 10.1016/j.nbd.2023.106059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/11/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
SCN1A gain-of-function variants are associated with early onset developmental and epileptic encephalopathies (DEEs) that possess distinct clinical features compared to Dravet syndrome caused by SCN1A loss-of-function. However, it is unclear how SCN1A gain-of-function may predispose to cortical hyper-excitability and seizures. Here, we first report the clinical features of a patient carrying a de novo SCN1A variant (T162I) associated with neonatal-onset DEE, and then characterize the biophysical properties of T162I and three other SCN1A variants associated with neonatal-onset DEE (I236V) and early infantile DEE (P1345S, R1636Q). In voltage clamp experiments, three variants (T162I, P1345S and R1636Q) exhibited changes in activation and inactivation properties that enhanced window current, consistent with gain-of-function. Dynamic action potential clamp experiments utilising model neurons incorporating Nav1.1. channels supported a gain-of-function mechanism for all four variants. Here, the T162I, I236V, P1345S, and R1636Q variants exhibited higher peak firing rates relative to wild type and the T162I and R1636Q variants produced a hyperpolarized threshold and reduced neuronal rheobase. To explore the impact of these variants upon cortical excitability, we used a spiking network model containing an excitatory pyramidal cell (PC) and parvalbumin positive (PV) interneuron population. SCN1A gain-of-function was modelled by enhancing the excitability of PV interneurons and then incorporating three simple forms of homeostatic plasticity that restored pyramidal cell firing rates. We found that homeostatic plasticity mechanisms exerted differential impact upon network function, with changes to PV-to-PC and PC-to-PC synaptic strength predisposing to network instability. Overall, our findings support a role for SCN1A gain-of-function and inhibitory interneuron hyperexcitability in early onset DEE. We propose a mechanism through which homeostatic plasticity pathways can predispose to pathological excitatory activity and contribute to phenotypic variability in SCN1A disorders.
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Affiliation(s)
- Géza Berecki
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3052, Australia.
| | - Alexander Bryson
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3052, Australia; Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia
| | - Tilman Polster
- Krankenhaus Mara, Bethel Epilepsy Centre, Department of Epileptology, Medical School, Bielefeld University, Campus Bielefeld-Bethel, Bielefeld, Germany
| | - Steven Petrou
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3052, Australia; Praxis Precision Medicines, Inc., Cambridge, MA 02142, USA; Department of the Florey Institute, University of Melbourne, Parkville, VIC 3050, Australia.
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4
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Naudin L. Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate. J Comput Neurosci 2023; 51:201-206. [PMID: 36905484 DOI: 10.1007/s10827-023-00848-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023]
Affiliation(s)
- Loïs Naudin
- Laboratoire Lorrain de Recherche en Informatique et ses Applications, CNRS, Université de Lorraine, Nancy, France. .,Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, F-75012, France.
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5
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Oláh VJ, Pedersen NP, Rowan MJM. Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons. eLife 2022; 11:e79535. [PMID: 36341568 PMCID: PMC9640191 DOI: 10.7554/elife.79535] [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: 04/16/2022] [Accepted: 10/23/2022] [Indexed: 11/09/2022] Open
Abstract
Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
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Affiliation(s)
- Viktor J Oláh
- Department of Cell Biology, Emory University School of MedicineAtlantaUnited States
| | - Nigel P Pedersen
- Department of Neurology, Emory University School of MedicineAtlantaUnited States
| | - Matthew JM Rowan
- Department of Cell Biology, Emory University School of MedicineAtlantaUnited States
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6
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Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA. Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep 2022; 40:111176. [PMID: 35947954 PMCID: PMC9793758 DOI: 10.1016/j.celrep.2022.111176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 01/28/2022] [Accepted: 07/18/2022] [Indexed: 12/30/2022] Open
Abstract
Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.
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Affiliation(s)
- Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA,These authors contributed equally
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland,These authors contributed equally
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Soo Yeun Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yina Wei
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Zhejiang Lab, Hangzhou City, Zhejiang Province 311121, China
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Costas A. Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Lead contact,Correspondence:
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7
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Ladd A, Kim KG, Balewski J, Bouchard K, Ben-Shalom R. Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. Front Neuroinform 2022; 16:882552. [PMID: 35784184 PMCID: PMC9248031 DOI: 10.3389/fninf.2022.882552] [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: 02/23/2022] [Accepted: 05/18/2022] [Indexed: 11/28/2022] Open
Abstract
Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.
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Affiliation(s)
- Alexander Ladd
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- *Correspondence: Alexander Ladd
| | - Kyung Geun Kim
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Jan Balewski
- NERSC, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Kristofer Bouchard
- Helen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States
- Scientific Data Division and Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Roy Ben-Shalom
- Neurology Department, MIND Institute, University of California, Davis, Sacramento, CA, United States
- Roy Ben-Shalom
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8
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Naudin L, Jiménez Laredo JL, Liu Q, Corson N. Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons. PLoS One 2022; 17:e0268380. [PMID: 35560186 PMCID: PMC9106219 DOI: 10.1371/journal.pone.0268380] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Unlike spiking neurons which compress continuous inputs into digital signals for transmitting information via action potentials, non-spiking neurons modulate analog signals through graded potential responses. Such neurons have been found in a large variety of nervous tissues in both vertebrate and invertebrate species, and have been proven to play a central role in neuronal information processing. If general and vast efforts have been made for many years to model spiking neurons using conductance-based models (CBMs), very few methods have been developed for non-spiking neurons. When a CBM is built to characterize the neuron behavior, it should be endowed with generalization capabilities (i.e. the ability to predict acceptable neuronal responses to different novel stimuli not used during the model’s building). Yet, since CBMs contain a large number of parameters, they may typically suffer from a lack of such a capability. In this paper, we propose a new systematic approach based on multi-objective optimization which builds general non-spiking models with generalization capabilities. The proposed approach only requires macroscopic experimental data from which all the model parameters are simultaneously determined without compromise. Such an approach is applied on three non-spiking neurons of the nematode Caenorhabditis elegans (C. elegans), a well-known model organism in neuroscience that predominantly transmits information through non-spiking signals. These three neurons, arbitrarily labeled by convention as RIM, AIY and AFD, represent, to date, the three possible forms of non-spiking neuronal responses of C. elegans.
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Affiliation(s)
- Loïs Naudin
- Department of Applied Mathematics, Normandie University, Le Havre, Normandie, France
- * E-mail:
| | | | - Qiang Liu
- Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong, SAR, China
| | - Nathalie Corson
- Department of Applied Mathematics, Normandie University, Le Havre, Normandie, France
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9
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Schürmann F, Courcol JD, Ramaswamy S. Computational Concepts for Reconstructing and Simulating Brain Tissue. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:237-259. [PMID: 35471542 DOI: 10.1007/978-3-030-89439-9_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Affiliation(s)
- Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
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10
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Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs. Biophys J 2022; 121:644-657. [PMID: 34999132 PMCID: PMC8873947 DOI: 10.1016/j.bpj.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/21/2021] [Accepted: 01/05/2022] [Indexed: 11/24/2022] Open
Abstract
In this work, we highlight an electrophysiological feature often observed in recordings from mouse CA1 pyramidal cells that has so far been ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e., the minimum value of the membrane potential between successive action potentials during a sustained input) in response to strong somatic current injections. Such an increase can directly affect neurotransmitter release properties and, more generally, the efficacy of synaptic transmission. However, it cannot be explained by any currently available conductance-based computational model. Here we present a model addressing this issue, demonstrating that experimental recordings can be reproduced by assuming that an input current modifies, in a time-dependent manner, the electrical and permeability properties of the neuron membrane by shifting the ionic reversal potentials and channel kinetics. For this reason, we propose that any detailed model of ion channel kinetics for neurons exhibiting this characteristic should be adapted to correctly represent the response and the synaptic integration process during strong and sustained inputs.
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11
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Bryson A, Berkovic SF, Petrou S, Grayden DB. State transitions through inhibitory interneurons in a cortical network model. PLoS Comput Biol 2021; 17:e1009521. [PMID: 34653178 PMCID: PMC8550371 DOI: 10.1371/journal.pcbi.1009521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/27/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022] Open
Abstract
Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state. Inhibitory interneurons comprise a significant proportion of all cortical neurons and play a crucial role in sustaining normal neural activity in the brain. Although it is well established that there exist distinct subtypes of interneurons, the impact of different interneuron subtypes upon cortical function remains unclear. In this work, we explore the role of interneuron subtypes for modulating neural activity using a network model containing two of the most common interneuron subtypes. We find that one interneuron subtype, known as fast spiking interneurons, preferentially control the strength of activity between excitatory neurons to regulate changes in network state. These findings suggest that interneuron subtypes may selectively modulate cortical activity to promote different computational capabilities.
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Affiliation(s)
- Alexander Bryson
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
- * E-mail: (AB); (DBG)
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Australia
| | - Steven Petrou
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia
- * E-mail: (AB); (DBG)
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12
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Poirazi P, Papoutsi A. Illuminating dendritic function with computational models. Nat Rev Neurosci 2020; 21:303-321. [PMID: 32393820 DOI: 10.1038/s41583-020-0301-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2020] [Indexed: 02/06/2023]
Abstract
Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.
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Affiliation(s)
- Panayiota Poirazi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
| | - Athanasia Papoutsi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece
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13
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Masoli S, Tognolina M, Laforenza U, Moccia F, D'Angelo E. Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage. Commun Biol 2020; 3:222. [PMID: 32385389 PMCID: PMC7210112 DOI: 10.1038/s42003-020-0953-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 04/13/2020] [Indexed: 02/06/2023] Open
Abstract
The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Marialuisa Tognolina
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Umberto Laforenza
- Department of Molecular Medicine, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Francesco Moccia
- Department of Biology and Biotechnology, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy. .,Brain Connectivity Center, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy.
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14
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Ujfalussy BB, Makara JK, Lengyel M, Branco T. Global and Multiplexed Dendritic Computations under In Vivo-like Conditions. Neuron 2019; 100:579-592.e5. [PMID: 30408443 PMCID: PMC6226578 DOI: 10.1016/j.neuron.2018.08.032] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 07/07/2018] [Accepted: 08/21/2018] [Indexed: 10/27/2022]
Abstract
Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.
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Affiliation(s)
- Balázs B Ujfalussy
- MRC Laboratory of Molecular Biology, Cambridge, UK; Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; MTA Wigner Research Center for Physics, Budapest, Hungary.
| | - Judit K Makara
- Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Department of Cognitive Science, Central European University, Budapest, Hungary
| | - Tiago Branco
- MRC Laboratory of Molecular Biology, Cambridge, UK; Sainsbury Wellcome Centre, University College London, London, UK
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15
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Fan X, Markram H. A Brief History of Simulation Neuroscience. Front Neuroinform 2019; 13:32. [PMID: 31133838 PMCID: PMC6513977 DOI: 10.3389/fninf.2019.00032] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 04/12/2019] [Indexed: 12/19/2022] Open
Abstract
Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.
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Affiliation(s)
- Xue Fan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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16
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Berecki G, Bryson A, Terhag J, Maljevic S, Gazina EV, Hill SL, Petrou S. SCN1A
gain of function in early infantile encephalopathy. Ann Neurol 2019; 85:514-525. [DOI: 10.1002/ana.25438] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 02/14/2019] [Accepted: 02/15/2019] [Indexed: 02/01/2023]
Affiliation(s)
- Géza Berecki
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Alexander Bryson
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Jan Terhag
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Snezana Maljevic
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Elena V. Gazina
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Sean L. Hill
- Blue Brain ProjectSwiss Federal Institute of Technology in Lausanne Geneva Switzerland
| | - Steven Petrou
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
- Department of MedicineRoyal Melbourne Hospital, University of Melbourne Parkville Victoria Australia
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17
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Gouwens NW, Berg J, Feng D, Sorensen SA, Zeng H, Hawrylycz MJ, Koch C, Arkhipov A. Systematic generation of biophysically detailed models for diverse cortical neuron types. Nat Commun 2018; 9:710. [PMID: 29459718 PMCID: PMC5818534 DOI: 10.1038/s41467-017-02718-3] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 12/20/2017] [Indexed: 01/17/2023] Open
Abstract
The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.
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Affiliation(s)
- Nathan W Gouwens
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - David Feng
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Staci A Sorensen
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Michael J Hawrylycz
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Anton Arkhipov
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA.
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18
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A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. J Neurosci Methods 2017; 293:264-283. [PMID: 28993204 DOI: 10.1016/j.jneumeth.2017.10.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 09/07/2017] [Accepted: 10/05/2017] [Indexed: 01/15/2023]
Abstract
BACKGROUND Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. NEW METHOD In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. RESULT We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. COMPARISON WITH EXISTING METHODS Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. CONCLUSIONS The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.
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19
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Masoli S, Rizza MF, Sgritta M, Van Geit W, Schürmann F, D'Angelo E. Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells. Front Cell Neurosci 2017; 11:71. [PMID: 28360841 PMCID: PMC5350144 DOI: 10.3389/fncel.2017.00071] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 02/27/2017] [Indexed: 01/30/2023] Open
Abstract
In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Martina F Rizza
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-BicoccaMilan, Italy
| | - Martina Sgritta
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Memory and Brain Research Center, Department of Neuroscience, Baylor College of MedicineHouston, TX, USA
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
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20
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Podlaski WF, Seeholzer A, Groschner LN, Miesenböck G, Ranjan R, Vogels TP. Mapping the function of neuronal ion channels in model and experiment. eLife 2017; 6. [PMID: 28267430 PMCID: PMC5340531 DOI: 10.7554/elife.22152] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 01/28/2017] [Indexed: 11/13/2022] Open
Abstract
Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. However, the number of published models, and the lack of standardization, make the comparison of ion channel models with one another and with experimental data difficult. Here, we present a framework for the automated large-scale classification of ion channel models. Using annotated metadata and responses to a set of voltage-clamp protocols, we assigned 2378 models of voltage- and calcium-gated ion channels coded in NEURON to 211 clusters. The IonChannelGenealogy (ICGenealogy) web interface provides an interactive resource for the categorization of new and existing models and experimental recordings. It enables quantitative comparisons of simulated and/or measured ion channel kinetics, and facilitates field-wide standardization of experimentally-constrained modeling.
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Affiliation(s)
- William F Podlaski
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom.,Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Alexander Seeholzer
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,School of Life Sciences, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.,Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Lukas N Groschner
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom.,Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Gero Miesenböck
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom.,Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Rajnish Ranjan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Tim P Vogels
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom.,Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
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21
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Using a Semi-Automated Strategy to Develop Multi-Compartment Models That Predict Biophysical Properties of Interneuron-Specific 3 (IS3) Cells in Hippocampus. eNeuro 2016; 3:eN-NWR-0087-16. [PMID: 27679813 PMCID: PMC5035096 DOI: 10.1523/eneuro.0087-16.2016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 08/19/2016] [Accepted: 08/22/2016] [Indexed: 11/30/2022] Open
Abstract
Determining how intrinsic cellular properties govern and modulate neuronal input–output processing is a critical endeavor for understanding microcircuit functions in the brain. However, lack of cellular specifics and nonlinear interactions prevent experiments alone from achieving this. Building and using cellular models is essential in these efforts. We focus on uncovering the intrinsic properties of mus musculus hippocampal type 3 interneuron-specific (IS3) cells, a cell type that makes GABAergic synapses onto specific interneuron types, but not pyramidal cells. While IS3 cell morphology and synaptic output have been examined, their voltage-gated ion channel profile and distribution remain unknown. We combined whole-cell patch-clamp recordings and two-photon dendritic calcium imaging to examine IS3 cell membrane and dendritic properties. Using these data as a target reference, we developed a semi-automated strategy to obtain multi-compartment models for a cell type with unknown intrinsic properties. Our approach is based on generating populations of models to capture determined features of the experimental data, each of which possesses unique combinations of channel types and conductance values. From these populations, we chose models that most closely resembled the experimental data. We used these models to examine the impact of specific ion channel combinations on spike generation. Our models predict that fast delayed rectifier currents should be present in soma and proximal dendrites, and this is confirmed using immunohistochemistry. Further, without A-type potassium currents in the dendrites, spike generation is facilitated at more distal synaptic input locations. Our models will help to determine the functional role of IS3 cells in hippocampal microcircuits.
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22
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Almog M, Korngreen A. Is realistic neuronal modeling realistic? J Neurophysiol 2016; 116:2180-2209. [PMID: 27535372 DOI: 10.1152/jn.00360.2016] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/17/2016] [Indexed: 11/22/2022] Open
Abstract
Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.
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Affiliation(s)
- Mara Almog
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and.,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Alon Korngreen
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and .,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
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23
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Rumbell TH, Draguljić D, Yadav A, Hof PR, Luebke JI, Weaver CM. Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons. J Comput Neurosci 2016; 41:65-90. [PMID: 27106692 DOI: 10.1007/s10827-016-0605-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 03/09/2016] [Accepted: 04/05/2016] [Indexed: 02/03/2023]
Abstract
Conductance-based compartment modeling requires tuning of many parameters to fit the neuron model to target electrophysiological data. Automated parameter optimization via evolutionary algorithms (EAs) is a common approach to accomplish this task, using error functions to quantify differences between model and target. We present a three-stage EA optimization protocol for tuning ion channel conductances and kinetics in a generic neuron model with minimal manual intervention. We use the technique of Latin hypercube sampling in a new way, to choose weights for error functions automatically so that each function influences the parameter search to a similar degree. This protocol requires no specialized physiological data collection and is applicable to commonly-collected current clamp data and either single- or multi-objective optimization. We applied the protocol to two representative pyramidal neurons from layer 3 of the prefrontal cortex of rhesus monkeys, in which action potential firing rates are significantly higher in aged compared to young animals. Using an idealized dendritic topology and models with either 4 or 8 ion channels (10 or 23 free parameters respectively), we produced populations of parameter combinations fitting the target datasets in less than 80 hours of optimization each. Passive parameter differences between young and aged models were consistent with our prior results using simpler models and hand tuning. We analyzed parameter values among fits to a single neuron to facilitate refinement of the underlying model, and across fits to multiple neurons to show how our protocol will lead to predictions of parameter differences with aging in these neurons.
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Affiliation(s)
- Timothy H Rumbell
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Computational Biology Center, IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Danel Draguljić
- Department of Mathematics, Franklin and Marshall College, Lancaster, PA, 17604, USA
| | - Aniruddha Yadav
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Gauge Data Solutions Pvt Ltd, Noida, India
| | - Patrick R Hof
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jennifer I Luebke
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Christina M Weaver
- Department of Mathematics, Franklin and Marshall College, Lancaster, PA, 17604, USA.
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24
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Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol JD, Muller EB, Schürmann F, Segev I, Markram H. BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience. Front Neuroinform 2016; 10:17. [PMID: 27375471 PMCID: PMC4896051 DOI: 10.3389/fninf.2016.00017] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/06/2016] [Indexed: 11/13/2022] Open
Abstract
At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases.
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Affiliation(s)
- Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Michael Gevaert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of JerusalemJerusalem, Israel; The Edmond and Lily Safra Centre for Brain Sciences, The Hebrew University of JerusalemJerusalem, Israel
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
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25
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Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Ailamaki A, Alonso-Nanclares L, Antille N, Arsever S, Kahou GAA, Berger TK, Bilgili A, Buncic N, Chalimourda A, Chindemi G, Courcol JD, Delalondre F, Delattre V, Druckmann S, Dumusc R, Dynes J, Eilemann S, Gal E, Gevaert ME, Ghobril JP, Gidon A, Graham JW, Gupta A, Haenel V, Hay E, Heinis T, Hernando JB, Hines M, Kanari L, Keller D, Kenyon J, Khazen G, Kim Y, King JG, Kisvarday Z, Kumbhar P, Lasserre S, Le Bé JV, Magalhães BRC, Merchán-Pérez A, Meystre J, Morrice BR, Muller J, Muñoz-Céspedes A, Muralidhar S, Muthurasa K, Nachbaur D, Newton TH, Nolte M, Ovcharenko A, Palacios J, Pastor L, Perin R, Ranjan R, Riachi I, Rodríguez JR, Riquelme JL, Rössert C, Sfyrakis K, Shi Y, Shillcock JC, Silberberg G, Silva R, Tauheed F, Telefont M, Toledo-Rodriguez M, Tränkler T, Van Geit W, Díaz JV, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Segev I, Schürmann F. Reconstruction and Simulation of Neocortical Microcircuitry. Cell 2015; 163:456-92. [PMID: 26451489 DOI: 10.1016/j.cell.2015.09.029] [Citation(s) in RCA: 747] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 05/04/2015] [Accepted: 09/11/2015] [Indexed: 02/03/2023]
Affiliation(s)
- Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland.
| | - Eilif Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Carlos Aguado Sanchez
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anastasia Ailamaki
- Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Lidia Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Nicolas Antille
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Selim Arsever
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Guy Antoine Atenekeng Kahou
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Thomas K Berger
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Ahmet Bilgili
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Nenad Buncic
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Athanassia Chalimourda
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Fabien Delalondre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Vincent Delattre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Shaul Druckmann
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Raphael Dumusc
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James Dynes
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Stefan Eilemann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Eyal Gal
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Michael Emiel Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Pierre Ghobril
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Albert Gidon
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Joe W Graham
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anirudh Gupta
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Valentin Haenel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Etay Hay
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Thomas Heinis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland; Imperial College London, London SW7 2AZ, UK
| | - Juan B Hernando
- CeSViMa, Centro de Supercomputación y Visualización de Madrid, Universidad Politécnica de Madrid, 28223 Madrid, Spain
| | - Michael Hines
- Department of Neurobiology, Yale University, New Haven, CT 06510 USA
| | - Lida Kanari
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - John Kenyon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Georges Khazen
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yihwa Kim
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James G King
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Zoltan Kisvarday
- MTA-Debreceni Egyetem, Neuroscience Research Group, 4032 Debrecen, Hungary
| | - Pramod Kumbhar
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Sébastien Lasserre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratoire d'informatique et de visualisation, EPFL, 1015 Lausanne, Switzerland
| | - Jean-Vincent Le Bé
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Bruno R C Magalhães
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Angel Merchán-Pérez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Julie Meystre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Benjamin Roy Morrice
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jeffrey Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Alberto Muñoz-Céspedes
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Shruti Muralidhar
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Keerthan Muthurasa
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Nachbaur
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Taylor H Newton
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Max Nolte
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Aleksandr Ovcharenko
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Juan Palacios
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Luis Pastor
- Modeling and Virtual Reality Group, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Rajnish Ranjan
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Imad Riachi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - José-Rodrigo Rodríguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Juan Luis Riquelme
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Konstantinos Sfyrakis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Julian C Shillcock
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Gilad Silberberg
- Department of Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ricardo Silva
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Farhan Tauheed
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Martin Telefont
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | | | - Thomas Tränkler
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jafet Villafranca Díaz
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Richard Walker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yun Wang
- Key Laboratory of Visual Science and National Ministry of Health, School of Optometry and Opthalmology, Wenzhou Medical College, Wenzhou 325003, China; Caritas St. Elizabeth's Medical Center, Genesys Research Institute, Tufts University, Boston, MA 02111, USA
| | - Stefano M Zaninetta
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Sean L Hill
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
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Anastassiou CA, Perin R, Buzsáki G, Markram H, Koch C. Cell type- and activity-dependent extracellular correlates of intracellular spiking. J Neurophysiol 2015; 114:608-23. [PMID: 25995352 DOI: 10.1152/jn.00628.2014] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 05/15/2015] [Indexed: 12/19/2022] Open
Abstract
Despite decades of extracellular action potential (EAP) recordings monitoring brain activity, the biophysical origin and inherent variability of these signals remain enigmatic. We performed whole cell patch recordings of excitatory and inhibitory neurons in rat somatosensory cortex slice while positioning a silicon probe in their vicinity to concurrently record intra- and extracellular voltages for spike frequencies under 20 Hz. We characterize biophysical events and properties (intracellular spiking, extracellular resistivity, temporal jitter, etc.) related to EAP recordings at the single-neuron level in a layer-specific manner. Notably, EAP amplitude was found to decay as the inverse of distance between the soma and the recording electrode with similar (but not identical) resistivity across layers. Furthermore, we assessed a number of EAP features and their variability with spike activity: amplitude (but not temporal) features varied substantially (∼ 30-50% compared with mean) and nonmonotonically as a function of spike frequency and spike order. Such EAP variation only partly reflects intracellular somatic spike variability and points to the plethora of processes contributing to the EAP. Also, we show that the shape of the EAP waveform is qualitatively similar to the negative of the temporal derivative to the intracellular somatic voltage, as expected from theory. Finally, we tested to what extent EAPs can impact the lowpass-filtered part of extracellular recordings, the local field potential (LFP), typically associated with synaptic activity. We found that spiking of excitatory neurons can significantly impact the LFP at frequencies as low as 20 Hz. Our results question the common assertion that the LFP acts as proxy for synaptic activity.
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Affiliation(s)
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland; and
| | - György Buzsáki
- New York University Medical Center Langone, New York University, New York, New York
| | - Henry Markram
- Laboratory of Neural Microcircuitry, Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland; and
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington
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27
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Hoffmann JHO, Meyer HS, Schmitt AC, Straehle J, Weitbrecht T, Sakmann B, Helmstaedter M. Synaptic Conductance Estimates of the Connection Between Local Inhibitor Interneurons and Pyramidal Neurons in Layer 2/3 of a Cortical Column. Cereb Cortex 2015; 25:4415-29. [PMID: 25761638 PMCID: PMC4816789 DOI: 10.1093/cercor/bhv039] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Stimulation of a principal whisker yields sparse action potential (AP) spiking in layer 2/3 (L2/3) pyramidal neurons in a cortical column of rat barrel cortex. The low AP rates in pyramidal neurons could be explained by activation of interneurons in L2/3 providing inhibition onto L2/3 pyramidal neurons. L2/3 interneurons classified as local inhibitors based on their axonal projection in the same column were reported to receive strong excitatory input from spiny neurons in L4, which are also the main source of the excitatory input to L2/3 pyramidal neurons. Here, we investigated the remaining synaptic connection in this intracolumnar microcircuit. We found strong and reliable inhibitory synaptic transmission between intracolumnar L2/3 local-inhibitor-to-L2/3 pyramidal neuron pairs [inhibitory postsynaptic potential (IPSP) amplitude −0.88 ± 0.67 mV]. On average, 6.2 ± 2 synaptic contacts were made by L2/3 local inhibitors onto L2/3 pyramidal neurons at 107 ± 64 µm path distance from the pyramidal neuron soma, thus overlapping with the distribution of synaptic contacts from L4 spiny neurons onto L2/3 pyramidal neurons (67 ± 34 µm). Finally, using compartmental simulations, we determined the synaptic conductance per synaptic contact to be 0.77 ± 0.4 nS. We conclude that the synaptic circuit from L4 to L2/3 can provide efficient shunting inhibition that is temporally and spatially aligned with the excitatory input from L4 to L2/3.
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Affiliation(s)
- Jochen H O Hoffmann
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany Current address: Department of Dermatology, University of Heidelberg, D-69120 Heidelberg, Germany
| | - H S Meyer
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany Current address: Department of Neurosurgery, Technical University of Munich, 81675 Munich, Germany
| | - Arno C Schmitt
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany
| | - Jakob Straehle
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany
| | - Trinh Weitbrecht
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany
| | - Bert Sakmann
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany Current address: Cortical Column in Silico Group, Max Planck Institute of Neurobiology, D-82152 Martinsried, Germany
| | - Moritz Helmstaedter
- Department of Cell Physiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany Present address: Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
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28
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Masoli S, Solinas S, D'Angelo E. Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization. Front Cell Neurosci 2015; 9:47. [PMID: 25759640 PMCID: PMC4338753 DOI: 10.3389/fncel.2015.00047] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 01/30/2015] [Indexed: 12/02/2022] Open
Abstract
The Purkinje cell (PC) is among the most complex neurons in the brain and plays a critical role for cerebellar functioning. PCs operate as fast pacemakers modulated by synaptic inputs but can switch from simple spikes to complex bursts and, in some conditions, show bistability. In contrast to original works emphasizing dendritic Ca-dependent mechanisms, recent experiments have supported a primary role for axonal Na-dependent processing, which could effectively regulate spike generation and transmission to deep cerebellar nuclei (DCN). In order to account for the numerous ionic mechanisms involved (at present including Nav1.6, Cav2.1, Cav3.1, Cav3.2, Cav3.3, Kv1.1, Kv1.5, Kv3.3, Kv3.4, Kv4.3, KCa1.1, KCa2.2, KCa3.1, Kir2.x, HCN1), we have elaborated a multicompartmental model incorporating available knowledge on localization and gating of PC ionic channels. The axon, including initial segment (AIS) and Ranvier nodes (RNs), proved critical to obtain appropriate pacemaking and firing frequency modulation. Simple spikes initiated in the AIS and protracted discharges were stabilized in the soma through Na-dependent mechanisms, while somato-dendritic Ca channels contributed to sustain pacemaking and to generate complex bursting at high discharge regimes. Bistability occurred only following Na and Ca channel down-regulation. In addition, specific properties in RNs K currents were required to limit spike transmission frequency along the axon. The model showed how organized electroresponsive functions could emerge from the molecular complexity of PCs and showed that the axon is fundamental to complement ionic channel compartmentalization enabling action potential processing and transmission of specific spike patterns to DCN.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Science, University of Pavia Pavia, Italy
| | - Sergio Solinas
- Brain Connectivity Center, Istituto Neurologico IRCCS C. Mondino Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Science, University of Pavia Pavia, Italy ; Brain Connectivity Center, Istituto Neurologico IRCCS C. Mondino Pavia, Italy
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29
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Sekulić V, Lawrence JJ, Skinner FK. Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells. PLoS One 2014; 9:e106567. [PMID: 25360752 PMCID: PMC4215854 DOI: 10.1371/journal.pone.0106567] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 07/30/2014] [Indexed: 01/15/2023] Open
Abstract
Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.
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Affiliation(s)
- Vladislav Sekulić
- Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - J. Josh Lawrence
- NIH COBRE Center for Structural and Functional Neuroscience, University of Montana, Missoula, Montana, United States of America
- Department of Biomedical and Pharmaceutical Sciences, University of Montana, Missoula, Montana, United States of America
| | - Frances K. Skinner
- Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada
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30
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Subramaniyam S, Solinas S, Perin P, Locatelli F, Masetto S, D'Angelo E. Computational modeling predicts the ionic mechanism of late-onset responses in unipolar brush cells. Front Cell Neurosci 2014; 8:237. [PMID: 25191224 PMCID: PMC4138490 DOI: 10.3389/fncel.2014.00237] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Accepted: 07/27/2014] [Indexed: 11/29/2022] Open
Abstract
Unipolar Brush Cells (UBCs) have been suggested to play a critical role in cerebellar functioning, yet the corresponding cellular mechanisms remain poorly understood. UBCs have recently been reported to generate, in addition to early-onset glutamate receptor-dependent synaptic responses, a late-onset response (LOR) composed of a slow depolarizing ramp followed by a spike burst (Locatelli et al., 2013). The LOR activates as a consequence of synaptic activity and involves an intracellular cascade modulating H- and TRP-current gating. In order to assess the LOR mechanisms, we have developed a UBC multi-compartmental model (including soma, dendrite, initial segment, and axon) incorporating biologically realistic representations of ionic currents and a cytoplasmic coupling mechanism regulating TRP and H channel gating. The model finely reproduced UBC responses to current injection, including a burst triggered by a low-threshold spike (LTS) sustained by CaLVA currents, a persistent discharge sustained by CaHVA currents, and a rebound burst following hyperpolarization sustained by H- and CaLVA-currents. Moreover, the model predicted that H- and TRP-current regulation was necessary and sufficient to generate the LOR and its dependence on the intensity and duration of mossy fiber activity. Therefore, the model showed that, using a basic set of ionic channels, UBCs generate a rich repertoire of bursts, which could effectively implement tunable delay-lines in the local microcircuit.
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Affiliation(s)
- Sathyaa Subramaniyam
- Neurophysiology Unit, Department of Brain and Behavioral Science, University of Pavia Pavia, Italy ; Consorzio Interuniversitario per le Scienze Fisiche della Materia (CNISM) Pavia, Italy
| | - Sergio Solinas
- Neurophysiology Unit, Brain Connectivity Center, Istituto Neurologico IRCCS C. Mondino Pavia, Italy
| | - Paola Perin
- Neurophysiology Unit, Department of Brain and Behavioral Science, University of Pavia Pavia, Italy
| | - Francesca Locatelli
- Neurophysiology Unit, Department of Brain and Behavioral Science, University of Pavia Pavia, Italy
| | - Sergio Masetto
- Neurophysiology Unit, Department of Brain and Behavioral Science, University of Pavia Pavia, Italy
| | - Egidio D'Angelo
- Neurophysiology Unit, Department of Brain and Behavioral Science, University of Pavia Pavia, Italy ; Neurophysiology Unit, Brain Connectivity Center, Istituto Neurologico IRCCS C. Mondino Pavia, Italy
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31
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Soudry D, Meir R. The neuronal response at extended timescales: a linearized spiking input-output relation. Front Comput Neurosci 2014; 8:29. [PMID: 24765073 PMCID: PMC3980113 DOI: 10.3389/fncom.2014.00029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 02/24/2014] [Indexed: 11/16/2022] Open
Abstract
Many biological systems are modulated by unknown slow processes. This can severely hinder analysis – especially in excitable neurons, which are highly non-linear and stochastic systems. We show the analysis simplifies considerably if the input matches the sparse “spiky” nature of the output. In this case, a linearized spiking Input–Output (I/O) relation can be derived semi-analytically, relating input spike trains to output spikes based on known biophysical properties. Using this I/O relation we obtain closed-form expressions for all second order statistics (input – internal state – output correlations and spectra), construct optimal linear estimators for the neuronal response and internal state and perform parameter identification. These results are guaranteed to hold, for a general stochastic biophysical neuron model, with only a few assumptions (mainly, timescale separation). We numerically test the resulting expressions for various models, and show that they hold well, even in cases where our assumptions fail to hold. In a companion paper we demonstrate how this approach enables us to fit a biophysical neuron model so it reproduces experimentally observed temporal firing statistics on days-long experiments.
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Affiliation(s)
- Daniel Soudry
- Laboratory for Network Biology Research, Department of Electrical Engineering Technion, Haifa, Israel
| | - Ron Meir
- Laboratory for Network Biology Research, Department of Electrical Engineering Technion, Haifa, Israel
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32
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A quantitative description of dendritic conductances and its application to dendritic excitation in layer 5 pyramidal neurons. J Neurosci 2014; 34:182-96. [PMID: 24381280 DOI: 10.1523/jneurosci.2896-13.2014] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Postsynaptic integration is a complex function of passive membrane properties and nonlinear activation of voltage-gated channels. Some cortical neurons express many voltage-gated channels, with each displaying heterogeneous dendritic conductance gradients. This complexity has hindered the construction of experimentally based mechanistic models of cortical neurons. Here we show that it is possible to overcome this obstacle. We recorded the membrane potential from the soma and apical dendrite of layer 5 (L5) pyramidal neurons of the rat somatosensory cortex. A combined experimental and numerical parameter peeling procedure was implemented to optimize a detailed ionic mechanism for the generation and propagation of dendritic spikes in neocortical L5 pyramidal neurons. In the optimized model, the density of voltage-gated Ca(2+) channels decreased linearly from the soma, and leveled at the distal apical dendrite. The density of the small-conductance Ca(2+)-activated channel decreased along the apical dendrite, whereas the density of the large-conductance Ca(2+)-gated K(+) channel was uniform throughout the apical dendrite. The model predicted an ionic mechanism for the generation of a dendritic spike, the interaction of this spike with the backpropagating action potential, the mechanism responsible for the ability of the proximal apical dendrite to control the coupling between the axon and the dendrite, and the generation of NMDA spikes in the distal apical tuft. Moreover, in addition to faithfully predicting many experimental results recorded from the apical dendrite of L5 pyramidal neurons, the model validates a new methodology for mechanistic modeling of neurons in the CNS.
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Marasco A, Limongiello A, Migliore M. Fast and accurate low-dimensional reduction of biophysically detailed neuron models. Sci Rep 2012; 2:928. [PMID: 23226594 PMCID: PMC3514644 DOI: 10.1038/srep00928] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2012] [Accepted: 11/12/2012] [Indexed: 11/18/2022] Open
Abstract
Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the methods used so far to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to > 40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.
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Affiliation(s)
- Addolorata Marasco
- Department of Mathematics and Applications R. Caccioppoli, University of Naples Federico II, Via Cintia, 80126 Naples, Italy
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Druckmann S, Hill S, Schürmann F, Markram H, Segev I. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. ACTA ACUST UNITED AC 2012; 23:2994-3006. [PMID: 22989582 DOI: 10.1093/cercor/bhs290] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.
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Affiliation(s)
- Shaul Druckmann
- Interdisciplinary Center for Neural Computation, and Department of Neurobiology, Edmond and Lily Safra Center for Brain Sciences, Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel and
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Hertäg L, Hass J, Golovko T, Durstewitz D. An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data. Front Comput Neurosci 2012; 6:62. [PMID: 22973220 PMCID: PMC3434419 DOI: 10.3389/fncom.2012.00062] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2012] [Accepted: 08/03/2012] [Indexed: 11/13/2022] Open
Abstract
For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.
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Affiliation(s)
- Loreen Hertäg
- Bernstein-Center for Computational Neuroscience, Central Institute of Mental Health, Psychiatry, Medical Faculty Mannheim of Heidelberg UniversityMannheim, Germany
| | - Joachim Hass
- Bernstein-Center for Computational Neuroscience, Central Institute of Mental Health, Psychiatry, Medical Faculty Mannheim of Heidelberg UniversityMannheim, Germany
| | - Tatiana Golovko
- Bernstein-Center for Computational Neuroscience, Central Institute of Mental Health, Psychiatry, Medical Faculty Mannheim of Heidelberg UniversityMannheim, Germany
| | - Daniel Durstewitz
- Bernstein-Center for Computational Neuroscience, Central Institute of Mental Health, Psychiatry, Medical Faculty Mannheim of Heidelberg UniversityMannheim, Germany
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36
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Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. J Neurosci Methods 2012; 210:22-34. [DOI: 10.1016/j.jneumeth.2012.04.006] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2011] [Revised: 04/04/2012] [Accepted: 04/05/2012] [Indexed: 11/17/2022]
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