1
|
Gast R, Solla SA, Kennedy A. Neural heterogeneity controls computations in spiking neural networks. Proc Natl Acad Sci U S A 2024; 121:e2311885121. [PMID: 38198531 PMCID: PMC10801870 DOI: 10.1073/pnas.2311885121] [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: 07/12/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024] Open
Abstract
The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.
Collapse
Affiliation(s)
- Richard Gast
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
| | - Sara A. Solla
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
| | - Ann Kennedy
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL60611
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
| |
Collapse
|
2
|
Hutt A, Trotter D, Pariz A, Valiante TA, Lefebvre J. Diversity-induced trivialization and resilience of neural dynamics. CHAOS (WOODBURY, N.Y.) 2024; 34:013147. [PMID: 38285722 DOI: 10.1063/5.0165773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/01/2024] [Indexed: 01/31/2024]
Abstract
Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.
Collapse
Affiliation(s)
- Axel Hutt
- MLMS, MIMESIS, Université de Strasbourg, CNRS, Inria, ICube, 67000 Strasbourg, France
| | - Daniel Trotter
- Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
| | - Aref Pariz
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Electrical and Computer Engineering, Institute of Medical Science, Institute of Biomedical Engineering, Division of Neurosurgery, Department of Surgery, CRANIA (Center for Advancing Neurotechnological Innovation to Application), Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, Ontario M5S 3G8, Canada
| | - Jérémie Lefebvre
- Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| |
Collapse
|
3
|
Hutt A, Rich S, Valiante TA, Lefebvre J. Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proc Natl Acad Sci U S A 2023; 120:e2218841120. [PMID: 37399421 PMCID: PMC10334753 DOI: 10.1073/pnas.2218841120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/19/2023] [Indexed: 07/05/2023] Open
Abstract
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
Collapse
Affiliation(s)
- Axel Hutt
- Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, StrasbourgF-67000, France
| | - Scott Rich
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
| | - Taufik A. Valiante
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ONM5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ONM5S 3G9, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ONM5S 1A8, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ONM5G 2C4, Canada
- Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ONM5G 2A2, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ONM5S 3G8, Canada
| | - Jérémie Lefebvre
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, ONK1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
| |
Collapse
|
4
|
Schmitt FJ, Rostami V, Nawrot MP. Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. Front Neuroinform 2023; 17:941696. [PMID: 36844916 PMCID: PMC9950635 DOI: 10.3389/fninf.2023.941696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/16/2023] [Indexed: 02/12/2023] Open
Abstract
Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.
Collapse
Affiliation(s)
| | | | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
| |
Collapse
|
5
|
Harkin EF, Lynn MB, Payeur A, Boucher JF, Caya-Bissonnette L, Cyr D, Stewart C, Longtin A, Naud R, Béïque JC. Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework. eLife 2023; 12:72951. [PMID: 36655738 PMCID: PMC9977298 DOI: 10.7554/elife.72951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/19/2022] [Indexed: 01/20/2023] Open
Abstract
By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.
Collapse
Affiliation(s)
- Emerson F Harkin
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Michael B Lynn
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Alexandre Payeur
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Jean-François Boucher
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Léa Caya-Bissonnette
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Dominic Cyr
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Chloe Stewart
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - André Longtin
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Richard Naud
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Jean-Claude Béïque
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| |
Collapse
|
6
|
Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony. Cell Rep 2022; 39:110863. [PMID: 35613586 DOI: 10.1016/j.celrep.2022.110863] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/16/2022] [Accepted: 05/03/2022] [Indexed: 12/25/2022] Open
Abstract
A myriad of pathological changes associated with epilepsy can be recast as decreases in cell and circuit heterogeneity. We thus propose recontextualizing epileptogenesis as a process where reduction in cellular heterogeneity, in part, renders neural circuits less resilient to seizure. By comparing patch clamp recordings from human layer 5 (L5) cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we demonstrate significantly decreased biophysical heterogeneity in seizure-generating areas. Implemented computationally, this renders model neural circuits prone to sudden transitions into synchronous states with increased firing activity, paralleling ictogenesis. This computational work also explains the surprising finding of significantly decreased excitability in the population-activation functions of neurons from epileptogenic tissue. Finally, mathematical analyses reveal a bifurcation structure arising only with low heterogeneity and associated with seizure-like dynamics. Taken together, this work provides experimental, computational, and mathematical support for the theory that ictogenic dynamics accompany a reduction in biophysical heterogeneity.
Collapse
|
7
|
Metzner C, Krauss P. Dynamics and Information Import in Recurrent Neural Networks. Front Comput Neurosci 2022; 16:876315. [PMID: 35573264 PMCID: PMC9091337 DOI: 10.3389/fncom.2022.876315] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/04/2022] [Indexed: 12/27/2022] Open
Abstract
Recurrent neural networks (RNNs) are complex dynamical systems, capable of ongoing activity without any driving input. The long-term behavior of free-running RNNs, described by periodic, chaotic and fixed point attractors, is controlled by the statistics of the neural connection weights, such as the density d of non-zero connections, or the balance b between excitatory and inhibitory connections. However, for information processing purposes, RNNs need to receive external input signals, and it is not clear which of the dynamical regimes is optimal for this information import. We use both the average correlations C and the mutual information I between the momentary input vector and the next system state vector as quantitative measures of information import and analyze their dependence on the balance and density of the network. Remarkably, both resulting phase diagrams C(b, d) and I(b, d) are highly consistent, pointing to a link between the dynamical systems and the information-processing approach to complex systems. Information import is maximal not at the "edge of chaos," which is optimally suited for computation, but surprisingly in the low-density chaotic regime and at the border between the chaotic and fixed point regime. Moreover, we find a completely new type of resonance phenomenon, which we call "Import Resonance" (IR), where the information import shows a maximum, i.e., a peak-like dependence on the coupling strength between the RNN and its external input. IR complements previously found Recurrence Resonance (RR), where correlation and mutual information of successive system states peak for a certain amplitude of noise added to the system. Both IR and RR can be exploited to optimize information processing in artificial neural networks and might also play a crucial role in biological neural systems.
Collapse
Affiliation(s)
- Claus Metzner
- Neuroscience Lab, University Hospital Erlangen, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, University Hospital Erlangen, Erlangen, Germany
- Cognitive Computational Neuroscience Group, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| |
Collapse
|
8
|
Di Volo M, Destexhe A. Optimal responsiveness and information flow in networks of heterogeneous neurons. Sci Rep 2021; 11:17611. [PMID: 34475456 PMCID: PMC8413388 DOI: 10.1038/s41598-021-96745-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/11/2021] [Indexed: 02/07/2023] Open
Abstract
Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.
Collapse
Affiliation(s)
- Matteo Di Volo
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, 95302, Cergy-Pontoise cedex, France.
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif sur Yvette, France
| |
Collapse
|
9
|
Takahashi Y, Murata S, Idei H, Tomita H, Yamashita Y. Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework. Sci Rep 2021; 11:14684. [PMID: 34312400 PMCID: PMC8313712 DOI: 10.1038/s41598-021-94067-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/06/2021] [Indexed: 11/20/2022] Open
Abstract
The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory. Predictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated. After the developmental learning process, emotional clusters emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional clustering in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition. These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.
Collapse
Affiliation(s)
- Yuta Takahashi
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Information Medicine, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8502, Japan
| | - Shingo Murata
- Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, Tokyo, Japan
| | - Hayato Idei
- Department of Intermedia Studies, Waseda University, Tokyo, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8502, Japan.
| |
Collapse
|
10
|
Deschle N, Ignacio Gossn J, Tewarie P, Schelter B, Daffertshofer A. On the Validity of Neural Mass Models. Front Comput Neurosci 2021; 14:581040. [PMID: 33469424 PMCID: PMC7814001 DOI: 10.3389/fncom.2020.581040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/01/2020] [Indexed: 11/26/2022] Open
Abstract
Modeling the dynamics of neural masses is a common approach in the study of neural populations. Various models have been proven useful to describe a plenitude of empirical observations including self-sustained local oscillations and patterns of distant synchronization. We discuss the extent to which mass models really resemble the mean dynamics of a neural population. In particular, we question the validity of neural mass models if the population under study comprises a mixture of excitatory and inhibitory neurons that are densely (inter-)connected. Starting from a network of noisy leaky integrate-and-fire neurons, we formulated two different population dynamics that both fall into the category of seminal Freeman neural mass models. The derivations contained several mean-field assumptions and time scale separation(s) between membrane and synapse dynamics. Our comparison of these neural mass models with the averaged dynamics of the population reveals bounds in the fraction of excitatory/inhibitory neuron as well as overall network degree for a mass model to provide adequate estimates. For substantial parameter ranges, our models fail to mimic the neural network's dynamics proper, be that in de-synchronized or in (high-frequency) synchronized states. Only around the onset of low-frequency synchronization our models provide proper estimates of the mean potential dynamics. While this shows their potential for, e.g., studying resting state dynamics obtained by encephalography with focus on the transition region, we must accept that predicting the more general dynamic outcome of a neural network via its mass dynamics requires great care.
Collapse
Affiliation(s)
- Nicolás Deschle
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences & Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Institute for Complex Systems and Mathematical Biology, University of Aberdeen, King's College, Aberdeen, United Kingdom
| | - Juan Ignacio Gossn
- Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Astronomía y Física del Espacio, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires, Argentina
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.,Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, King's College, Aberdeen, United Kingdom
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences & Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
11
|
Capogna M, Castillo PE, Maffei A. The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles. Eur J Neurosci 2020; 54:6882-6901. [PMID: 32663353 DOI: 10.1111/ejn.14907] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/30/2020] [Accepted: 07/08/2020] [Indexed: 01/05/2023]
Abstract
GABAergic interneurons are highly diverse, and their synaptic outputs express various forms of plasticity. Compelling evidence indicates that activity-dependent changes of inhibitory synaptic transmission play a significant role in regulating neural circuits critically involved in learning and memory and circuit refinement. Here, we provide an updated overview of inhibitory synaptic plasticity with a focus on the hippocampus and neocortex. To illustrate the diversity of inhibitory interneurons, we discuss the case of two highly divergent interneuron types, parvalbumin-expressing basket cells and neurogliaform cells, which support unique roles on circuit dynamics. We also present recent progress on the molecular mechanisms underlying long-term, activity-dependent plasticity of fast inhibitory transmission. Lastly, we discuss the role of inhibitory synaptic plasticity in neuronal circuits' function. The emerging picture is that inhibitory synaptic transmission in the CNS is extremely diverse, undergoes various mechanistically distinct forms of plasticity and contributes to a much more refined computational role than initially thought. Both the remarkable diversity of inhibitory interneurons and the various forms of plasticity expressed by GABAergic synapses provide an amazingly rich inhibitory repertoire that is central to a variety of complex neural circuit functions, including memory.
Collapse
Affiliation(s)
- Marco Capogna
- Department of Biomedicine, Danish National Research Foundation Center of Excellence PROMEMO, Aarhus University, Aarhus, Denmark
| | - Pablo E Castillo
- Dominck P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Arianna Maffei
- Center for Neural Circuit Dynamics and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
12
|
Idei H, Murata S, Yamashita Y, Ogata T. Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder. Front Psychiatry 2020; 11:762. [PMID: 32903328 PMCID: PMC7434834 DOI: 10.3389/fpsyt.2020.00762] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric symptoms and an unusual estimation of information uncertainty (precision), which is an essential aspect of the real world and is estimated through learning processes. Here, we propose a mechanistic explanation that unifies the disparate observations via a hierarchical predictive coding and developmental learning framework, which is demonstrated in experiments using a neural network-controlled robot. The results show that, through the developmental learning process, homogeneous intrinsic neuronal excitability at the neural level induced via self-organization changes at the information processing level, such as hyper sensory precision and overfitting to sensory noise. These changes led to multifaceted alterations at the behavioral level, such as inflexibility, reduced generalization, and motor clumsiness. In addition, these behavioral alterations were accompanied by fluctuating neural activity and excessive development of synaptic connections. These findings might bridge various levels of understandings in autism spectrum and other neurodevelopmental disorders and provide insights into the disease processes underlying observed behaviors and brain activities in individual patients. This study shows the potential of neurorobotics frameworks for modeling how psychiatric disorders arise from dynamic interactions among the brain, body, and uncertain environments.
Collapse
Affiliation(s)
- Hayato Idei
- Department of Intermedia Studies, Waseda University, Tokyo, Japan
| | - Shingo Murata
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tetsuya Ogata
- Department of Intermedia Art and Science, Waseda University, Tokyo, Japan
| |
Collapse
|
13
|
Luccioli S, Angulo-Garcia D, Torcini A. Neural activity of heterogeneous inhibitory spiking networks with delay. Phys Rev E 2019; 99:052412. [PMID: 31212434 DOI: 10.1103/physreve.99.052412] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Indexed: 11/07/2022]
Abstract
We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a winner-takes-all mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of subthreshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behavior alternating periods of silence with periods where they fire freely in absence of any inhibition.
Collapse
Affiliation(s)
- Stefano Luccioli
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
| | - David Angulo-Garcia
- Grupo de Modelado Computacional-Dinámica y Complejidad de Sistemas. Instituto de Matemáticas Aplicadas. Universidad de Cartagena. Carrera 6 # 36 - 100, Cartagena de Indias, Colombia
| | - Alessandro Torcini
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy.,Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, 95302 Cergy-Pontoise cedex, France
| |
Collapse
|
14
|
Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators. Sci Rep 2018; 8:8370. [PMID: 29849108 PMCID: PMC5976724 DOI: 10.1038/s41598-018-26730-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 05/11/2018] [Indexed: 12/20/2022] Open
Abstract
Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that – when isolated – can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.
Collapse
|
15
|
Tsai KT, Hu CK, Li KW, Hwang WL, Chou YH. Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity. Sci Rep 2018; 8:8027. [PMID: 29795277 PMCID: PMC5966413 DOI: 10.1038/s41598-018-26286-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 05/09/2018] [Indexed: 01/15/2023] Open
Abstract
Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.
Collapse
Affiliation(s)
- Kuo-Ting Tsai
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.,Institute of Physics, Academia Sinica, Taipei, Taiwan
| | - Chin-Kun Hu
- Institute of Physics, Academia Sinica, Taipei, Taiwan.,Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.,National Center for Theoretical Sciences, National Tsing Hua University, Hsinchu, Taiwan.,Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Kuan-Wei Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan
| | - Wen-Liang Hwang
- Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Ya-Hui Chou
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan. .,Neuroscience Program of Academia Sinica, Academia Sinica, Taipei, Taiwan.
| |
Collapse
|
16
|
Joglekar MR, Mejias JF, Yang GR, Wang XJ. Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex. Neuron 2018; 98:222-234.e8. [DOI: 10.1016/j.neuron.2018.02.031] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 12/27/2017] [Accepted: 02/27/2018] [Indexed: 01/19/2023]
|
17
|
Kopeikina E, Dukhinova M, Ponomarev ED. Methods of Study of Neuron Structural Heterogeneity: Flow Cytometry vs. Laser Interferometry. Methods Mol Biol 2018; 1745:155-166. [PMID: 29476468 DOI: 10.1007/978-1-4939-7680-5_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Neuronal cells are probably the less studied cells regarding their heterogeneity on a single cell or population levels. One of the main problems of studying of individual neurons is the presence of long processes (axons) on differentiated adult neurons that hamper their isolation without significant damage to the cells. Therefore, the most common method to study neuronal cells is immunofluorescent microscopy of sections of the brain, which remains poorly quantitative and allows analyzing a small number of fixed cells. Also, immunofluorescent microscopy has a number of staining artifacts since histology section has high level of autofluorescence and non-specific binding of fluorescent probes. Alternative methods that could overcome disadvantages of immunofluorescent histology include flow cytometry, scanning cytometry, and laser interferometry. Flow cytometry and, to some extent of degree, scanning cytometry allow performing analysis of multiple markers with a low level of non-specific background and very robust statistics. Laser interferometry allows studies intact, alive neurons without staining. Limitations and advantages of these methods are discussed in this chapter.
Collapse
Affiliation(s)
- Ekaterina Kopeikina
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Marina Dukhinova
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Eugene D Ponomarev
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SAR, China.
| |
Collapse
|
18
|
Garcia Del Molino LC, Yang GR, Mejias JF, Wang XJ. Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types. eLife 2017; 6:29742. [PMID: 29256863 PMCID: PMC5777826 DOI: 10.7554/elife.29742] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 12/02/2017] [Indexed: 11/13/2022] Open
Abstract
Pyramidal cells and interneurons expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP) show cell-type-specific connectivity patterns leading to a canonical microcircuit across cortex. Experiments recording from this circuit often report counterintuitive and seemingly contradictory findings. For example, the response of SST cells in mouse V1 to top-down behavioral modulation can change its sign when the visual input changes, a phenomenon that we call response reversal. We developed a theoretical framework to explain these seemingly contradictory effects as emerging phenomena in circuits with two key features: interactions between multiple neural populations and a nonlinear neuronal input-output relationship. Furthermore, we built a cortical circuit model which reproduces counterintuitive dynamics observed in mouse V1. Our analytical calculations pinpoint connection properties critical to response reversal, and predict additional novel types of complex dynamics that could be tested in future experiments.
Collapse
Affiliation(s)
| | | | - Jorge F Mejias
- Center for Neural Science, New York University, New York, United States
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, United States
| |
Collapse
|
19
|
Rich S, Zochowski M, Booth V. Dichotomous Dynamics in E-I Networks with Strongly and Weakly Intra-connected Inhibitory Neurons. Front Neural Circuits 2017; 11:104. [PMID: 29326558 PMCID: PMC5733501 DOI: 10.3389/fncir.2017.00104] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 12/04/2017] [Indexed: 11/13/2022] Open
Abstract
The interconnectivity between excitatory and inhibitory neural networks informs mechanisms by which rhythmic bursts of excitatory activity can be produced in the brain. One such mechanism, Pyramidal Interneuron Network Gamma (PING), relies primarily upon reciprocal connectivity between the excitatory and inhibitory networks, while also including intra-connectivity of inhibitory cells. The causal relationship between excitatory activity and the subsequent burst of inhibitory activity is of paramount importance to the mechanism and has been well studied. However, the role of the intra-connectivity of the inhibitory network, while important for PING, has not been studied in detail, as most analyses of PING simply assume that inhibitory intra-connectivity is strong enough to suppress subsequent firing following the initial inhibitory burst. In this paper we investigate the role that the strength of inhibitory intra-connectivity plays in determining the dynamics of PING-style networks. We show that networks with weak inhibitory intra-connectivity exhibit variations in burst dynamics of both the excitatory and inhibitory cells that are not obtained with strong inhibitory intra-connectivity. Networks with weak inhibitory intra-connectivity exhibit excitatory rhythmic bursts with weak excitatory-to-inhibitory synapses for which classical PING networks would show no rhythmic activity. Additionally, variations in dynamics of these networks as the excitatory-to-inhibitory synaptic weight increases illustrates the important role that consistent pattern formation in the inhibitory cells serves in maintaining organized and periodic excitatory bursts. Finally, motivated by these results and the known diversity of interneurons, we show that a PING-style network with two inhibitory subnetworks, one strongly intra-connected and one weakly intra-connected, exhibits organized and periodic excitatory activity over a larger parameter regime than networks with a homogeneous inhibitory population. Taken together, these results serve to better articulate the role of inhibitory intra-connectivity in generating PING-like rhythms, while also revealing how heterogeneity amongst inhibitory synapses might make such rhythms more robust to a variety of network parameters.
Collapse
Affiliation(s)
- Scott Rich
- Applied and Interdisciplinary Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Michal Zochowski
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics and Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
20
|
Beiran M, Kruscha A, Benda J, Lindner B. Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations. J Comput Neurosci 2017; 44:189-202. [PMID: 29222729 DOI: 10.1007/s10827-017-0674-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 11/08/2017] [Accepted: 11/12/2017] [Indexed: 11/29/2022]
Abstract
We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disorder depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show that, depending on the type of signal, noise as well as heterogeneity can enhance the encoding performance of neuronal populations.
Collapse
Affiliation(s)
- Manuel Beiran
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany. .,Group for Neural Theory, Laboratoire de Neurosciences Cognitives, Département Études Cognitives, École Normale Supérieure, INSERM, PSL Research University, Paris, France.
| | - Alexandra Kruscha
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jan Benda
- Institute for Neurobiology, Eberhard Karls Universität, Tübingen, Germany
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Physics Department, Humboldt-Universität zu Berlin, Berlin, Germany
| |
Collapse
|
21
|
Li R, Yin S, Zhu X, Ren W, Yu J, Wang P, Zheng Z, Niu YN, Huang X, Li J. Linking Inter-Individual Variability in Functional Brain Connectivity to Cognitive Ability in Elderly Individuals. Front Aging Neurosci 2017; 9:385. [PMID: 29209203 PMCID: PMC5702299 DOI: 10.3389/fnagi.2017.00385] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 11/09/2017] [Indexed: 12/11/2022] Open
Abstract
Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity-cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases.
Collapse
Affiliation(s)
- Rui Li
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Shufei Yin
- Department of Psychology, Faculty of Education, Hubei University, Wuhan, China
| | - Xinyi Zhu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Weicong Ren
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Education, Hebei Normal University, Shijiazhuang, China
| | - Jing Yu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Faculty of Psychology, Southwest University, Chongqing, China
| | - Pengyun Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Zhiwei Zheng
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ya-Nan Niu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xin Huang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Li
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
22
|
Ly C, Marsat G. Variable synaptic strengths controls the firing rate distribution in feedforward neural networks. J Comput Neurosci 2017; 44:75-95. [DOI: 10.1007/s10827-017-0670-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 12/27/2022]
|
23
|
Mejias JF, Murray JD, Kennedy H, Wang XJ. Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. SCIENCE ADVANCES 2016; 2:e1601335. [PMID: 28138530 PMCID: PMC5262462 DOI: 10.1126/sciadv.1601335] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 10/20/2016] [Indexed: 05/25/2023]
Abstract
Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.
Collapse
Affiliation(s)
- Jorge F. Mejias
- Center for Neural Science, New York University (NYU), New York, NY 10003, USA
| | - John D. Murray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Henry Kennedy
- Stem Cell and Brain Research Institute, INSERM U846, Bron, France
- Université de Lyon, Université Lyon I, Lyon, France
| | - Xiao-Jing Wang
- Center for Neural Science, New York University (NYU), New York, NY 10003, USA
- NYU–East China Normal University Institute for Brain and Cognitive Science, NYU Shanghai, Shanghai, China
| |
Collapse
|
24
|
Gollo LL, Copelli M, Roberts JA. Diversity improves performance in excitable networks. PeerJ 2016; 4:e1912. [PMID: 27168961 PMCID: PMC4860327 DOI: 10.7717/peerj.1912] [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: 12/07/2015] [Accepted: 03/17/2016] [Indexed: 11/20/2022] Open
Abstract
As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.
Collapse
Affiliation(s)
- Leonardo L Gollo
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mauro Copelli
- Departamento de Física, Universidade Federal de Pernambuco , Recife PE , Brazil
| | - James A Roberts
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| |
Collapse
|
25
|
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity. J Comput Neurosci 2015; 39:311-27. [DOI: 10.1007/s10827-015-0578-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 07/06/2015] [Accepted: 09/23/2015] [Indexed: 11/25/2022]
|
26
|
Harrison PM, Badel L, Wall MJ, Richardson MJE. Experimentally Verified Parameter Sets for Modelling Heterogeneous Neocortical Pyramidal-Cell Populations. PLoS Comput Biol 2015; 11:e1004165. [PMID: 26291316 PMCID: PMC4546387 DOI: 10.1371/journal.pcbi.1004165] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 01/30/2015] [Indexed: 11/19/2022] Open
Abstract
Models of neocortical networks are increasingly including the diversity of excitatory and inhibitory neuronal classes. Significant variability in cellular properties are also seen within a nominal neuronal class and this heterogeneity can be expected to influence the population response and information processing in networks. Recent studies have examined the population and network effects of variability in a particular neuronal parameter with some plausibly chosen distribution. However, the empirical variability and covariance seen across multiple parameters are rarely included, partly due to the lack of data on parameter correlations in forms convenient for model construction. To addess this we quantify the heterogeneity within and between the neocortical pyramidal-cell classes in layers 2/3, 4, and the slender-tufted and thick-tufted pyramidal cells of layer 5 using a combination of intracellular recordings, single-neuron modelling and statistical analyses. From the response to both square-pulse and naturalistic fluctuating stimuli, we examined the class-dependent variance and covariance of electrophysiological parameters and identify the role of the h current in generating parameter correlations. A byproduct of the dynamic I-V method we employed is the straightforward extraction of reduced neuron models from experiment. Empirically these models took the refractory exponential integrate-and-fire form and provide an accurate fit to the perisomatic voltage responses of the diverse pyramidal-cell populations when the class-dependent statistics of the model parameters were respected. By quantifying the parameter statistics we obtained an algorithm which generates populations of model neurons, for each of the four pyramidal-cell classes, that adhere to experimentally observed marginal distributions and parameter correlations. As well as providing this tool, which we hope will be of use for exploring the effects of heterogeneity in neocortical networks, we also provide the code for the dynamic I-V method and make the full electrophysiological data set available. Neurons are the fundamental components of the nervous system and a quantitative description of their properties is a prerequisite to understanding the complex structures they comprise, from microcircuits to networks. Mathematical modelling provides an essential tool to this end and there has been intense effort directed at analysing networks constructed from different classes of neurons. However, even neurons from the same class show a broad variability in parameter values and the distributions and correlations between these parameters are likely to significantly affect network properties. To quantify this variability, we used a combination of intracellular recording, single-neuron modelling, and statistical analysis to measure the physiological variability in pyramidal-cell populations of the neocortex. We employ protocols that measure parameters from both square-pulse and naturalistic stimuli, characterising the perisomatic integration properties of these cells and allowing for the straightforward extraction of mathematically tractable reduced neuron models. We provide algorithms to generate populations of these neuron models that respect the parameter variability and co-variability observed in our experiments. These represent novel tools for exploring heterogeneity in neocortical networks that will be useful for subsequent theoretical and numerical studies. Finally, we make our full electrophysiological dataset available for other research groups to extend and improve on our analysis.
Collapse
Affiliation(s)
- Paul M. Harrison
- MOAC Doctoral Training Centre, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Laurent Badel
- Laboratory for Circuit Mechanisms of Sensory Perception, RIKEN Brain Science Institute, Wako, Saitama, Japan
| | - Mark J. Wall
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | | |
Collapse
|
27
|
Baroni F, Mazzoni A. Heterogeneity of heterogeneities in neuronal networks. Front Comput Neurosci 2014; 8:161. [PMID: 25540619 PMCID: PMC4261729 DOI: 10.3389/fncom.2014.00161] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 11/21/2014] [Indexed: 11/13/2022] Open
Affiliation(s)
- Fabiano Baroni
- School of Psychological Sciences, Monash University Clayton, VIC, Australia ; NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne Parkville, VIC, Australia ; Centre for Neural Engineering, University of Melbourne Carlton, VIC, Australia
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna Pontedera, Italy
| |
Collapse
|