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Meyer LM, Samann F, Schanze T. DualSort: online spike sorting with a running neural network. J Neural Eng 2023; 20:056031. [PMID: 37795548 DOI: 10.1088/1741-2552/acfb3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.
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Affiliation(s)
- L M Meyer
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - F Samann
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
- Department of Biomedical Engineering, University of Duhok, Kurdistan Region, Iraq
| | - T Schanze
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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Arribas DM, Marin-Burgin A, Morelli LG. Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus. eLife 2023; 12:e80250. [PMID: 37584478 PMCID: PMC10476965 DOI: 10.7554/elife.80250] [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: 05/13/2022] [Accepted: 08/15/2023] [Indexed: 08/17/2023] Open
Abstract
Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs' encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time-dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.
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Affiliation(s)
- Diego M Arribas
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
| | - Antonia Marin-Burgin
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
| | - Luis G Morelli
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico TecnologicoBuenos AiresArgentina
- Departamento de Fisica, FCEyN UBA, Ciudad UniversitariaBuenos AiresArgentina
- Max Planck Institute for Molecular Physiology, Department of Systemic Cell BiologyDortmundGermany
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3
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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.
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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
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4
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AbdelAty AM, Fouda ME, Eltawil A. Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms. Front Neuroinform 2022; 16:771730. [PMID: 35250525 PMCID: PMC8888432 DOI: 10.3389/fninf.2022.771730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 01/13/2022] [Indexed: 11/17/2022] Open
Abstract
The automatic fitting of spiking neuron models to experimental data is a challenging problem. The integrate and fire model and Hodgkin–Huxley (HH) models represent the two complexity extremes of spiking neural models. Between these two extremes lies two and three differential-equation-based models. In this work, we investigate the problem of parameter estimation of two simple neuron models with a sharp reset in order to fit the spike timing of electro-physiological recordings based on two problem formulations. Five optimization algorithms are investigated; three of them have not been used to tackle this problem before. The new algorithms show improved fitting when compared with the old ones in both problems under investigation. The improvement in fitness function is between 5 and 8%, which is achieved by using the new algorithms while also being more consistent between independent trials. Furthermore, a new problem formulation is investigated that uses a lower number of search space variables when compared to the ones reported in related literature.
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Affiliation(s)
- Amr M. AbdelAty
- Engineering Mathematics and Physics Department, Faculty of Engineering, Fayoum University, Faiyum, Egypt
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mohammed E. Fouda
- Center for Embedded & Cyber-Physical Systems, University of California, Irvine, Irvine, CA, United States
- Nanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt
- *Correspondence: Mohammed E. Fouda
| | - Ahmed Eltawil
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Williams E, Payeur A, Gidon A, Naud R. Neural burst codes disguised as rate codes. Sci Rep 2021; 11:15910. [PMID: 34354118 PMCID: PMC8342467 DOI: 10.1038/s41598-021-95037-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
Abstract
The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.
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Affiliation(s)
- Ezekiel Williams
- grid.28046.380000 0001 2182 2255Department of Mathematics and Statistics, University of Ottawa, 150 Louis Pasteur, Ottawa, K1N 6N5 Canada
| | - Alexandre Payeur
- grid.28046.380000 0001 2182 2255University of Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, K1H 8M5 Canada
| | - Albert Gidon
- grid.7468.d0000 0001 2248 7639Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Naud
- grid.28046.380000 0001 2182 2255University of Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, K1H 8M5 Canada ,grid.28046.380000 0001 2182 2255Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, K1N 6N5 Canada
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Wang X, Lin X, Dang X. Supervised learning in spiking neural networks: A review of algorithms and evaluations. Neural Netw 2020; 125:258-280. [PMID: 32146356 DOI: 10.1016/j.neunet.2020.02.011] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 12/15/2019] [Accepted: 02/20/2020] [Indexed: 01/08/2023]
Abstract
As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.
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Affiliation(s)
- Xiangwen Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, People's Republic of China
| | - Xianghong Lin
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, People's Republic of China.
| | - Xiaochao Dang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, People's Republic of China
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7
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Tezuka T. Multineuron spike train analysis with R-convolution linear combination kernel. Neural Netw 2018; 102:67-77. [PMID: 29544140 DOI: 10.1016/j.neunet.2018.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Revised: 11/14/2017] [Accepted: 02/20/2018] [Indexed: 11/16/2022]
Abstract
A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods.
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Affiliation(s)
- Taro Tezuka
- Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Japan; Faculty of Library, Information, and Media Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Japan.
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8
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Spike and burst coding in thalamocortical relay cells. PLoS Comput Biol 2018; 14:e1005960. [PMID: 29432418 PMCID: PMC5834212 DOI: 10.1371/journal.pcbi.1005960] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 03/02/2018] [Accepted: 01/08/2018] [Indexed: 11/19/2022] Open
Abstract
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA) and the Event-Triggered Covariance (ETC). This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms) and showed a clear distinction between spikes (selective for fluctuations) and bursts (selective for integration). The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT) and the cyclic nucleotide modulated h current (Ih). The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two currents. Finally, the model was used to investigate the more realistic “high-conductance state”, where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation. Neurons in the brain respond to (sensory) stimuli by generating electrical pulses called ‘spikes’ or ‘action potentials’. Spikes are organized in different temporal patterns, such as ‘bursts’ in which they occur at a high frequency followed by a period of silence. Bursts are ubiquitous in the nervous system: they occur in different parts of the brain and in different species. Different mechanisms that generate them have been pointed out. Why the nervous system uses bursts in its communication, or what type of information is represented by bursts, remains largely unknown. Here, we looked at bursting in thalamocortical relay (TCR) cells, neurons that form a bridge between early sensory processing and higher-order structures (cortex). These cells fire bursts as a result of the activation of two distinct subthreshold ionic currents: the T-type calcium current and the h-type current. We investigated experimentally and computationally what features in the input makes TCR cells respond with bursts, and what features with single spikes. Bursts are a response to low-frequency slowly increasing input; single spikes are a response to faster fluctuations. Moreover, bursts are rare and highly informative, in line with an earlier hypothesis that bursts could play a ‘wake-up call’ role in the nervous system.
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9
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Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T. Measures of spike train synchrony for data with multiple time scales. J Neurosci Methods 2017; 287:25-38. [PMID: 28583477 PMCID: PMC5508708 DOI: 10.1016/j.jneumeth.2017.05.028] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Revised: 05/04/2017] [Accepted: 05/30/2017] [Indexed: 10/29/2022]
Abstract
BACKGROUND Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. NEW METHOD In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing. RESULTS The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. COMPARISON WITH EXISTING METHODS We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization. CONCLUSIONS With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance).
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Affiliation(s)
- Eero Satuvuori
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; MOVE Research Institute, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, The Netherlands.
| | - Mario Mulansky
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
| | - Nebojsa Bozanic
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
| | - Irene Malvestio
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Fleur Zeldenrust
- Donders Institute for Brain Cognition and Behaviour, Radboud Universiteit, Nijmegen, The Netherlands.
| | - Kerstin Lenk
- BioMediTech, Tampere University of Technology, Tampere, Finland; DFG-Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany.
| | - Thomas Kreuz
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
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Box M, Jones MW, Whiteley N. A hidden Markov model for decoding and the analysis of replay in spike trains. J Comput Neurosci 2016; 41:339-366. [PMID: 27624733 PMCID: PMC5097117 DOI: 10.1007/s10827-016-0621-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 06/12/2016] [Accepted: 08/23/2016] [Indexed: 11/28/2022]
Abstract
We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.
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Affiliation(s)
- Marc Box
- Bristol Centre for Complexity Sciences, University of Bristol, Bristol, UK
| | - Matt W. Jones
- School of Physiology and Pharmacology, University of Bristol, Bristol, UK
| | - Nick Whiteley
- School of Mathematics, University of Bristol, Bristol, UK
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Mensi S, Hagens O, Gerstner W, Pozzorini C. Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons. PLoS Comput Biol 2016; 12:e1004761. [PMID: 26907675 PMCID: PMC4764342 DOI: 10.1371/journal.pcbi.1004761] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 01/19/2016] [Indexed: 11/25/2022] Open
Abstract
The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations. Over the last decades, a variety of simplified spiking models have been shown to achieve a surprisingly high performance in predicting the neuronal responses to in vitro somatic current injections. Because of the complex adaptive behavior featured by cortical neurons, this success is however restricted to limited stimulus ranges: model parameters optimized for a specific input regime are often inappropriate to describe the response to input currents with different statistical properties. In the present study, a new spiking neuron model is introduced that captures single-neuron computation over a wide range of input statistics and explains different aspects of the neuronal dynamics within a single framework. Our results indicate that complex forms of single neuron adaptation are mediated by the nonlinear dynamics of the firing threshold and that the input-output transformation performed by cortical pyramidal neurons can be intuitively understood in terms of an enhanced Generalized Linear Model in which both the input filter and the spike-history filter adapt to the input statistics.
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Affiliation(s)
- Skander Mensi
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olivier Hagens
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Wulfram Gerstner
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Christian Pozzorini
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail:
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12
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Pozzorini C, Mensi S, Hagens O, Naud R, Koch C, Gerstner W. Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models. PLoS Comput Biol 2015; 11:e1004275. [PMID: 26083597 PMCID: PMC4470831 DOI: 10.1371/journal.pcbi.1004275] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Accepted: 04/08/2015] [Indexed: 11/18/2022] Open
Abstract
Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.
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Affiliation(s)
- Christian Pozzorini
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail:
| | - Skander Mensi
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olivier Hagens
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Richard Naud
- Department of Physics, University of Ottawa, Ottawa, Canada
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Wulfram Gerstner
- Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Lynch EP, Houghton CJ. Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data. Front Neuroinform 2015; 9:10. [PMID: 25941485 PMCID: PMC4403314 DOI: 10.3389/fninf.2015.00010] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2014] [Accepted: 03/27/2015] [Indexed: 11/30/2022] Open
Abstract
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.
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Affiliation(s)
- Eoin P Lynch
- School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK
| | - Conor J Houghton
- Department of Computer Science, University of Bristol Bristol, UK
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Faghihi F, Moustafa AA. Impaired homeostatic regulation of feedback inhibition associated with system deficiency to detect fluctuation in stimulus intensity: a simulation study. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Naud R, Bathellier B, Gerstner W. Spike-timing prediction in cortical neurons with active dendrites. Front Comput Neurosci 2014; 8:90. [PMID: 25165443 PMCID: PMC4131408 DOI: 10.3389/fncom.2014.00090] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 07/20/2014] [Indexed: 11/13/2022] Open
Abstract
A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrite with only two compartments, without significant loss in the accuracy of spike-timing predictions. The model is based on experimentally measurable impulse-response functions, which transfer the effect of current injected in one compartment to current reaching the other. Each compartment was modeled with a pair of non-linear differential equations and a small number of parameters that approximate the Hodgkin-and-Huxley equations. The predictive power of this model was tested on electrophysiological experiments where noisy current was injected in both the soma and the apical dendrite simultaneously. We conclude that a simple two-compartment model can predict spike times of pyramidal cells stimulated in the soma and dendrites simultaneously. Our results support that regenerating activity in the apical dendritic is required to properly account for the dynamics of layer 5 pyramidal cells under in-vivo-like conditions.
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Affiliation(s)
- Richard Naud
- Department of Physics, University of Ottawa Ottawa, ON, Canada
| | - Brice Bathellier
- Cortical Dynamics and Multisensory Processing Team, Unit of Neuroscience Information and Complexity, CNRS UPR-3239 Gif-sur-Yvette, France
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and School of Life Sciences, Ecole Polytechnique Federale de Lausanne Lausanne, Switzerland
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16
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Abstract
The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.
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Affiliation(s)
- Cătălin V Rusu
- Center for Cognitive and Neural Studies (Coneural), Romanian Institute of Science and Technology, 400487 Cluj-Napoca, Romania; Computer Science Department, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania; and Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
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17
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Temporal whitening by power-law adaptation in neocortical neurons. Nat Neurosci 2013; 16:942-8. [PMID: 23749146 DOI: 10.1038/nn.3431] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2012] [Accepted: 05/08/2013] [Indexed: 11/08/2022]
Abstract
Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the firing threshold and by adaptation currents triggered after each spike. Combining electrophysiological recordings in mice with modeling, we found that these adaptation processes lasted for more than 20 s and decayed over multiple timescales according to a power law. The power-law decay associated with adaptation mirrored and canceled the temporal correlations of input current received in vivo at the somata of layer 2/3 somatosensory pyramidal neurons. These findings suggest that, in the cortex, SFA causes temporal decorrelation of output spikes (temporal whitening), an energy-efficient coding procedure that, at high signal-to-noise ratio, improves the information transfer.
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Zeldenrust F, Chameau PJP, Wadman WJ. Reliability of spike and burst firing in thalamocortical relay cells. J Comput Neurosci 2013; 35:317-34. [PMID: 23708878 DOI: 10.1007/s10827-013-0454-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Accepted: 04/16/2013] [Indexed: 10/26/2022]
Abstract
The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between -80 and -50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.
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Affiliation(s)
- Fleur Zeldenrust
- Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, P.O. Box 94215, 1090, GE, Amsterdam, The Netherlands,
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Zeldenrust F, Wadman WJ. Modulation of spike and burst rate in a minimal neuronal circuit with feed-forward inhibition. Neural Netw 2013; 40:1-17. [DOI: 10.1016/j.neunet.2012.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Revised: 12/18/2012] [Accepted: 12/19/2012] [Indexed: 02/07/2023]
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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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A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations. J Comput Neurosci 2012; 34:163-83. [PMID: 22878687 DOI: 10.1007/s10827-012-0412-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2012] [Revised: 06/04/2012] [Accepted: 06/27/2012] [Indexed: 10/28/2022]
Abstract
We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.
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Park IM, Seth S, Rao M, Príncipe JC. Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences. Neural Comput 2012; 24:2223-50. [DOI: 10.1162/neco_a_00309] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.
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Affiliation(s)
- Il Memming Park
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, U.S.A
| | - Sohan Seth
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, U.S.A
| | - Murali Rao
- Department of Mathematics, University of Florida, Gainesville, FL 32611, U.S.A
| | - José C. Príncipe
- Department of Biomedical Engineering and Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, U.S.A
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Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 2011; 107:1756-75. [PMID: 22157113 DOI: 10.1152/jn.00408.2011] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.e., membrane time constant, reversal potential, and capacitance), the spike-triggered adaptation currents, as well as the dynamics of the action potential threshold. The stochastic model that results from our maximum likelihood approach accurately predicts the spike times, the subthreshold voltage, the firing patterns, and the type of frequency-current curve. Extracting the model parameters for three cortical cell types revealed that cell types show highly significant differences in the time course of the spike-triggered currents and moving threshold, that is, in their adaptation and refractory properties but not in their passive properties. In particular, GABAergic fast-spiking neurons mediate weak adaptation through spike-triggered currents only, whereas regular spiking excitatory neurons mediate adaptation with both moving threshold and spike-triggered currents. GABAergic nonfast-spiking neurons combine the two distinct adaptation mechanisms with reduced strength. Differences between cell types are large enough to enable automatic classification of neurons into three different classes. Parameter extraction is performed for individual neurons so that we find not only the mean parameter values for each neuron type but also the spread of parameters within a group of neurons, which will be useful for future large-scale computer simulations.
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Affiliation(s)
- Skander Mensi
- School of Computer and Communication Sciences and School of Life Sciences, Ecole Polytechnique Federale de Lausanne, 1015 Lausanne EPFL, Switzerland.
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