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Multiplexed Spike coding and Adaptation in the Thalamus

CELL REPORTS 2017年第6期19卷 1130-1140页

作者： Mease, Rebecca A. Kuner, Thomas Fairhall, Adrienne L. Groh, Alexander Tech Univ Munich Dept Neurosurg D-81675 Munich Germany Univ Washington Neurobiol & Behav Grad Program Seattle WA 98195 USA Heidelberg Univ Dept Funct Neuroanat D-69120 Heidelberg Germany Univ Washington Dept Physiol & Biophys Seattle WA 98195 USA

High-frequency "burst'' clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ "multiplexing'' to differentially encode low-and high-frequency stimulus features associated with either T-typecalcium "low-threshold'' or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1) burst size, (2) burst onset time, and (3) precise spike timing within bursts. Strikingly, this latter "intraburst'' encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

关键词： neural coding linear-nonlinear model adaptation thalamocortical system bursting T-type calcium channel excitability multiplexing

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Contrast coding in the primary visual cortex depends on temporal contexts

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EUROPEAN JOURNAL OF NEUROSCIENCE 2018年第8期47卷 947-958页

作者： Dai, Ji Wang, Yi Chinese Acad Sci Inst Biophys State Key Lab Brain & Cognit Sci Beijing 100101 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Neuropsychiat Modulat Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Ctr Excellence Brain Sci & Intelligence Techn Collaborat Innovat Ctr Brain SciBrain Cognit & B Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100101 Peoples R China

Contrast response function in the primary visual cortex (V1) has long been described as following a sigmoid curve. However, this is mainly based on measuring neural responses to drifting contrast grating in a stable stimulation, a model that does not consider the effects of motion or length of stimulus presentation. During natural viewing, the visual system can obtain sufficient information for identifying the shapes defined by contrast from a single glance;acquiring greater knowledge of the neuronal response properties to contrast in such a short timescale is necessary to understand the underlying mechanisms. We investigated responses of cat V1 neurons to contrast presented by static grating for 40ms without pause compared to drifting grating presented continuously for 2000ms. The neuronal response to transiently presented contrast could be well described by a linear function. Further examination of the effects of motion and presentation duration on contrast responses demonstrated that motion increased response sensitivity in the low-contrast range, while brief presentation increased response sensitivity in the high-contrast range. Motion and prolonged presentation (adaptation) together resulted in an asymptotic sigmoid curve with a saturation response in the high-contrast range. These results suggest that motion mainly enhance the neural response sensitivity to low-contrast objects, while short and rapid presentation mainly enhance the neural sensitivity to high-contrast stimulus. Our findings indicate that multiple factors influence the properties of contrast response functions, suggesting that V1 neuron contrast coding is flexible and depends on the temporal contexts.

关键词： contrast response motion neural coding stimulus duration striate cortex

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Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

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NEUROINFORMATICS 2009年第3期7卷 165-178页

作者： Goldberg, David H. Victor, Jonathan D. Gardner, Esther P. Gardner, Daniel Cornell Univ Weill Med Coll Lab Neuroinformat D404 New York NY 10065 USA Cornell Univ Weill Med Coll Dept Physiol New York NY 10065 USA Cornell Univ Weill Med Coll Dept Neurol & Neurosci New York NY 10065 USA Cornell Univ Weill Med Coll Lab Neuroinformat New York NY 10065 USA NYU Sch Med Dept Physiol & Neurosci New York NY 10016 USA

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://***, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

关键词： Computational neuroscience Information theory neural coding Neurodatabases Data sharing

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Algebraic signatures of convex and non-convex codes

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JOURNAL OF PURE AND APPLIED ALGEBRA 2019年第9期223卷 3919-3940页

作者： Curto, Carina Gross, Elizabeth Jeffries, Jack Morrison, Katherine Rosen, Zvi Shiu, Anne Youngs, Nora Penn State Univ Dept Math University Pk PA 16802 USA Univ Hawaii Manoa Dept Math Honolulu HI 96822 USA Univ Michigan Dept Math Ann Arbor MI 48109 USA Univ Northern Colorado Sch Math Sci Greeley CO 80639 USA Florida Atlantic Univ Dept Math Sci Boca Raton FL 33431 USA Texas A&M Univ Dept Math College Stn TX 77843 USA Colby Coll Dept Math & Stat Waterville ME 04901 USA

A convex code is a binary code generated by the pattern of intersections of a collection of open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as they arise from the activity of neurons that have convex receptive fields. In this paper, we develop algebraic methods to determine if a code is convex. Specifically, we use the neural ideal of a code, which is a generalization of the Stanley Reisner ideal. Using the neural ideal together with its standard generating set, the canonical form, we provide algebraic signatures of certain families of codes that are non-convex. We connect these signatures to the precise conditions on the arrangement of sets that prevent the codes from being convex. Finally, we also provide algebraic signatures for some families of codes that are convex, including the class of intersection-complete codes. These results allow us to detect convexity and non-convexity in a variety of situations, and point to some interesting open questions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： neural coding Convex codes neural ideal Local obstructions Simplicial complexes Links

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In vivo conditions influence the coding of stimulus features by bursts of action potentials

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2011年第2期31卷 369-383页

作者： Akerberg, Oscar Avila Chacron, Maurice J. McGill Univ Dept Phys Montreal PQ Canada McGill Univ Dept Physiol Montreal PQ Canada

The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions. While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results

关键词： Weakly electric fish Burst neural coding Model

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The Ising decoder: reading out the activity of large neural ensembles

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2012年第1期32卷 101-118页

作者： Schaub, Michael T. Schultz, Simon R. Univ London Imperial Coll Sci Technol & Med Dept Bioengn London SW7 2AZ England

The Ising model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.

关键词： neural coding Decoding algorithm Brain machine interface Brain computer interface BCI Correlations Synchrony Visual cortex Spatial patterns

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Melanopsin-driven increases in maintained activity enhance thalamic visual response reliability across a simulated dawn

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2015年第42期112卷 E5734-E5743页

作者： Storchi, Riccardo Milosavljevic, Nina Eleftheriou, Cyril G. Martial, Franck P. Orlowska-Feuer, Patrycja Bedford, Robert A. Brown, Timothy M. Montemurro, Marcelo A. Petersen, Rasmus S. Lucas, Robert J. Univ Manchester Fac Life Sci Manchester M13 9PT Lancs England Jagiellonian Univ Inst Zool Dept Neurophysiol & Chronobiol PL-30387 Krakow Poland

Twice a day, at dawn and dusk, we experience gradual but very high amplitude changes in background light intensity (irradiance). Although we perceive the associated change in environmental brightness, the representation of such very slow alterations in irradiance by the early visual system has been little studied. Here, we addressed this deficit by recording electrophysiological activity in the mouse dorsal lateral geniculate nucleus under exposure to a simulated dawn. As irradiance increased we found a widespread enhancement in baseline firing that extended to units with ON as well as OFF responses to fast luminance increments. This change in baseline firing was equally apparent when the slow irradiance ramp appeared alone or when a variety of higher-frequency artificial or natural visual stimuli were superimposed upon it. Using a combination of conventional knockout, chemogenetic, and receptor-silent substitution manipulations, we continued to show that, over higher irradiances, this increase in firing originates with inner-retinal melanopsin photoreception. At the single-unit level, irradiance-dependent increases in baseline firing were strongly correlated with improvements in the amplitude of responses to higher-frequency visual stimuli. This in turn results in an up to threefold increase in single-trial reliability of fast visual responses. In this way, our data indicate that melanopsin drives a generalized increase in dorsal lateral geniculate nucleus excitability as dawn progresses that both conveys information about changing background light intensity and increases the signal: noise for fast visual responses.

关键词： melanopsin irradiance neural coding silent substitution DREADD

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Representing the dynamics of high-dimensional data with non-redundant wavelets

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PATTERNS 2022年第3期3卷 100424页

作者： Jia, Shanshan Li, Xingyi Huang, Tiejun Liu, Jian K. Yu, Zhaofei Peking Univ Inst Artificial Intelligence Beijing 100871 Peoples R China Peking Univ Dept Comp Sci & Technol Beijing 100871 Peoples R China Chongqing Univ Ctr Neurointelligence Sch Med Chongqing 400030 Peoples R China Univ Leeds Sch Comp Leeds LS2 9JT W Yorkshire England

A crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a fewlevels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features.

关键词： neural coding wavelet analysis mutual information conditional information feature selection dimensionality reduction neural spikes calcium imaging ECoG

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Learning shapes cortical dynamics to enhance integration of relevant sensory input

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NEURON 2023年第1期111卷 106-+页

作者： Chadwick, Angus Khan, Adil G. Poort, Jasper Blot, Antonin Hofer, Sonja B. Mrsic-Flogel, Thomas D. Sahani, Maneesh UCL Gatsby Computat Neurosci Unit London England UCL Sainsbury Wellcome Ctr Neural Circuits & Behav London England Univ Edinburgh Inst Adapt & Neural Computat Sch Informat Edinburgh Scotland Kings Coll London Ctr Dev Neurobiol London England Univ Cambridge Dept Physiol Dev & Neurosci Cambridge England

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

关键词： neural coding network dynamics cortical circuits learning decision making visual cortex noise correlations computational model dynamical systems sensory processing

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INFORMATION PROCESSING ACROSS BEHAVIORAL STATES: MODES OF OPERATION AND POPULATION DYNAMICS IN RODENT SENSORY CORTEX

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NEUROSCIENCE 2018年第0期368卷 214-228页

作者： Sabri, Mohammad Mahdi Arabzadeh, Ehsan Australian Natl Univ Eccles Inst Neurosci John Curtin Sch Med Res Canberra ACT Australia Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Australian Natl Univ Node Ctr Excellence Integrat Brain Funct Australian Res Council Canberra ACT Australia

Animals live in a complex and changing environment with various degrees of behavioral demands. In rodents, the behavioral states can change from sleep and quiet wakefulness to active exploration of the environment which is often manifested by whisking and locomotion. Efficient information processing is more important in some of these behavioral states such as during episodes of sensory decision-making, and specific cortical areas are expected to receive priority of processing depending on the behavioral context. It is therefore not surprising that the behavioral state affects the responsiveness of individual cortical neurons and the dynamics of neuronal population activity. Here, we review the circuit mechanisms that determine the operating mode of the sensory cortex. We explore state modulations across multiple sensory modalities, but maintain a focus on whisker-mediated behaviors, the processing of information in the vibrissal somatosensory cortex and its transfer to higher order areas. Finally, we suggest a rodent sensory prioritization paradigm to further probe the link between behavioral state, neuronal population dynamics and coding efficiency. This article is part of a Special Issue entitled: Barrel Cortex. (C) 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： behavioral state cortical circuits neural coding barrel cortex attention sensory prioritization

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