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作者： Mehmet Fişek James M. Jeanne

Optogenetics enables experimental control over neural activity using light. Channelrhodopsin and its variants are typically activated using visible light excitation but can also be activated using infrared two-photon excitation. Two-photon excitation can improve the spatial precision of stimulation in scattering tissue but has several practical limitations that need to be considered before use. Here we describe the methodology and best practices for using two-photon optogenetic stimulation of neurons within the brain of the fruit fly, Drosophila melanogaster, with an emphasis on projection neurons of the antennal lobe. less

关键词： Optogenetics Two-photon Microscopy Microdissection Immunohistochemistry Two-photon Imaging Electrophysiology Dissection Drosophila melanogaster Drosophila neural circuits neural coding

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Methods for Studying Functional Interactions Among Neuronal Populations

Methods for Studying Functional Interactions Among Neuronal ...

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作者： Nandakumar S. Narayanan Mark Laubach

How do populations of neurons work together to control behavior? To study this issue, our group simultaneously records from populations of neurons across multiple electrodes in multiple brain regions during operant behavior. Here, we describe methods for quantifying the relationship between neuronal population activity and performance of operant behavioral tasks. We describe statistical techniques, based on time- and trial-shuffling, that can establish the significance of correlations between multiple and simultaneously recorded spike trains. Then, we describe several approaches to studying functional interactions between neurons, including principal component analysis, cross-correlation analysis, analyses of rate correlations, and analyses of shared predictive information. Finally, we compare these techniques using a sample data set and discuss how the combined use of these techniques can lead to novel insights regarding neuronal interactions during behavior. less

关键词： Principal Component Analysis MATLAB neural coding neural ensemble population activity correlated variability JPSTH multi-electrode recordings

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Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory

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NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第2期3卷 024007-024007页

作者： El Ferdaoussi, Ahmad Rouat, Jean Plourde, Eric Univ Sherbrooke Dept Elect & Comp Engn NECOTIS Sherbrooke PQ J1K 2R1 Canada

Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.

关键词： audio signal processing neural coding spike encoding spiking neural networks mutual information

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neural substrates of perception in the vestibular thalamus during natural self-motion: A review

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Current Research in Neurobiology 2023年 4卷 100073页

作者： Cullen, Kathleen E. Chacron, Maurice J. Department of Biomedical Engineering Johns Hopkins University Baltimore United States Department of Otolaryngology-Head and Neck Surgery Johns Hopkins University School of Medicine Baltimore United States Department of Neuroscience Johns Hopkins University School of Medicine Baltimore United States Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore United States Department of Physiology McGill University Montréal Canada

Accumulating evidence across multiple sensory modalities suggests that the thalamus does not simply relay information from the periphery to the cortex. Here we review recent findings showing that vestibular neurons within the ventral posteriolateral area of the thalamus perform nonlinear transformations on their afferent input that determine our subjective awareness of motion. Specifically, these neurons provide a substrate for previous psychophysical observations that perceptual discrimination thresholds are much better than predictions from Weber's law. This is because neural discrimination thresholds, which are determined from both variability and sensitivity, initially increase but then saturate with increasing stimulus amplitude, thereby matching the previously observed dependency of perceptual self-motion discrimination thresholds. Moreover, neural response dynamics give rise to unambiguous and optimized encoding of natural but not artificial stimuli. Finally, vestibular thalamic neurons selectively encode passively applied motion when occurring concurrently with voluntary (i.e., active) movements. Taken together, these results show that the vestibular thalamus plays an essential role towards generating motion perception as well as shaping our vestibular sense of agency that is not simply inherited from afferent input. © 2023 The Authors

关键词： Adaptation neural coding Optimal coding Perception Vestibular Voluntary movement

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Pluralistic Mechanism

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THEORIA-REVISTA DE TEORIA HISTORIA Y FUNDAMENTOS DE LA CIENCIA 2017年第2期32卷 161-175页

作者： Wajnerman Paz, Abel Univ Buenos Aires Fac Filosofia & Letras Inst Filosofia Dr Alejandro Korn Puan 4804to PisoOf 431 RA-1406 Buenos Aires DF Argentina

An argument recently proposed by Chirimuuta (2014) seems to motivate the rejection of the claims that every neurocognitive phenomenon can have a mechanistic explanation and that every neurocognitive explanation is mechanistic. In this paper, I focus on efficient coding models involving the so-called "canonical neural computations" and argue that although they imply some form of pluralism, they are compatible with two mechanistic generalizations: all neurocognitive explanations are (at least in part) mechanistic;and all neurocognitive phenomena that have an explanation have (at least) a purely mechanistic explanation.

关键词： mechanism pluralism efficient coding neural computation neural coding

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Constraints on the design of neuromorphic circuits set by the properties of neural population codes

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NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第1期3卷 012001-012001页

作者： Panzeri, Stefano Janotte, Ella Pequeno-Zurro, Alejandro Bonato, Jacopo Bartolozzi, Chiara Univ Med Ctr Hamburg Eppendorf UKE Dept Excellence Neural Informat Proc Hamburg Germany Ist Italiano Tecnol Ctr Human Technol CHT I-16153 Genoa Italy Ist Italiano Tecnol Ctr Robot & Intelligent Syst CRIS Genoa Italy Univ Bologna Dept Pharm & Biotechnol Bologna Italy

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the timescales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.

关键词： neural coding neuromorphic circuit design population coding

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Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems

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NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第3期3卷 034002-034002页

作者： Zendrikov, Dmitrii Solinas, Sergio Indiveri, Giacomo Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Univ Sassari Sassari Italy

Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorph

关键词： heterogeneity neural coding EI spiking networks neuromorphic hardware

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Motion processing across multiple topographic maps in the electrosensory system

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PHYSIOLOGICAL REPORTS 2014年第3期2卷 e00253-e00253页

作者： Khosravi-Hashemi, Navid Chacron, Maurice J. McGill Univ Dept Physiol 3655 Sir William OslerRoom 1137 Montreal PQ Canada McGill Univ Dept Phys Montreal PQ Canada

Animals can efficiently process sensory stimuli whose attributes vary over orders of magnitude by devoting specific neural pathways to process specific features in parallel. Weakly electric fish offer an attractive model system as electrosensory pyramidal neurons responding to amplitude modulations of their self-generated electric field are organized into three parallel maps of the body surface. While previous studies have shown that these fish use parallel pathways to process stationary stimuli, whether a similar strategy is used to process motion stimuli remains unknown to this day. We recorded from electrosensory pyramidal neurons in the weakly electric fish Apteronotus leptorhynchus across parallel maps of the body surface (centromedial, centrolateral, and lateral) in response to objects moving at velocities spanning the natural range. Contrary to previous observations made with stationary stimuli, we found that all cells responded in a similar fashion to moving objects. Indeed, all cells showed a stronger directionally nonselective response when the object moved at a larger velocity. In order to explain these results, we built a mathematical model incorporating the known antagonistic center-surround receptive field organization of these neurons. We found that this simple model could quantitatively account for our experimentally observed differences seen across E and I-type cells across all three maps. Our results thus provide strong evidence against the hypothesis that weakly electric fish use parallel neural pathways to process motion stimuli and we discuss their implications for sensory processing in general.

关键词： neural coding pyramidal neuron velocity tuning weakly electric fish

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Neuroengineering and neuroprosthetics

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Brain and neuroscience advances 2018年 2卷 2398212818817499页

作者： Kevin Warwick Coventry University Coventry UK.

This article contains a directed overview of the field of neuroengineering and neuroprosthetics. The aim of the article is, however, not to go over introductory material covered elsewhere, but rather to look ahead at exciting areas for likely future development. The BrainGate implant is focussed on in terms of its use as an interface between the Internet and the human nervous system. Sensory prosthetics of different types and deep brain stimulation are considered. Different possibilities with deep brain stimulation are also discussed.

关键词： BrainGate Implants deep brain stimulation human enhancement neural coding prosthetics

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Bayesian entropy estimation for countable discrete distributions

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2014年第1期15卷

作者： Kevin Murphy Bernhard Schölkopf Evan Archer Il Memming Park Jonathan W. Pillow Google Center for Perceptual Systems The University of Texas at Austin Austin TX and Max Planck Institute for Biological Cybernetics Tübingen Germany Center for Perceptual Systems The University of Texas at Austin Austin TX Department of Psychology Section of Neurobiology Division of Statistics and Scientific Computation and Center for Perceptual Systems The University of Texas at Austin Austin TX

We consider the problem of estimating Shannon's entropy H from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning. Here we show that it provides a natural family of priors for Bayesian entropy estimation, due to the fact that moments of the induced posterior distribution over H can be computed analytically. We derive formulas for the posterior mean (Bayes' least squares estimate) and variance under Dirichlet and Pitman-Yor process priors. Moreover, we show that a fixed Dirichlet or Pitman-Yor process prior implies a narrow prior distribution over H, meaning the prior strongly determines the entropy estimate in the under-sampled regime. We derive a family of continuous measures for mixing Pitman-Yor processes to produce an approximately flat prior over H. We show that the resulting "Pitman-Yor Mixture" (PYM) entropy estimator is consistent for a large class of distributions. Finally, we explore the theoretical properties of the resulting estimator, and show that it performs well both in simulation and in application to real data.

关键词： Bayesian estimation Bayesian nonparametrics Dirichlet process Pitman-Yor process entropy information theory neural coding

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