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Cortical visual prostheses: from microstimulation to functional percept

JOURNAL OF NEURAL ENGINEERING 2018年第2期15卷 021005-021005页

作者： Foroushani, Armin Najarpour Pack, Christopher C. Sawan, Mohamad Polytech Montreal Inst Biomed Engn PolyStim Neurotechnol Lab Montreal PQ Canada McGill Univ Montreal Neurol Inst Montreal PQ H3A 2B4 Canada

Cortical visual prostheses are intended to restore vision by targeted electrical stimulation of the visual cortex. The perception of spots of light, called phosphenes, resulting from microstimulation of the visual pathway, suggests the possibility of creating meaningful percept made of phosphenes. However, to date electrical stimulation of V1 has still not resulted in perception of phosphenated images that goes beyond punctate spots of light. In this review, we summarize the clinical and experimental progress that has been made in generating phosphenes and modulating their associated perceptual characteristics in human and macaque primary visual cortex (V1). We focus specifically on the effects of different microstimulation parameters on perception and we analyse key challenges facing the generation of meaningful artificial percepts. Finally, we propose solutions to these challenges based on the application of supervised learning of population codes for spatial stimulation of visual cortex.

关键词： phosphene visual prosthesis visual cortex electrical microstimulation population coding

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From neuronal populations to behavior: a computational journey

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2014年第AUG期8卷 81页

作者： Graf, Arnulf B. A. CALTECH Div Biol & Biol Engn Pasadena CA 91125 USA

Using a different type of network based on the popular Integrate and Fire neurons, Nicola and Campbell (2013) analytically derived mean-field models that they evaluated in numerical simulations using typical parameter values for hippocampal neurons. In that study the authors recorded from the visual cortex in the mouse to predict visual stimuli based on the neuronal population response. Conflict of Interest Statement The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

关键词： neuronal populations population coding networks of neurons decoding sensory input behavior

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Neural representations that support invariant object recognition

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2009年第2009期3卷 3-3页

作者： Goris, Robbe L. T. Op de Beeck, Hans P. Univ Louvain Expt Psychol Lab Tiensestr 102 B-3000 Louvain Belgium

Neural mechanisms underlying invariant behaviour such as object recognition are not well understood. For brain regions critical for object recognition, such as inferior temporal cortex (ITC), there is now ample evidence indicating that single cells code for many stimulus aspects, implying that only a moderate degree of invariance is present. However, recent theoretical and empirical work seems to suggest that integrating responses of multiple non-invariant units may produce invariant representations at population level. We provide an explicit test for the hypothesis that a linear read-out mechanism of a pool of units resembling ITC neurons may achieve invariant performance in an identification task. A linear classifier was trained to decode a particular value in a 2-D stimulus space using as input the response pattern across a population of units. Only one dimension was relevant for the task, and the stimulus location on the irrelevant dimension (ID) was kept constant during training. In a series of identification tests, the stimulus location on the relevant dimension (RD) and ID was manipulated, yielding estimates for both the level of sensitivity and tolerance reached by the network. We studied the effects of several single-cell characteristics as well as population characteristics typically considered in the literature, but found little support for the hypothesis. While the classifier averages out effects of idiosyncratic tuning properties and inter-unit variability, its invariance is very much determined by the (hypothetical) 'average' neuron. Consequently, even at population level there exists a fundamental trade-off between selectivity and tolerance, and invariant behaviour does not emerge spontaneously.

关键词： object recognition inferior temporal cortex population coding multidimensional tuning

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FROM SENSORS TO SPIKES: EVOLVING RECEPTIVE FIELDS TO ENHANCE SENSORIMOTOR INFORMATION IN A ROBOT-ARM

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2012年第4期22卷 1250013-1250013页

作者： Luque, Niceto R. Garrido, Jesus A. Ralli, Jarno Laredo, Juanlu J. Ros, Eduardo Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture;i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.

关键词： Receptive field evolutionary algorithm parallelism population coding cuneate nucleus spiking neural network robot

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CODE-SPECIFIC LEARNING RULES IMPROVE ACTION SELECTION BY populationS OF SPIKING NEURONS

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2014年第5期24卷 1450002-1450002页

作者： Friedrich, Johannes Urbanczik, Robert Senn, Walter Univ Bern Inst Physiol CH-3012 Bern Switzerland Univ Cambridge Dept Engn Computat & Biol Learning Lab Cambridge CB2 1PZ England Univ Bern Ctr Cognit Learning & Memory CH-3012 Bern Switzerland

population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight-or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

关键词： Spiking neural network population coding reinforcement learning decision making action perturbation spike-timing-dependent synaptic plasticity code specificity policy gradient

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Clustering of Neural Activity: A Design Principle for population Codes

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2020年 14卷 20页

作者： Berry, Michael J., II Tkacik, Gasper Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA IST Austria Klosterneuburg Austria

We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons;(ii) clusters encode qualitatively different visual features than their constituent neurons;and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a "learnable" neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.

关键词： population coding maximum entropy information theory correlations clusters error correction unsupervised learning criticality

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Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2010年第2010期4卷 130-130页

作者： Yaeli, Steve Meir, Ron Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel Technion Israel Inst Technol Lab Network Biol Res IL-32000 Haifa Israel

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.

关键词： tuning functions neural encoding population coding Bayesian decoding optimal width Fisher information

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Learning-induced biases in the ongoing dynamics of sensory representations predict stimulus generalization

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CELL REPORTS 2022年第6期38卷 110340-110340页

作者： Aschauer, Dominik F. Eppler, Jens-Bastian Ewig, Luke Chambers, Anna R. Pokorny, Christoph Kaschube, Matthias Rumpel, Simon Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Physiol Focus Program Translat Neurosci Mainz Germany Goethe Univ Frankfurt Frankfurt Inst Adv Studies Frankfurt Germany Goethe Univ Frankfurt Inst Comp Sci Frankfurt Germany Graz Univ Technol Inst Theoret Comp Sci Graz Austria Univ Oslo Fac Med Inst Basic Med Sci Oslo Norway Ecole Polytech Fed Lausanne EPFL Blue Brain Project Connect Geneva Switzerland

Sensory stimuli have long been thought to be represented in the brain as activity patterns of specific neuronal assemblies. However, we still know relatively little about the long-term dynamics of sensory representations. Using chronic in vivo calcium imaging in the mouse auditory cortex, we find that sensory representations undergo continuous recombination, even under behaviorally stable conditions. Auditory cued fear conditioning introduces a bias into these ongoing dynamics, resulting in a long-lasting increase in the number of stimuli activating the same subset of neurons. This plasticity is specific for stimuli sharing representational similarity to the conditioned sound prior to conditioning and predicts behaviorally observed stimulus generalization. Our findings demonstrate that learning-induced plasticity leading to a representational linkage between the conditioned stimulus and non-conditioned stimuli weaves into ongoing dynamics of the brain rather than acting on an otherwise static substrate.

关键词： mouse auditory cortex population coding perception chronic calcium imaging response transitions associations auditory-cued fear conditioning representational drift

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Functional network properties of the auditory cortex

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HEARING RESEARCH 2023年第1期433卷 108768页

作者： Lestang, Jean-Hugues Cai, Huaizhen Averbeck, Bruno B. Cohen, Yale E. Univ Penn Dept Otorhinolaryngol Philadelphia PA 19104 USA Natl Inst Mental Hlth NIH Lab Neuropsychol Bethesda MD 20892 USA Univ Penn Neurosci Philadelphia PA 19104 USA Univ Penn Bioengn Philadelphia PA 19104 USA Natl Inst Mental Hlth NIH Lab Neuropsychol Bldg 49 Room 1B8049 Convent Dr MSC 4415 Bethesda MD 20892 USA

The auditory system transforms auditory stimuli from the external environment into perceptual auditory objects. Recent studies have focused on the contribution of the auditory cortex to this transformation. Other studies have yielded important insights into the contributions of neural activity in the auditory cortex to cognition and decision-making. However, despite this important work, the relationship between auditory-cortex activity and behavior/perception has not been fully elucidated. Two of the more important gaps in our understanding are (1) the specific and differential contributions of different fields of the auditory cortex to auditory perception and behavior and (2) the way networks of auditory neurons impact and facilitate auditory information processing. Here, we focus on recent work from non-human primate models of hearing and review work related to these gaps and put forth challenges to further our understanding of how single-unit activity and network activity in different cortical fields contribution to behavior and perception.& COPY;2023 Published by Elsevier B.V.

关键词： Auditory cortex Primate Network population coding Decision making

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Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues

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NEURON 2022年第11期110卷 1843-+页

作者： Waaga, Torgeir Agmon, Haggai Normand, Valentin A. Nagelhus, Anne Gardner, Richard J. Moser, May-Britt Moser, Edvard, I Burak, Yoram Norwegian Univ Sci & Technol Kavli Inst Syst Neurosci Trondheim Norway Norwegian Univ Sci & Technol Ctr Neural Computat Trondheim Norway Hebrew Univ Jerusalem Edmond & Lily Safra Ctr Brain Sci Jerusalem Israel Hebrew Univ Jerusalem Racah Inst Phys Jerusalem Israel

The representation of an animal's position in the medial entorhinal cortex (MEC) is distributed across several modules of grid cells, each characterized by a distinct spatial scale. The population activity within each module is tightly coordinated and preserved across environments and behavioral states. Little is known, however, about the coordination of activity patterns across modules. We analyzed the joint activity patterns of hundreds of grid cells simultaneously recorded in animals that were foraging either in the light, when sensory cues could stabilize the representation, or in darkness, when such stabilization was disrupted. We found that the states of different modules are tightly coordinated, even in darkness, when the internal representation of position within the MEC deviates substantially from the true position of the animal. These findings suggest that internal brain mechanisms dynamically coordinate the representation of position in different modules, ensuring that they jointly encode a coherent and smooth trajectory.

关键词： neural coding neural decoding grid cells entorhinal cortex spatial memory spatial coding population coding neural circuits

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