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Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2017年第2期114卷 394-399页

作者： Murray, John D. Bernacchia, Alberto Roy, Nicholas A. Constantinidis, Christos Romo, Ranulfo Wang, Xiao-Jing Yale Univ Sch Med Dept Psychiat New Haven CT 06510 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA Wake Forest Univ Sch Med Dept Neurobiol & Anat Winston Salem NC 27157 USA Univ Nacl Autonoma Mexico Inst Fisiol Celular Neurociencias Mexico City 04510 DF Mexico El Colegio Nacl Mexico City 06020 DF Mexico NYU Ctr Neural Sci New York NY 10012 USA NYU East China Normal Univ Inst Brain & Cognit Sci Shanghai 200122 Peoples R China

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

关键词： working memory prefrontal cortex population coding

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The effect of neural adaptation on population coding accuracy

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2012年第3期32卷 387-402页

作者： Cortes, Jesus M. Marinazzo, Daniele Series, Peggy Oram, Mike W. Sejnowski, Terry J. van Rossum, Mark C. W. Univ Edinburgh Inst Adapt & Neural Computat Sch Informat Edinburgh Midlothian Scotland Univ Paris 05 Lab Neurophys & Physiol CNRS UMR 8119 Paris France Univ St Andrews Sch Psychol St Andrews Fife Scotland Salk Inst Howard Hughes Med Inst San Diego CA 92037 USA Univ Calif San Diego Div Biol Sci San Diego CA 92093 USA

Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.

关键词： Visual adaptation Primary visual cortex population coding Fisher Information Cortical circuit Computational model Short-term synaptic depression Spike-frequency adaptation

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DYNAMICS OF population coding FOR OBJECT VIEWS FOLLOWING OBJECT DISCRIMINATION TRAINING

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NEUROSCIENCE 2016年第0期330卷 109-120页

作者： Yamaguchi, Reona Okamura, Jun-ya Wang, Gang Kagoshima Univ Grad Sch Sci & Engn Dept Informat Sci & Biomed Engn Kagoshima 8900065 Japan Weifang Med Univ Lab Cognit Neurosci Weifang Shandong Peoples R China

We have previously demonstrated that inferotemporal neurons respond to objects viewed from a range of angles, even without any prior experience in learning the associations among the views. Several models have been proposed to explain object recognition across disparate views. However, direct neuronal evidence is rare. In the present study, we focused on the response similarity of a population of inferotemporal cells to object views, following different prior experiences. Two monkeys were subjected to a task in which object discrimination across views was required. We found significantly higher neural response similarity to 30 degrees separated views, 190 ms after object image presentation, than without any prior discrimination experience across views. The time period over which the similarity was significant began and endured similarly for 60 degrees separated views at 190-850 ms. For 90 degrees separated views, the time period over which the similarity was significant was shorter and started later, at 230-550 ms. The results demonstrate the dynamics of cell population activity and suggest a possible explanation for object recognition across disparate views. (C) 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： monkey inferotemporal cortex object recognition view invariance population coding

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Single and population coding of taste in the gustatory cortex of awake mice

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JOURNAL OF NEUROPHYSIOLOGY 2019年第4期122卷 1342-1356页

作者： Levitan, David Lin, Jian-You Wachutka, Joseph Mukherjee, Narendra Nelson, Sacha B. Katz, Donald B. Brandeis Univ Dept Psychol MS 062415 South St Waltham MA 02454 USA Brandeis Univ Dept Biol Waltham MA 02254 USA Brandeis Univ Volen Ctr Complex Syst Waltham MA USA

Electrophysiological analysis has revealed much about the broad coding and neural ensemble dynamics that characterize gustatory cortical (GC) taste processing in awake rats and about how these dynamics relate to behavior. With regard to mice, however, data concerning cortical taste coding have largely been restricted to imaging, a technique that reveals average levels of neural responsiveness but that (currently) lacks the temporal sensitivity necessary for evaluation of fast response dynamics;furthermore, the few extant studies have thus far failed to provide consensus on basic features of coding. We have recorded the spiking activity of ensembles of GC neurons while presenting representatives of the basic taste modalities (sweet, salty, sour, and bitter) to awake mice. Our first central result is the identification of similarities between rat and mouse taste processing: most mouse GC neurons (similar to 66%) responded distinctly to multiple (3-4) tastes;temporal coding analyses further reveal, for the first time, that single mouse GC neurons sequentially code taste identity and palatability, the latter responses emerging similar to 0.5 s after the former, with whole GC ensembles transitioning suddenly and coherently from coding taste identity to coding taste palatability. The second finding is that spatial location plays very little role in any aspect of taste responses: neither between(anterior-posterior) nor within-mouse (dorsal-ventral) mapping revealed anatomic regions with narrow or temporally simple taste responses. These data confirm recent results showing that mouse cortical taste responses are not "gustotopic" but also go beyond these imaging results to show that mice process tastes through time. NEW & NOTEWORTHY Here, we analyzed taste-related spiking activity in awake mouse gustatory cortical (GC) neural ensembles, revealing deep similarities between mouse cortical taste processing and that repeatedly demonstrated in rat: mouse GC ensembles code multipl

关键词： gustatory cortex mice population coding taste

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Compression for Quantum population coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2018年第7期64卷 4766-4783页

作者： Yang, Yuxiang Bai, Ge Chiribella, Giulio Hayashi, Masahito Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China HKU Shenzhen Inst Res & Innovat Shenzhen 518057 Peoples R China Univ Oxford Dept Comp Sci Oxford OX1 2JD England Canadian Inst Adv Res Toronto ON M5G 1M1 Canada Nagoya Univ Grad Sch Math Nagoya Aichi 4648602 Japan Natl Univ Singapore Ctr Quantum Technol Singapore 117542 Singapore

We study the compression of n quantum systems, each prepared in the same state belonging to a given parametric family of quantum states. For a family of states with f independent parameters, we devise an asymptotically faithful protocol that requires a hybrid memory of size (f/2) log n, including both quantum and classical bits. Our construction uses a quantum version of local asymptotic normality and, as an intermediate step, solves the problem of compressing displaced thermal states of n identically prepared modes. In both cases, we show that (f/2) log n is the minimum amount of memory needed to achieve asymptotic faithfulness. In addition, we analyze how much of the memory needs to be quantum. We find that the ratio between quantum and classical bits can be made arbitrarily small, but cannot reach zero: unless all the quantum states in the family commute, no protocol using only classical bits can be faithful, even if it uses an arbitrarily large number of classical bits.

关键词： population coding compression quantum system local asymptotic normality identically prepared state

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Compression for quantum population coding

Compression for quantum population coding

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IEEE International Symposium on Information Theory (ISIT)

作者： Yang, Yuxiang Bai, Ge Chiribella, Giulio Hayashi, Masahito Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Canadian Inst Adv Res CIFAR Program Quantum Informat Sci Toronto ON Canada Nagoya Univ Grad Sch Math Nagoya Aichi Japan Natl Univ Singapore Ctr Quantum Technol Singapore Singapore

ISBN: (纸本)9781509040964

We study the compression of arbitrary parametric families of n identically prepared finite-dimensional quantum states, in a setting that can be regarded as a quantum analogue of population coding. For a family with f free parameters, we propose an asymptotically faithful protocol that requires a memory of overall size (f/2) log n. Our construction uses a quantum version of local asymptotic normality and, as an intermediate step, solves the problem of the optimal compression of n identically prepared displaced thermal states. Our protocol achieves the ultimate bound predicted by quantum Shannon theory. In addition, we explore the minimum requirement for quantum memory: On the one hand, the amount of quantum memory used by our protocol can be made arbitrarily small compared to the overall memory cost;on the other hand, any protocol using only classical memory cannot be faithful.

关键词： population coding compression quantum system local asymptotic normality identically prepared states

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COMBINATION OF SELF ORGANIZING MAPS AND FEEDFORWARD NETS INSPIRED BY population coding

COMBINATION OF SELF ORGANIZING MAPS AND FEEDFORWARD NETS INS...

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World Automation Congress 2008

作者： Harneit, Steffen Cutec-Institute GMBH Germany

ISBN: (纸本)9781889335384

Self-organizing feature maps and feedforward nets trained with the backpropagation algorithm are the most common used and the most analyzed artificial neural nets. Although a combination of them, called Counterpropagation, already bad been proposed, its full capabilities had not been recognized. In this paper an extension of this net structure is proposed, which takes into account the whole activity pattern of the self-organizing feature map's competitive layer. It is inspired by both neurological studies about information coding in the human brain and the psychological thesis of H. Benesch, who states that each information processing system is based on the triple of medium, pattern and meaning. A practical example using situations in robot soccer games is given and the results are analyzed.

关键词： Self Organizing Map Feedforward Net Backpropagation Learning Robot Soccer Computing with Activities population coding

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A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

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Natural Computing 2004年第2期3卷 177-193页

作者： Volkmer, Markus Technical University Hamburg-Harburg Dept. of Computer Engineering VI D-21073 Hamburg Germany

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. © 2004 Kluwer Academic Publishers.

关键词： Auditory cortex model Overcomplete dictionary population coding Pulsed neural networks Sparse neural code spectro-temporal receptive fields time-frequency analysis

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population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2014年第JUN期8卷 58页

作者： Montijn, Jorrit S. Vinck, Martin Pennartz, Cyriel M. A. Univ Amsterdam Fac Sci Ctr Neurosci Swammerdam Inst Life Sci NL-1098 XH Amsterdam Noord Holland Netherlands Univ Amsterdam Res Prior Program Brain & Cognit NL-1098 XH Amsterdam Noord Holland Netherlands

The primary visual cortex is an excellent model system for investigating how neuronal populations encode information, because of well-documented relationships between stimulus characteristics and neuronal activation patterns. We used two-photon calcium imaging data to relate the performance of different methods for studying population coding (population vectors, template matching, and Bayesian decoding algorithms) to their underlying assumptions. We show that the variability of neuronal responses may hamper the decoding of population activity, and that a normalization to correct for this variability may be of critical importance for correct decoding of population activity. Second, by comparing noise correlations and stimulus tuning we find that these properties have dissociated anatomical correlates, even though noise correlations have been previously hypothesized to reflect common synaptic input. We hypothesize that noise correlations arise from large non-specific increases in spiking activity acting on many weak synapses simultaneously, while neuronal stimulus response properties are dependent on more reliable connections. Finally, this paper provides practical guidelines for further research on population coding and shows that population coding cannot be approximated by a simple summation of inputs, but is heavily influenced by factors such as input reliability and noise correlation structure.

关键词： population coding calcium imaging spatial organization mouse visual cortex orientation tuning noise correlations signal correlations

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Discrete Belief Propagation Network Using population coding and Factor Graph for Kinematic Control of a Mobile Robot

Discrete Belief Propagation Network Using Population Coding ...

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IEEE International Conference on Computational Intelligence and Cybernetics (IEEE CyberneticsCom)

作者： Sugiarto, Indar Conradt, Joerg Tech Univ Munich Inst Automat Control Engn D-80290 Munich Germany

ISBN: (纸本)9781467360531

This paper presents a probabilistic graphical model in the form of a factor graph to perform hierarchical probabilistic inference by computing kinematics of an omnidirectional mobile robot. We propose applying population coding principles to encode messages transmitted within the factor graph to update the network's internal belief, as inspired by neuronal information processing. We examine two inference scenarios in this paper: first for single wheel motor control using real data from an omnidirectional mobile robot;and second for the robot's velocity and orientation in real-world coordinates using simulation data. The experimental results for the first scenario show that the factor graph can learn input-output relations almost perfectly and the simulation results for the second scenario demonstrate that the selected model in the factor graph is quite robust against disturbances due to noise during inference. The results of this study can be applied in more complex intelligence tasks, which build on top of this basic kinematics system.

关键词： discrete belief propagatin factor graph population coding kinematics mobile robot

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