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The formation and use of hierarchical cognitive maps in the brain: A neural network model

NETWORK-COMPUTATION IN NEURAL SYSTEMS 2020年第1-4期31卷 37-141页

作者： Jordan, Henry O. C. Navarro, Daniel M. Stringer, Simon M. Univ Oxford Oxford Ctr Theoret Neurosci & Artificial Intellig Dept Expt Psychol New Radcliffe HouseWoodstock Rd Oxford OX2 6HG England

Many researchers have tried to model how environmental knowledge is learned by the brain and used in the form of cognitive maps. However, previous work was limited in various important ways: there was little consensus on how these cognitive maps were formed and represented, the planning mechanism was inherently limited to performing relatively simple tasks, and there was little consideration of how these mechanisms would scale up. This paper makes several significant advances. Firstly, the planning mechanism used by the majority of previous work propagates a decaying signal through the network to create a gradient that points towards the goal. However, this decaying signal limited the scale and complexity of tasks that can be solved in this manner. Here we propose several ways in which a network can can self-organize a novel planning mechanism that does not require decaying activity. We also extend this model with a hierarchical planning mechanism: a layer of cells that identify frequently-used sequences of actions and reuse them to significantly increase the efficiency of planning. We speculate that our results may explain the apparent ability of humans and animals to perform model-based planning on both small and large scales without a noticeable loss of efficiency.

关键词： Neural Network models motor control model-based behaviour neural development self-organization population coding

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Change-based inference for invariant discrimination

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2008年第3期19卷 236-252页

作者： Moazzezi, Reza Dayan, Peter Gatsby Computat Neurosci Unit London WC1N 3AR England

Under a conventional view of information processing in recurrently connected populations of neurons, computations consist in mapping inputs onto terminal attractor states of the dynamical interactions. However, there is evidence that substantial information representation and processing can occur over the course of the initial evolution of the dynamical states of such populations, a possibility that has attractive computational properties. Here, we suggest a model that explores one such property, namely, the invariance to an irrelevant feature dimension that arises from monitoring not the state of the population, but rather (a statistic of) the change in this state over time. We illustrate our proposal in the context of the bisection task, a paradigmatic example of perceptual learning for which an attractor-state recurrent model has previously been suggested. We show a change-based inference scheme that achieves near optimal performance in the task (with invariance to translation), is robust to high levels of dynamical noise and variations of the synaptic weight matrix, and indeed admits a computationally straightforward learning rule.

关键词： population coding network models perceptual learning

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A working memory model based on recurrent neural networks using reinforcement learning

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COGNITIVE NEURODYNAMICS 2024年第5期18卷 3031-3058页

作者： Wang, Mengyuan Wang, Yihong Xu, Xuying Pan, Xiaochuan East China Univ Sci & Technol Inst Cognit Neurodynam Ctr Intelligent Comp Sch Math 130 Meilong Rd Shanghai 200237 Peoples R China

Numerous electrophysiological experiments have reported that the prefrontal cortex (PFC) is involved in the process of working memory. PFC neurons continue firing to maintain stimulus information in the delay period without external stimuli in working memory tasks. Further findings indicate that while the activity of single neurons exhibits strong temporal and spatial dynamics (heterogeneity), the activity of population neurons can encode spatiotemporal information of stimuli stably and reliably. From the perspective of neural networks, the computational mechanism underlying this phenomenon is not well demonstrated. The main purpose of this paper is to adopt a new strategy to explore the neural computation mechanism of working memory. We used reinforcement learning to train a recurrent neural network model to learn a spatial working memory task. The model is composed of a decision network and a baseline network. The decision network is responsible for updating strategies to make action choices, while the baseline network evaluates action choices to predict rewards. Simulated results demonstrate that the model can perform the spatial working memory task. The activity of the recurrent units has characteristics such as temporal dynamics and preferred direction selectivity, but their population activity encodes the stimulus information stably during the delay period in a low-dimensional subspace. These activity characteristics displayed by the model units are similar to those of PFC neurons observed in the same experiments. Meanwhile, as the network model continued learning the task, the temporal stability and spatial separability of the stimulus information encoded by the activity of model units in the low-dimensional subspace gradually strengthened, and the accuracy of the network's action choices also increased. In summary, this network model provides a new simulation method for spatial working memory tasks and a new perspective for understanding the characteristics

关键词： Spatial working memory Prefrontal cortex Recurrent neural network Reinforcement learning population coding

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Diversity, heterogeneity and orientation-dependent variation of spike count correlation in the cat visual cortex

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EUROPEAN JOURNAL OF NEUROSCIENCE 2013年第11期38卷 3611-3627页

作者： Maruyama, Yoshiko Ito, Hiroyuki Kyoto Sangyo Univ Fac Comp Sci & Engn Kyoto 6038555 Japan

Cortical neurons are known to be noisy encoders of information, showing large response variabilities with repeated presentations of identical stimuli. These spike count variabilities are correlated over the cell population and their neuronal mechanism and functional significance have not been well understood. Recently there has been much debate over the magnitude of the population mean of the correlation, ranging from 0.1 to 0.2 down to nearly zero. We performed multi-neuron recordings on the cat visual cortex and found that the population mean did not necessarily represent the nature of correlated variabilities because the spike count correlation showed significant diversity and heterogeneity. Although the population mean was relatively small (0.06), the correlations of individual unit pairs were distributed over a broad range, extending to both positive and negative values. In most of the recording sessions of local cell populations (83%), significantly positive correlations coexisted with significantly negative ones in different unit pairs. Furthermore, nearly 20% of the unit pairs showed significant variation in the spike count correlation for different stimulus orientations. Correlation analysis between the spike count correlation and the firing activity of the unit pair suggested that the orientation tuning properties of the two quantities were unlikely to have originated from a common neuronal mechanism. Diversity, heterogeneity and context-dependent variation suggests that the correlated spike count variabilities originate not from fixed anatomical connections but rather from the dynamic interaction of neuronal networks.

关键词： noise correlation population coding trial variability

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Energy-efficient encoding by shifting spikes in neocortical neurons

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EUROPEAN JOURNAL OF NEUROSCIENCE 2013年第8期38卷 3181-3188页

作者： Malyshev, Aleksey Tchumatchenko, Tatjana Volgushev, Stanislav Volgushev, Maxim Univ Connecticut Dept Psychol Storrs CT 06269 USA RAS Inst Higher Nervous Act & Neurophysiol Moscow 117901 Russia Max Planck Inst Brain Res Frankfurt Germany Ruhr Univ Bochum Fac Math Bochum Germany Univ Illinois Dept Stat Urbana IL 61801 USA

The speed of computations in neocortical networks critically depends on the ability of populations of spiking neurons to rapidly detect subtle changes in the input and translate them into firing rate changes. However, high sensitivity to perturbations may lead to explosion of noise and increased energy consumption. Can neuronal networks reconcile the requirements for high sensitivity, operation in a low-noise regime, and constrained energy consumption? Using intracellular recordings in slices from the rat visual cortex, we show that layer 2/3 pyramidal neurons are highly sensitive to minor input perturbations. They can change their population firing rate in response to small artificial excitatory postsynaptic currents (aEPSCs) immersed in fluctuating noise very quickly, within 2-2.5ms. These quick responses were mediated by the generation of new, additional action potentials (APs), but also by shifting spikes into the response peak. In that latter case, the spike count increase during the peak and the decrease after the peak cancelled each other, thus producing quick responses without increases in total spike count and associated energy costs. The contribution of spikes from one or the other source depended on the aEPSCs timing relative to the waves of depolarization produced by ongoing activity. Neurons responded by shifting spikes to aEPSCs arriving at the beginning of a depolarization wave, but generated additional spikes in response to aEPSCs arriving towards the end of a wave. We conclude that neuronal networks can combine high sensitivity to perturbations and operation in a low-noise regime. Moreover, certain patterns of ongoing activity favor this combination and energy-efficient computations.

关键词： population coding rat neocortex response speed signal detection time slices

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Relating cluster and population responses to natural sounds and tonal stimuli in cat primary auditory cortex

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HEARING RESEARCH 2001年第1-2期152卷 110-127页

作者： Rotman, Y Bar-Yosef, O Nelken, I Hebrew Univ Jerusalem Hadassah Med Sch Dept Physiol IL-91120 Jerusalem Israel Hebrew Univ Jerusalem Interdisciplinary Ctr Neural Computat IL-91120 Jerusalem Israel

Most information about neuronal properties in primary auditory cortex (AI) has been gathered using simple artificial sounds such as purl tones and broad-band noise. These sounds are very different from the natural sounds that are processed by the auditory system in real world situations. In an attempt to bridge this gap, simple tonal stimuli and a standard set of six natural sounds were used to create models relating the responses of neuronal clusters in AI of barbiturate-anesthetized cats to the two classes of stimuli. A significant correlation was often found between the response to the separate frequency components of the natural sounds and the response to the natural sound itself. At the population level, this correlation resulted in a rate profile that represented robustly the spectral profiles of the natural sounds. There was however a significant scatter in the responses to the natural sound around the predictions based on the responses to tonal stimuli, Going the other way, in order to understand better the non-linearities in the responses to natural sounds, responses of neuronal clusters were characterized using second order Volterra kernel analysis of their responses to natural sounds. This characterization predicted reasonably well the amplitude of the response to other natural sounds, but could not reproduce the responses to tonal stimuli. Thus, second order non-linear characterizations, at least those using the Volterra kernel model, do not interpolate well between responses to tones and to natural sounds in auditory cortex. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： cat primary auditory cortex natural sound population coding rate profile Volterra kernel

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Design of multielectrode arrays for uniform sampling of different orientations of tuned unit populations in the cat visual cortex

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NEUROSCIENCE RESEARCH 2017年第0期122卷 51-63页

作者： Maruyama, Yoshiko Ito, Hiroyuki Kyoto Sangyo Univ Fac Comp Sci & Engn Kyoto 6038555 Japan Hakodate Coll Natl Inst Technol Dept Prod Syst Engn Hakodate Hokkaido 0428501 Japan

For better reconstruction of stimulus orientation from a single trial activity of the neuron population in the visual cortex, we need uniform samplings of differently oriented tuned neurons. We recorded multiple neurons simultaneously by using either a four-tetrode array or an eight-microelectrode array, and examined what kinds of electrodes and layouts provided a more homogeneous distribution of the units' optimal orientations. The unit population sampled by a four-tetrode array showed more homogeneous distribution than those sampled by an eight-microelectrode array. We confirmed this property by simulated recording sessions based on the optical imaging data of the orientation map. (C) 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

关键词： population coding Circular variance Tetrode Orientation map Electrode array Multineuron recording Brain-machine interface

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A robust and compact population code for competing sounds in auditory cortex

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JOURNAL OF NEUROPHYSIOLOGY 2023年第3期130卷 775-787页

作者： Nocon, Jian Carlo Witter, Jake Gritton, Howard Han, Xue Houghton, Conor Sen, Kamal Boston Univ Neurophoton Ctr Boston MA 02215 USA Boston Univ Ctr Syst Neurosci Boston MA 02215 USA Boston Univ Hearing Res Ctr Boston MA 02215 USA Boston Univ Dept Biomed Engn Boston MA 02215 USA Univ Bristol Dept Comp Sci Bristol England Univ Illinois Dept Comparat Biosci Urbana IL USA Univ Illinois Dept Bioengn Urbana IL USA

Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single-neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex code competing sounds have not been previously investigated. Here, we apply a novel information-theoretic approach to estimate information in populations of neurons in mouse auditory cortex about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the labeled line code outperforming the summed population code and approaching information levels attained in the absence of competing stimuli. Finally, information in the labeled line code increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in humans and animals. Taken together, our results reveal that a compact population of neurons in auditory cortex provides a robust code for competing sounds from different spatial locations.

关键词： auditory cortex complex scene analysis information theory neural coding population coding

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Integration across directions in dynamic random dot displays: Vector summation or winner take all?

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VISION RESEARCH 1996年第15期36卷 2321-2331页

作者： Zohary, E Scase, MO Braddick, OJ UCL DEPT PSYCHOL LONDON ENGLAND

Recent studies have clearly demonstrated that the activity of directionally selective neuronal populations in the middle temporal (MT) and medial superior temporal (MST) cortical areas plays a direct role in the judgment of the direction of visual motion, However, the way in which the information is derived from a population of neurons remains unknown. Two principal models have been suggested in the past: the vector summation model suggests that the responses of neurons encoding all directions of motion are weighted and pooled to obtain an accurate estimate of the mean direction of motion;the winner-take-all model is based on a competition between different direction-specific channels, so that decisions are cast in favor of the channel generating the strongest directional signal. To discriminate between these two models we generated random dot stimuli that contained an asymmetric distribution of directions of motion. Human subjects were asked to adjust the global direction of motion to the upward vertical direction. When the directional signals were of similar strength, subjects tended to perceive global motion in the mean direction of motion (corresponding to vector summation), but as one directional signal became more prominent, most subjects' settings diverged from the mean towards the modal direction of motion. Some subjects could either match the mean or the modal direction of motion in the display, depending on the task instructions. These results suggest that the perceptual judgment of direction of motion is not based on any rigid algorithm generating a single valued output. Rather, human observers are able to judge different aspects of the distribution of activity in a cortical area depending on the task requirements. Copyright (C) 1996 Elsevier Science Ltd.

关键词： motion perception global motion direction perception population coding

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Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics

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NEURON 2018年第5期98卷 1005-+页

作者： Remington, Evan D. Narain, Devika Hosseini, Eghbal A. Jazayeri, Mehrdad MIT McGovern Inst Brain Res 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci E25-618 Cambridge MA 02139 USA Netherlands Inst Neurosci Amsterdam Netherlands Erasmus MC Rotterdam Netherlands

Neural mechanisms that support flexible sensorimotor computations are not well understood. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly reconfigured by controlling the system's inputs and initial conditions. To investigate whether the brain employs such control mechanisms, we recorded from the dorsomedial frontal cortex of monkeys trained to measure and produce time intervals in two sensorimotor contexts. The geometry of neural trajectories during the production epoch was consistent with a mechanism wherein the measured interval and sensorimotor context exerted control over cortical dynamics by adjusting the system's initial condition and input, respectively. These adjustments, in turn, set the speed at which activity evolved in the production epoch, allowing the animal to flexibly produce different time intervals. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.

关键词： Dynamical Systems cognitive flexibility electrophysiology frontal cortex motor planning population coding recurrent neural networks sensorimotor coordination timing

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