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Redundancy and synergy arising from pairwise correlations in neuronal ensembles

JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2002年第3期12卷 165-174页

作者： Bezzi, M Diamond, ME Treves, A SISSA Program Neurosci I-34014 Trieste Italy

Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by measures of the information conveyed by neuronal activity;a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et al., 1999). Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise components of neuronal firing are small in absolute value and entirely random in origin. Even such small random correlations are shown to lead to large possible synergy or redundancy, whenever the time window for extracting information from neuronal firing extends to the order of the mean interspike interval. In addition, a sample of recordings from rat barrel cortex illustrates the mean time window at which such "corrections" dominate when correlations are, as often in the real brain, neither random nor small. The presence of this kind of correlations for a large ensemble of cells restricts further the time of validity of the expansion.

关键词： mutual information population coding short-time expansion temporal coding somatosensory system barrel cortex

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Modular design of a factor-graph-based inference engine on a System-On-Chip (SoC)

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MICROPROCESSORS AND MICROSYSTEMS 2018年 60卷 53-64页

作者： Sugiarto, Indar Conradt, Joerg Petra Christian Univ Dept Elect Engn Surabaya Indonesia Univ Manchester Sch Comp Sci Manchester Lancs England Tech Univ Munich Neurosci Syst Theory Munich Germany

Factor graphs are probabilistic graphical frameworks for modeling complex and dynamic systems. They can be used in a broad range of application domains, from machine learning and robotics, to signal processing and digital communications. One important aspect that makes a factor graph very useful and very promising to be applied widely is its inference mechanism that is suitable for performing a complex model-based reasoning. However, its features have not fully explored and factor graphs are still used mainly as modeling tools that run on standard computers. Whereas in real applications such as robotics, one needs a practical implementation of such a framework. In this paper, we describe the development of a factor-graph-based inference engine that runs on a System-on-Chip (SoC). Running natively on a low level hardware, our factor graph engine delivers highest performance for real-time applications. We designed the embedded architecture so that it conveys important aspects such as modularity, scalability, flexibility and platform-friendly framework. The proposed architecture has customizable levels of parallelism as well as re-configurable modules that are extensible to accommodate large networks. We optimized the design to achieve high efficiency in terms of clock latency and resources consumption. We have tested our design on Xilinx Zynq-7000 SoCs and the implementation result demonstrates that the proposed framework can potentially be extended into a massively distributed probabilistic computing engine.

关键词： Discrete factor graph population coding System-on-Chip Re-configurable computing

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Bio-Inspired Small Target Motion Detection With Spatio-Temporal Feedback in Natural Scenes

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 451-465页

作者： Wang, Hongxin Zhong, Zhiyan Lei, Fang Peng, Jigen Yue, Shigang Guangzhou Univ Machine Life & Intelligence Res Ctr Sch Math & Informat Sci Guangzhou 510006 Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou 510665 Peoples R China Guangdong Univ Petrochem Technol Sch Elect & Informat Engn Maoming 525000 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects' excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments.

关键词： Visualization Visual systems Motion detection Neurons Insects Feature extraction Dynamics Biologically inspired visual system small target motion detection natural scenes spatio-temporal feedback population coding

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Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance

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PHYSICS LETTERS A 2006年第3期352卷 183-189页

作者： McDonnell, MD Stocks, NG Pearce, CEM Abbott, D Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia Univ Adelaide CBME Adelaide SA 5005 Australia Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Univ Adelaide Sch Math Sci Adelaide SA 5005 Australia

We examine the optimal threshold distribution in populations of noisy threshold devices. When the noise on each threshold is independent, and sufficiently large. the optimal thresholds are realized by the suprathreshold stochastic resonance effect, in which case all threshold devices are identical. This result has relevance for neural population coding, as such noisy threshold devices model the key dynamics of nerve fibres. It is also relevant to quantization and lossy source coding theory, since the model provides a form of stochastic signal quantization. Furthermore, it is shown that a bifurcation pattern appears in the optimal threshold distribution as the noise intensity increases. Fisher information is used to demonstrate that the optimal threshold distribution remains in the suprathreshold stochastic resonance configuration as the population size approaches infinity. (c) 2005 Elsevier B.V. All rights reserved.

关键词： information theory neural coding suprathreshold stochastic resonance quantization optimal quantization population coding bifurcations point density function

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Early cortical orientation selectivity: How fast inhibition decodes the order of spike latencies

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2003年第3期15卷 357-365页

作者： Delorme, A Salk Inst Biol Studies CNL La Jolla CA 92037 USA

Following a flashed stimulus, I show that a simple neurophysiological mechanism in the primary visual system can generate orientation selectivity based on the first incoming spikes. A biological model of the lateral geniculate nucleus generates an asynchronous wave of spikes, with the most strongly activated neurons firing first. Geniculate activation leads to both the direct excitation of a cortical pyramidal cell and disynaptic feed-forward inhibition. The mechanism provides automatic gain control, so the cortical neurons respond over a wide range of stimulus contrasts. It also demonstrates the biological plausibility of a new computationally efficient neural code: latency rank order coding.

关键词： shunting inhibition visual cortex rank order coding population coding contrast invariance single spike

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Learning navigational maps through potentiation and modulation of hippocampal place cells

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 1997年第1期4卷 79-94页

作者： Gerstner, W Abbott, LF BRANDEIS UNIV VOLEN CTR COMPLEX SYST WALTHAM MA 02254 USA

We analyze a model of navigational map formation based on correlation-based, temporally asymmetric potentiation and depression of synapses between hippocampal place cells. We show that synaptic modification during random exploration of an environment shifts the location encoded by place cell activity in such a way that it indicates the direction from any location to a fixed target avoiding walls and other obstacles. Multiple maps to different targets can be simultaneously stored if we introduce target-dependent modulation of place cell activity. Once maps to a number of target locations in a given environment have been stored, novel maps to previously unknown target locations are automatically constructed by interpolation between existing maps.

关键词： maps hippocampus synaptic plasticity population coding

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The dual-route hypothesis: evaluating a neurocomputational model of fear conditioning in rats

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CONNECTION SCIENCE 2009年第1期21卷 15-37页

作者： Lowe, Robert Humphries, Mark Ziemke, Tom Univ Skovde Sch Humanities & Informat Skovde Sweden Univ Sheffield Dept Psychol Sheffield S10 2TN S Yorkshire England

Research on the neural bases of emotion raises much controversy and few quantitative models exist that can help address the issues raised. Here we replicate and dissect one of those models, Armony and colleagues' neurocomputational model of fear conditioning, which is based on LeDoux's dual-route hypothesis regarding the rat fear circuitry. The importance of the model's modular abstraction of the neuroanatomy, its use of population coding, and in particular the interplay between thalamo-amygdala and thalamo-cortical pathways are tested. We show that a trivially minimal version of the model can produce conditioning to a reinforced stimulus without recourse to the dual pathway structure, but a modification of the original model, which nevertheless preserves the thalamo-amygdala and (reduced) thalamo-cortical pathways, enables stronger conditioning to a conditioned stimulus. Implications for neurocomputational modelling approaches are discussed.

关键词： dual-route hypothesis population coding emotion fear amygdala

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Odor to sensor space transformations in biological and artificial noses

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NEUROCOMPUTING 2000年 32卷 941-952页

作者： Pearce, TC Univ Leicester Dept Engn Leicester LE1 7RH Leics England

A geometric interpretation of population coding is described which gives an intuitive understanding of how individual sensor/receptor "tunings" define the overall performance of a sensory system based upon such a code. The approach is demonstrated by applying it to biologically realistic, and synthetic olfactory systems. The analytical measures developed as part of this treatment may be widely applied to optimize the detection performance of artificial "electronic nose" systems as well as for comparing different tuning scenarios in the biological olfactory pathway. The examples show clearly how a population coded sensory system can deliver both extreme selectivity to compounds of interest without comprising the range of stimuli that may be perceived. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： olfaction artificial nose population coding

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Schema generation in recurrent neural nets for intercepting a moving target

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BIOLOGICAL CYBERNETICS 2010年第6期102卷 451-473页

作者： Fleischer, Andreas G. Univ Hamburg Dept Biol D-22527 Hamburg Germany

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

关键词： Schema theory Feedforward controller Interception of a moving target Eye-hand coordination Recurrent neural nets Neural field theory population coding

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Towards a Neural Implementation of Causal Inference in Cue Combination

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MULTISENSORY RESEARCH 2013年第1-2期26卷 159-176页

作者： Ma, Wei Ji Rahmati, Masih Baylor Coll Med Houston TX 77030 USA

Causal inference in sensory cue combination is the process of determining whether multiple sensory cues have the same cause or different causes. Psychophysical evidence indicates that humans closely follow the predictions of a Bayesian causal inference model. Here, we explore how Bayesian causal inference could be implemented using probabilistic population coding and plausible neural operations, but conclude that the resulting architecture is unrealistic.

关键词： Multisensory perception cue combination Bayesian inference causal inference modeling population coding neural networks

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