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neural coding for the retrieval of multiple memory patterns

BIOSYSTEMS 2006年第1-3期86卷 100-109页

作者： Morelli, A. Grotto, R. Lauro Arecchi, F. T. Univ Florence Dept Phys Ctr Interdipartimentale Studio Dinam Complesse I-50121 Florence Italy Univ Florence Dept Psychol I-50121 Florence Italy

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh-Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： memory model neural coding chaotic retrieval dynamics multiple and overlapping memories finite-time analysis

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Region-Based Approach versus Mechanism-Based Approach to the Brain

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Neuropsychoanalysis 2010年第2期12卷 167-170页

In “Neuropathologies of the Self: A General Theory,” Todd Feinberg discusses the neurological basis of various defense mechanisms and postulates a hierarchy of them. He thereby presupposes the concept of localization in the brain. In my commentary, I discuss the concept of localization in both its empirical and conceptual aspects and contrast it with an alternative approach, a more holistic one to the function of the brain. This pertains to the question of how information is coded in the brain which in turn is central for the kind of neural structure and organization the brain itself employs in its own processing of stimuli and their related information.

关键词： defense mechanisms localization hierarchy neural coding neural structure and organization

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Neuroscience inspired architecture for neural computing 10

Neuroscience inspired architecture for neural computing

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Proceedings of the 13th International Conference on Humans and Computers

作者： Subha Fernando Koichi Yamada Ashu Marasinghe Nagaoka University of Technology Kamitomioka Nagaoka Niigata Japan

This paper proposes new architecture for neural computing. The modeled architecture consists of neurons which has large number of synapses. These synapses not just make connections between neurons but are capable of computing their excitations level themselves and adjust those connections. Their excitations depend on the activities due to short-term and long-term plasticity mechanisms. Indeed our modeled synapses express both long-term and short-term behaviors at the same time and allow the interaction between these two plasticity mechanisms. Therefore synapses in a neuron might be activated due to short-term activities or long-term activities or both. By identifying how many synapses have been activated and due to which plasticity mechanism they have been activated give more information than simply saying which neuron is activated. As a result of these computationally potential synapses, a given neuron can also be activated by both of these plasticity mechanisms. Therefore, identification of the pattern of activation of neurons due to long-term plasticity and pattern of activation of neurons due to short-term plasticity gives two interpretations to the same input in two different time scales. That is, short-term activation of neurons can be considered as the formation of the short-term memory while long-term activation of neurons is the generalization of the inputs. Higher cognitive representation of the input can be considered as the pattern of activation of neurons in lower- and higher area of the hidden layer.

关键词： cognition neuroscience neural coding neural network

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Distance and orientation estimation of graspable objects in natural and artificial systems

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NEUROCOMPUTING 2009年第4-6期72卷 879-886页

作者： Chinellato, Eris del Pobil, Angel P. Univ Jaume 1 Robot Intelligence Lab Castellon De La Plana Spain

Being able to estimate pose and location of nearby objects is a fundamental skill for any natural or artificial agent actively interacting with its environment. The methods for extraction and integration of visual cues employed in artificial systems are usually very different from the solutions found in nature. We present a biologically plausible model of distance and orientation estimation based on neuroscience findings that is suitable to be implemented in a robotic vision-based grasping setup. Key novelties of the model are the use of simple retinal and proprioceptive data, and the integration between stereoptic and perspective cues. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Vision-based grasping Pose estimation Cue integration neural coding

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Techniques for temporal detection of neural sensitivity to external stimulation

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BIOLOGICAL CYBERNETICS 2009年第4期100卷 289-297页

作者： Rodriguez, Francisco B. Huerta, Ramon Univ Autonoma Madrid Escuela Politecn Super Dpto Ingn Informat GNB E-28049 Madrid Spain Univ Calif San Diego Inst Nonlinear Sci La Jolla CA 92093 USA

We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.

关键词： Sensitivity Bayes test neural response Olfaction Statistical testing neural coding Fisher test Likelihood ratio test

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Combining frequency and time domain approaches to systems with multiple spike train input and output

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BIOLOGICAL CYBERNETICS 2009年第6期100卷 459-474页

作者： Brillinger, D. R. Lindsay, K. A. Rosenberg, J. R. Univ Glasgow Div Neurosci & Biomed Syst Glasgow G12 8QQ Lanark Scotland Univ Glasgow Dept Math Glasgow G12 8QW Lanark Scotland Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA

A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two static fusimotor axons when the parent muscle is held at a fixed length and when it is subjected to random length changes. The frequency domain analysis has an associated error process which provides a measure of how well the input processes can be used to predict the output processes and is also used to specify how the interactions between the recorded processes contribute to this error. Without assuming stationarity of the input, the time domain approach uses a sequence of probability models of increasing complexity in which the number of input processes to the model is progressively increased. This feature of the time domain approach was used to identify a preferred direction of interaction between the processes underlying the generation of the activity of the primary and secondary endings. In the presence of fusimotor activity and dynamic length changes imposed on the muscle, it was shown that the activity of the primary and secondary endings carried different information about the effects of the inputs imposed on the muscle spindle. The results presented in this work emphasise that the analysis of the behaviour of complex systems benefits from a combination of frequency and time domain methods.

关键词： Multiple spike trains neural coding Maximum likelihood Canonical coherence Muscle spindle Multivariate point processes

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neural code metrics: Analysis and application to the assessment of neural models

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NEUROCOMPUTING 2009年第10-12期72卷 2337-2350页

作者： Martins, Joao Tomas, Pedro Sousa, Leonel INESC ID P-1000029 Lisbon Portugal Univ Tecn Lisboa Inst Super Tecn Dept Elect & Comp Engn P-1100 Lisbon Portugal Inst Politecn Beja Dept Engn Escola Super Tecnol & Gestao Beja Portugal

For the analysis of natural neural responses it is necessary to evaluate and compare the reliability of the produced spike sequences. The same occurs in the development and evaluation of neural models, which should mimic the real neural centers that are being modeled. Several neural metrics have been proposed to analyze neural responses, and to tune and evaluate neural models. neural metrics measure different characteristics of the neural code and can be grouped into distinct classes, as they follow a firing rate or time-code perspective. In this paper, several metrics belonging to the firing rate, spike train and firing event classes are reviewed. Using sets of neuronal responses and a set of models, the metrics are analyzed and compared to disclose their advantages and drawbacks. In most cases these metrics depend on a free parameter, that establishes their sensitivity to particular characteristics of the neural code. After showing that the incorrect choice of these parameters can lead to meaningless results, methods are presented in this paper to define a valid range of values for the parameters. These methods are based on a statistical analysis of the inter-trials errors. The application of neural metrics to the tuning and assessment of neural models of distinct classes reveals important results. Some of the analyzed metrics possess pronounced minima, specifically around the origin, which makes the optimization process more difficult;nonetheless, they provide insightful results for the evaluation of models. This paper also discusses the application of the neural metrics to evaluate neural models, providing relevant guidelines for their utilization. (c) 2009 Elsevier B.V. All rights reserved.

关键词： neural code metrics neural coding neural models Retina responses analysis

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The systems analysis approach to mechanosensory coding

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BIOLOGICAL CYBERNETICS 2009年第6期100卷 417-426页

作者： French, Andrew S. Dalhousie Univ Dept Physiol & Biophys Halifax NS B3H 1X5 Canada

An important problem in neuroscience is to obtain quantitative knowledge of how information is represented, or encoded, in the signals that nerve cells process and transmit. Sensory receptors have provided important models for the study of neural coding because their inputs can often be relatively easily controlled and measured, while the resultant activity is recorded. A variety of engineering concepts have been successfully applied to physiological sciences, particularly those related to control of dynamic systems. Linear systems analysis was one of the earliest methods used to probe sensory coding, and measurements such as step responses and frequency responses have become standard tools for describing sensory functions. Modern systems analysis has evolved to provide accurate and efficient linear identification of encoding in sensory receptors that use either graded potentials or action potentials. It has also led to nonlinear systems analysis, the creation of parametric nonlinear models, and measures of information coding by sensory neurons. These methods promise to provide important new knowledge about sensory systems in the future, especially when complemented with parallel biophysical and molecular studies of sensory neurons. Mechanoreceptors provided some of the earliest preparations for the investigation of neural coding, and both the linear and nonlinear properties of wide variety of vertebrate and invertebrate mechanoreceptors continue to be explored.

关键词： Linear Nonlinear Mechanoreceptor Volterra Noise neural coding

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PATTERNS AND PAUSES IN PURKINJE CELL SIMPLE SPIKE TRAINS: EXPERIMENTS, MODELING AND THEORY

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NEUROSCIENCE 2009年第3期162卷 816-826页

作者： De Schutter, E. Steuber, V. Univ Antwerp B-2610 Antwerp Belgium Univ Hertfordshire Sci & Technol Res Inst Hatfield AL10 9AB Herts England

We review our recent experimental and modeling results on how cerebellar Purkinje cells encode information in their simple spike trains and present a theory of the function of pauses and regular spiking patterns. The regular spiking patterns were discovered in extracellular recordings of simple spikes in awake and anesthetized rodents, where it was shown that more than half of the spontaneous activity consists of short epochs of regular spiking. These periods of regular spiking are interrupted by pauses, which can be tightly synchronized among nearby Purkinje cells, while the spikes in the regular patterns are not. Interestingly, pauses are affected by long-term depression of the parallel fiber synapses. Both in modeling and slice experiments it was demonstrated that long-term depression causes a decrease in the duration of pauses, leading to an increase of the spike output of the neuron. Based on these results we propose that pauses in the simple spike train form a temporal code which can lead to a rebound burst in the target deep cerebellar nucleus neurons. Conversely, the regular spike patterns may be a rate code, which presets the amplitude of future rebound bursts. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： cerebellum Purkinje cell deep cerebellar nucleus neural coding long-term depression rebound burst

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Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

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NEUROINFORMATICS 2009年第3期7卷 165-178页

作者： Goldberg, David H. Victor, Jonathan D. Gardner, Esther P. Gardner, Daniel Cornell Univ Weill Med Coll Lab Neuroinformat D404 New York NY 10065 USA Cornell Univ Weill Med Coll Dept Physiol New York NY 10065 USA Cornell Univ Weill Med Coll Dept Neurol & Neurosci New York NY 10065 USA Cornell Univ Weill Med Coll Lab Neuroinformat New York NY 10065 USA NYU Sch Med Dept Physiol & Neurosci New York NY 10016 USA

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://***, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

关键词： Computational neuroscience Information theory neural coding Neurodatabases Data sharing

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