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Enhancement of neural stochastic firing in cochlear implant stimulation by the addition of noise: A computational study of the influence of stimulation settings and spontaneous activity

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COMPUTERS IN BIOLOGY AND MEDICINE 2010年第6期40卷 597-606页

作者： Paglialonga, Alessia Fiocchi, Serena Ravazzani, Paolo Tognola, Gabriella Politecn Milan CNR Ist Ingn Biomed Dipartimento BioingnISIB I-20133 Milan Italy

It is known that, in cochlear implants (CIs), the addition of noise to CI stimuli may enhance the stochastic firing in the auditory nerve (AN). The aim of this study was to investigate, by using a model of a fiber of the AN the influence of CI stimulation settings (i.e., stimulation rate and stimulus magnitude) and fiber spontaneous activity on the enhancement of stochastic firing when CI stimuli are combined with noise. Results showed that the stimulation rate had an effect on the enhancement of stochastic firing in the AN, whereas the stimulus magnitude and the spontaneous firing rate had no influence. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Cochlear implants Electrical stimulation Auditory nerve Nerve fiber models Stochastic firing neural coding Stochastic resonance Noise

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Assessing the performance of neural encoding models in the presence of noise

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2000年第2期8卷 95-112页

作者： Roddey, JC Girish, B Miller, JP Montana State Univ Ctr Computat Biol Bozeman MT 59717 USA

An analytical method is introduced for evaluating the performance of neural encoding models. The method addresses a critical question that arises during the course of the development and validation of encoding models: is a given model near optimal in terms of its accuracy in predicting the stimulus-elicited responses of a neural system, or can the predictive accuracy be improved significantly by further model development? The evaluation method is based on a derivation of the minimum mean-square error between actual responses and modeled responses. It is formulated as a comparison between the mean-square error of the candidate model and the theoretical minimum mean-square error attainable through an optimal model for the system. However, no a priori information about the nature of the optimal model is required. The theoretically minimum error is determined solely from the coherence function between pairs of system responses to repeated presentations of the same dynamic stimulus. Thus, the performance of the candidate model is judged against the performance of an optimal model rather than against that of an arbitrarily assumed model. Using this method, we evaluated a linear model for neural encoding by mechanosensory cells in the cricket cereal system. At low stimulus intensities, the best-fit linear model of encoding by single cells was found to be nearly optimal, even though the coherence between stimulus-response pairs (a commonly used measure of system linearity) was low. In this low-stimulus-intensity regime, the mean square error of the linear model was on the order of the power of the cell responses. In contrast, at higher stimulus intensities the linear model was not an accurate representation of neural encoding, even though the stimulus-response coherence was substantially higher than in the low-intensity regime.

关键词： neural coding information theory sensory systems cricket cereal system

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Information processing in the LGN: a comparison of neural codes and cell types

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BIOLOGICAL CYBERNETICS 2019年第4期113卷 453-464页

作者： Pregowska, Agnieszka Casti, Alex Kaplan, Ehud Wajnryb, Eligiusz Szczepanski, Janusz Polish Acad Sci Inst Fundamental Technol Res Pawinskiego 5B PL-02106 Warsaw Poland Fairleigh Dickinson Univ Gildart Haase Sch Comp Sci & Engn Dept Math Teaneck NJ 07666 USA Icahn Sch Med Mt Sinai New York NY 10029 USA Natl Inst Mental Hlth NUDZ Topolova 748 Klecany 25067 Czech Republic Charles Univ Prague Dept Philosophy Sci Prague Czech Republic

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient temporal code is employed, while for X-OFF cells a straightforward rate code is used, which is more reliable and is correlated with energy consumption.

关键词： Shannon information theory Cat LGN ON-OFF cells neural coding Entropy Firing rate

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Unsupervised learning in images and audio to produce neural receptive fields: a primer and accessible notebook

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 111-128页

作者： Urs, Namratha Behpour, Sahar Georgaras, Angie Albert, Mark V. Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA Univ North Texas Dept Informat Sci Denton TX 76203 USA Loyola Univ Dept Neurosci Chicago IL 60611 USA Univ North Texas Dept Biomed Engn Denton TX 76203 USA

Sensory processing relies on efficient computation driven by a combination of low-level unsupervised, statistical structural learning, and high-level task-dependent learning. In the earliest stages of sensory processing, sparse and independent coding strategies are capable of modeling neural processing using the same coding strategy with only a change in the input (e.g., grayscale images, color images, and audio). We present a consolidated review of Independent Component Analysis (ICA) as an efficient neural coding scheme with the ability to model early visual and auditory neural processing. We created a self-contained, accessible Jupyter notebook using Python to demonstrate the efficient coding principle for different modalities following a consistent five-step strategy. For each modality, derived receptive field models from natural and non-natural inputs are contrasted, demonstrating how neural codes are not produced when the inputs sufficiently deviate from those animals were evolved to process. Additionally, the demonstration shows that ICA produces more neurally-appropriate receptive field models than those based on common compression strategies, such as Principal Component Analysis. The five-step strategy not only produces neural-like models but also promotes reuse of code to emphasize the input-agnostic nature where each modality can be modeled with only a change in inputs. This notebook can be used to readily observe the links between unsupervised machine learning strategies and early sensory neuroscience, improving our understanding of flexible data-driven neural development in nature and future applications.

关键词： neural coding Efficient coding principle Sensory processing

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Noise, not stimulus entropy, determines neural information rate

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2003年第1期14卷 23-31页

作者： Borst, A Univ Calif Berkeley ESPM Div Insect Biol Berkeley CA 94720 USA

In the quest for deciphering the neural code, theoretical advances were made which allow for the determination of the information rate inherent in the spike trains of nerve cells. However, up to now, the dependence of the information rate on stimulus parameters has not been studied in any neuron in a systematic way. Here, I investigate the information carried by the spike trains of H1, a motion- sensitive visual interneuron of the blowfly ( Calliphora vicina) using a moving grating as a stimulus. Stimulus parameters fall in two classes: those that have only a minor effect on the information rate like increasing the frequency bandwidth or the maximum amplitude of the stimulus velocity, and those which dramatically affect the neural information rate, like varying the spatial size or the contrast of the visual pattern being moved. It appears that, for a broad range of complex stimuli, the neuron covers the stimulus with its whole response repertoire regardless of the stimulus entropy, with the information rate being limited by the noise of the stimulus and the neural hardware.

关键词： neural coding spike timing precision motion vision adaptation

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Symmetry Breaking in Soft Clustering Decoding of neural Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第2期56卷 901-927页

作者： Parker, Albert E. Dimitrov, Alexander G. Gedeon, Tomas Montana State Univ Ctr Biofilm Engn Bozeman MT 59717 USA Univ Auckland New Zealand Inst Math Auckland 1 New Zealand Montana State Univ Ctr Computat Biol Bozeman MT 59717 USA Montana State Univ Dept Cell Biol & Neurosci Bozeman MT 59717 USA Montana State Univ Dept Math Sci Bozeman MT 59717 USA

Information-based distortion methods have been used successfully in the analysis of neural coding problems. These approaches allow the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble quantitatively, while making few assumptions about the nature of either the code or of relevant stimulus features. The neural code-book is derived by quantizing sensory stimuli and neural responses into a small set of clusters, and optimizing the quantization to minimize an information distortion function. The method of annealing has been used to solve the corresponding high-dimensional nonlinear optimization problem. The annealing solutions undergo a series of bifurcations, which we study using bifurcation theory in the presence of symmetries. In this contribution we describe these symmetry breaking bifurcations in detail, and indicate some of the consequences of the form of the bifurcations. In particular, we show that the annealing solutions break symmetry at pitchfork bifurcations, and that subcritical branches can exist. Thus, at a subcritical bifurcation, there are local information distortion solutions which are not found by the method of annealing. Since the annealing procedure is guaranteed to converge to a local solution eventually, the subcritical branch must turn and become optimal at some later saddle-node bifurcation, which we have shown occur generically for this class of problems. This implies that the rate distortion curve, while convex for noninformation-based distortion measures, is not convex for information-based distortion methods.

关键词： Annealing bifurcations clustering information distortion neural coding symmetry breaking

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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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No Pattern Separation in the Human Hippocampus

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TRENDS IN COGNITIVE SCIENCES 2020年第12期24卷 994-1007页

作者： Quiroga, Rodrigo Quian Univ Leicester Ctr Syst Neurosci Leicester Leics England Peng Cheng Lab Shenzhen Peoples R China

Pattern separation is a basic principle of neuronal coding that precludes memory interference in the hippocampus. Its existence is supported by numerous theoretical, computational, and experimental findings in different species. However, I argue that recent evidence from single-neuron recordings suggests that pattern separation may not be present in the human hippocampus and that memories are instead coded by the coactivation of invariant and context -independent engrams. This alternative model prompts a reassessment of the definition of episodic memory and its distinction from semantic memory. Furthermore, I propose that a lack of pattern separation in memory coding may have profound implications that could explain cognitive abilities that are uniquely developed in humans, such as our power of generalization and of creative and abstract thinking.

关键词： episodic memory Concept Cells engram conjunctive coding neural coding human intelligence

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Natural time scales for neural encoding

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NEUROCOMPUTING 2000年 32卷 1027-1034页

作者： Dimitrov, AG Miller, JP Montana State Univ Ctr Computat Biol Bozeman MT 59717 USA

We propose natural time scales for characterization of two important parameters of neural encoding: spike timing precision dT and average word length T-w. These scales emerge through asymptotic limits of the mutual information between stimuli and corresponding spike trains. We used crickets (acheta domestica) as animal models to illustrate our definitions and test their validity. Intracellular multielectrode extracellular recordings were performed to assess neuronal activity in the crickets' terminal ganglion. The stimuli for these experiments were random waveform air currents with various statistical characteristics. Using an information-theoretic analysis based on the abovementioned time scales, we characterize the maximal information content of spike sequences in this sensory system, independent of their temporal discretization. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： neural coding temporal precision information theory cricket

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Synergy, redundancy, and multivariate information measures: an experimentalist's perspective

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2014年第2期36卷 119-140页

作者： Timme, Nicholas Alford, Wesley Flecker, Benjamin Beggs, John M. Indiana Univ Dept Phys Bloomington IN 47405 USA

Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussedwithin this paper.

关键词： Information theory Multivariate information measures Complex systems neural coding Dissociated neuronal cultures Multielectrode array

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