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Time-dependence of SI RA neuron response to cutaneous flutter stimulation

SOMATOSENSORY AND MOTOR RESEARCH 2003年第1期20卷 45-69页

作者： Whitsel, BL Kelly, EF Quibrera, M Tommerdahl, M Li, Y Favorov, OV Xu, M Metz, CB Univ N Carolina Sch Med Dept Cell & Mol Physiol Chapel Hill NC 27599 USA Univ N Carolina Dept Biomed Engn Chapel Hill NC 27599 USA Univ N Carolina Sch Dent Dept Diagnost Studies Chapel Hill NC 27599 USA

Spike discharge activity of RA-type SI cortical neurons was recorded extracellularly in anesthetized monkeys and cats. Multiple applications ( trials) of 10-50 Hz sinusoidal vertical skin displacement stimulation ("flutter'') were delivered to the receptive field (RF). Analysis revealed large and systematic temporal trends not only in SI RA neuron responsivity ( measured as spikes/s and as spikes/stimulus cycle), but also in entrainment, and in phase angle of the entrained responses. In contrast to SI RA neurons, the response of RA skin afferents to comparable conditions of skin flutter stimulation exhibited little or no dynamics. The occurrence and form of the SI RA neuron response dynamics that accompany skin flutter stimulation are shown to depend on factors such as stimulus frequency and the locus of the recording site in the global cortical response pattern. Comparison of recordings obtained in near-radial vs tangential microelectrode penetrations further reveals that the SI RA neuron response dynamics that occur during skin flutter stimulation are relatively consistent within, but heterogeneous across column-sized regions. The observed SI RA neuron response dynamics are suggested to account, in part, for the improved capacity to discriminate stimulus frequency after an exposure ("adaptation'') to skin flutter stimulation (Goble and Hollins, J Acoust Soc Am 96: 771 780, 1994). Parallels with recent proposals about the contributions to visual perception of short-term primary sensory cortical neuron dynamics and synchrony in multineuron spike activity patterns are identified and discussed.

关键词： somatosensation cerebral cortex vibrotaction neural coding response synchrony information processing

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Exercise increases information content and affects long-term stability of hippocampal place codes

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CELL REPORTS 2022年第8期41卷 000-000页

作者： Rechavi, Yoav Rubin, Alon Yizhar, Ofer Ziv, Yaniv Weizmann Inst Sci Dept Brain Sci IL-76100 Rehovot Israel

Physical exercise is known to augment brain functioning, improving memory and cognition. However, while some of the physiological effects of physical activity on the brain are known, little is known about its effects on the neural code. Using calcium imaging in freely behaving mice, we study how voluntary exercise affects the quality and long-term stability of hippocampal place codes. We find that running accelerates the emer-gence of a more informative spatial code in novel environments and increases code stability over days and weeks. Paradoxically, although runners demonstrated an overall more stable place code than their sedentary peers, their place code changed faster when controlling for code quality level. A model-based simulation shows that the combination of improved code quality and faster representational drift in runners, but neither of these effects alone, could account for our results. Thus, exercise may enhance hippocampal function via a more informative and dynamic place code.

关键词： running physical activity hippocampus adult neurogenesis place cells calcium imaging neural coding representational drift memory

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Pain and itch coding mechanisms of polymodal sensory neurons

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CELL REPORTS 2023年第11期42卷 113316页

作者： Guo, Changxiong Jiang, Haowu Huang, Cheng-Chiu Li, Fengxian Olson, William Yang, Weishan Fleming, Michael Yu, Guang Hoekel, George Luo, Wenqin Liu, Qin Washington Univ Pain Ctr Sch Med St Louis Dept Anesthesiol St Louis MO 63110 USA Univ Penn Perelman Sch Med Dept Neurosci Philadelphia PA 19104 USA

Pain and itch coding mechanisms in polymodal sensory neurons remain elusive. MrgprD+ neurons represent a major polymodal population and mediate both mechanical pain and nonhistaminergic itch. Here, we show that chemogenetic activation of MrgprD+ neurons elicited both pain-and itch-related behavior in a dose -dependent manner, revealing an unanticipated compatibility between pain and itch in polymodal neurons. While VGlut2-dependent glutamate release is required for both pain and itch transmission from MrgprD+ neu-rons, the neuropeptide neuromedin B (NMB) is selectively required for itch signaling. Electrophysiological re-cordings further demonstrated that glutamate synergizes with NMB to excite NMB-sensitive postsynaptic neurons. Ablation of these spinal neurons selectively abolished itch signals from MrgprD+ neurons, without affecting pain signals, suggesting a dedicated itch-processing central circuit. These findings reveal distinct neurotransmitters and neural circuit requirements for pain and itch signaling from MrgprD+ polymodal sen-sory neurons, providing new insights on coding and processing of pain and itch.

关键词： pain itch polymodal sensory neurons MrgprD neural coding neurotransmitters glutamate neuromedin B Research topic(s)CP: Neuroscience

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How Far can neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli Processes

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INTERNATIONAL JOURNAL OF neural SYSTEMS 2019年第8期29卷 1950003页

作者： Pregowska, Agnieszka Kaplan, Ehud Szczepanski, Janusz Polish Acad Sci Inst Fundamental Technol Res Ul Pawinskiego 5B PL-02106 Warsaw Poland Icahn Sch Med Mt Sinai One Gustave Levy Pl New York NY 10029 USA Charles Univ Prague Fac Sci Dept Philosophy & Hist Sci Albertov 6 Prague 12843 2 Czech Republic Natl Inst Mental Hlth Topolova 748 Klecany 25067 Czech Republic

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon's definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code). In a previous paper we found that a crucial role in the relation between information transmission rates (ITRs) and firing rates is played by a parameter s, which is the sum of transition probabilities from the no-spike state to the spike state and vice versa. We found that in this case too a crucial role is played by the same parameter s. We calculated the maximal and minimal bounds of the quotient of ITRs for these sources. Next, making use of the entropy grouping axiom, we determined the loss of information in a Markov source compared with the information in the corresponding Bernoulli source for a given word length. Our results show that in the case of correlated signals the loss of information is relatively small, and thus temporal codes, which are more energetically efficient, can replace rate codes effectively. These results were confirmed by experiments.

关键词： Shannon information theory information source information transmission rate firing rate neural coding

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neural variability, or lack thereof

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2013年第2013期7卷 7-7页

作者： Masquelier, Timothee Univ Pompeu Fabra Unit Brain & Cognit Dept Informat & Commun Technol Barcelona Spain Univ Paris 06 CNRS Lab Neurobiol Adapt Proc UMR 7102 Paris France

We do not claim that the brain is completely deterministic, and we agree that noise may be beneficial in some cases. But we suggest that neuronal variability may be often overestimated, due to uncontrolled internal variables, and/or the use of inappropriate reference times. These ideas are not new, but should be re-examined in the light of recent experimental findings: trial-to-trial variability is often correlated across neurons, across trials, greater for higher-order neurons, and reduced by attention, suggesting that "intrinsic" sources of noise can only account for a minimal part of it. While it is obviously difficult to control for all internal variables, the problem of reference time can be largely avoided by recording multiple neurons at the same time, and looking at statistical structures in relative latencies. These relative latencies have another major advantage: they are insensitive to the variability that is shared across neurons, which is often a significant part of the total variability. Thus, we suggest that signal-to-noise ratios in the brain may be much higher than usually thought, leading to reactive systems, economic in terms of number of neurons, and energy efficient.

关键词： neural variability signal-to-noise ratio reliability redundancy neural coding

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FLUCTUATION SCALING IN neural SPIKE TRAINS

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2016年第3期13卷 537-550页

作者： Koyama, Shinsuke Kobayashi, Ryota Inst Stat Math Dept Stat Modeling 10-3 Midoricho Tachikawa Tokyo Japan Grad Univ Adv Studies SOKENDAI Dept Stat Sci 10-3 Midoricho Tachikawa Tokyo Japan Natl Inst Informat Principles Informat Res Div Chiyoda Ku 2-1-2 Hitotsubashi Tokyo Japan Grad Univ Adv Studies Dept Informat SOKENDAI Chiyoda Ku 2-1-2 Hitotsubashi Tokyo Japan

Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have considered the first-passage time of an Ornstein-Uhlenbeck process and used a conductance-based neuron model with excitatory and inhibitory synaptic inputs to demonstrate the emergence of fluctuation scaling with various exponents, depending on the input regimes and the ratio between excitation and inhibition. Furthermore, we have discussed the possible implication of these results in the context of neural coding.

关键词： neural coding Ornstein-Uhlenbeck process first-passage time spike train statistics single neuron models

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An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in neural Prosthetics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第5期12卷 1034-1047页

作者： Xin, Yao Li, Will X. Y. Zhang, Zhaorui Cheung, Ray C. C. Song, Dong Berger, Theodore W. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ So Calif Ctr Neural Engn Dept Biomed Engn Los Angeles CA 90089 USA

neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.

关键词： Neuroprosthetics neural coding adaptive filter application specific instruction set processor field-programmable gate array

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Data-Driven Approaches to Understanding Visual Neuron Activity

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Vision Science 1000年第1期5卷 451-477页

作者： Daniel A. Butts Department of Biology and Program in Neuroscience and Cognitive Science University of Maryland College Park Maryland 20742 USA email: dab@umd.edu

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

关键词： modeling receptive field neural coding machine learning neural networks

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coding Principles in Adaptation

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Vision Science 1000年第1期5卷 427-449页

作者： Alison I. Weber Kamesh Krishnamurthy Adrienne L. Fairhall 1Department of Physiology and Biophysics and Computational Neuroscience Center University of Washington Seattle Washington 98195 USA email: aiweber@uw.edufairhall@uw.edu 2Neuroscience Institute and Center for Physics of Biological Function Department of Physics Princeton University Princeton New Jersey 08544 USA email: kameshk@princeton.edu 3UW Institute for Neuroengineering University of Washington Seattle Washington 98195 USA

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

关键词： neural coding efficient coding redundancy reduction predictive coding surprise inference

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Optimal attentional modulation of a neural population

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2014年第MAR期8卷 34页

作者： Borji, Ali Itti, Laurent Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Grad Program Neurosci Los Angeles CA 90089 USA Univ So Calif Dept Psychol Los Angeles CA 90089 USA

Top-down attention has often been separately studied in the contexts of either optimal population coding or biasing of visual search. Yet, both are intimately linked, as they entail optimally modulating sensory variables in neural populations according to top-down goals. Designing experiments to probe top-down attentional modulation is difficult because non-linear population dynamics are hard to predict in the absence of a concise theoretical framework. Here, we describe a unified framework that encompasses both contexts. Our work sheds light onto the ongoing debate on whether attention modulates neural response gain, tuning width, and/or preferred feature. We evaluate the framework by conducting simulations for two tasks: (1) classification (discrimination) of two stimuli s(a) and s(b) and (2) searching for a target T among distractors D. Results demonstrate that all of gain, tuning, and preferred feature modulation happen to different extents, depending on stimulus conditions and task demands. The theoretical analysis shows that task difficulty (linked to difference Lambda between s(a) and s(b), or T, and D) is a crucial factor in optimal modulation, with different effects in discrimination vs. search. Further, our framework allows us to quantify the relative utility of neural parameters. In easy tasks (when Delta is large compared to the density of the neural population), modulating gains and preferred features is sufficient to yield nearly optimal performance;however, in difficult tasks (smaller Delta), modulating tuning width becomes necessary to improve performance. This suggests that the conflicting reports from different experimental studies may be due to differences in tasks and in their difficulties. We further propose future electrophysiology experiments to observe different types of attentional modulation in a same neuron.

关键词： top-down attention neural modulation neural coding gain tuning width feature selectivity

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