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Jonghan Shin - One of the best experts on this subject based on the ideXlab platform.
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The noise shaping Neural Coding hypothesis: a brief history and physiological implications
Neurocomputing, 2002Co-Authors: Jonghan ShinAbstract:Abstract About 10 years ago, I first proposed the noise shaping Neural Coding hypothesis which has successfully contributed to resolving several issues related to Neural code. Here I provide experimental evidences which support the noise shaping Neural Coding hypothesis and its physiological implications: (1) fast/slow cortical gain control mechanisms in vivo (e.g., attention and visual contrast adaptation); (2) oscillations and synchrony; (3) why does the thalamic deep brain stimulation for Parkinsonian patients work; and (4) whether irregular spike train is noise or signal.
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Adaptation in spiking neurons based on the noise shaping Neural Coding hypothesis.
Neural Networks, 2001Co-Authors: Jonghan ShinAbstract:Shin, Koch and Douglas [Shin, J., Koch, C., & Douglas, R. (1999). Adaptive Neural Coding dependent on the time-varying statistics of the somatic input current. Neural Computation, 11, 1983-2003] proposed an adaptive Neural Coding model that makes spiking neurons adapt its input/output relation to the stimulus statistics. In a surprisingly precise manner, the adaptive Neural Coding model has been supported by recent experiments. However, the previous report has two problems: (a) although the adaptive Neural Coding model was developed based on the noise shaping Neural Coding hypothesis, their connection was not explained clearly in the previous report; and (b) the previous model did not suggest a biologically plausible method to estimate the stimulus mean and variance from spike-evoked intracellular calcium concentration. In this paper, I present how the noise shaping Neural Coding hypothesis produced such a precise model without any available experimental data at that time. Moreover, I propose a computational model for a biologically plausible signal statistics extraction from spike-evoked intracellular calcium concentration. An asymmetry in contrast adaptation time between increasing and decreasing variance, observed in biological experiments, is explained using the signal statistics extraction method. In addition, a new perspective on the relationship between the spike train of spiking neurons and EEG (or local field potential (LFP)) is suggested based on the noise shaping Neural Coding hypothesis.
Shanglin Zhou - One of the best experts on this subject based on the ideXlab platform.
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Synaptic Excitatory-Inhibitory Balance Underlying Efficient Neural Coding.
Advances in neurobiology, 2018Co-Authors: Shanglin ZhouAbstract:Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how Neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of Neural Coding. We also point out the benefit of adopting such a balance during Neural Coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient Coding.
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Synaptic E-I Balance Underlies Efficient Neural Coding.
Frontiers in neuroscience, 2018Co-Authors: Shanglin ZhouAbstract:Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we first briefly summarize the evidence for how Neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of Neural Coding. We also point out the benefit of adopting such a balance during Neural Coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient Coding.
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Key factors dominating the Neural Coding preference to 1/f signal.
BMC Neuroscience, 2015Co-Authors: Wen Zhang, Shanglin Zhou, Yuguo YuAbstract:Experiments have demonstrated that cortical and sensory neurons prefer to response to signals with characteristics of long-term correlation or 1/f noise feature better than signals with no correlation like white-noise-type [1]. In order to study the underlying mechanism, we built up a cortical neuronal model [2] based on Hodgkin-Huxley theory to study the correlations between neuron kinetics and signal statistics. Interestingly, we observed that (see Figure 1a,b) white-noise-type signal (cutoff frequency >10000Hz) is hard to induce action potentials unless signals with very strong intensity while 1/f signal and low-pass filtered white noise type signal (cutoff frequency
Paul S Muhlekarbe - One of the best experts on this subject based on the ideXlab platform.
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reward boosts Neural Coding of task rules to optimise cognitive flexibility
The Journal of Neuroscience, 2019Co-Authors: Sam Hallmcmaster, Paul S Muhlekarbe, Nicholas E Myers, Mark G StokesAbstract:Cognitive flexibility is critical for intelligent behavior. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behavior can be improved by the prospect of reward, which suggests that rewards optimize flexible control processes. Here we investigated how different reward prospects influence Neural enCoding of task rule information to optimize cognitive flexibility. We applied representational similarity analysis to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high-reward trials. Critically, high reward boosted Neural Coding of the active task rule, and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule Coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening Neural Coding of task rule information, helping to improve cognitive flexibility during complex behavior. SIGNIFICANCE STATEMENT The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behavior and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape Neural Coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates the enCoding of task rules needed for flexible behavior. The extent to which reward increased task rule Coding also tracked improvements in behavioral performance under high-reward conditions. These findings help to inform how motivation shapes Neural processing in the healthy human brain.
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reward boosts Neural Coding of task rules to optimise cognitive flexibility
bioRxiv, 2019Co-Authors: Sam Hallmcmaster, Paul S Muhlekarbe, Nicholas E Myers, Mark G StokesAbstract:Abstract Cognitive flexibility is critical for intelligent behaviour. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behaviour can be improved by the prospect of reward, which suggests that rewards optimise flexible control processes. Here we investigated how different reward prospects influence Neural enCoding of task rule information to optimise cognitive flexibility. We applied representational similarity analysis (RSA) to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high reward trials. Critically, high reward boosted Neural Coding of the active task rule and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule Coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening Neural Coding of task rule information, helping to improve cognitive flexibility during complex behaviour. Significance Statement The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behaviour and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape Neural Coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates enCoding of task rules needed for flexible behaviour. The extent to which reward increased task rule Coding also tracked improvements in behavioural performance under high reward conditions. These findings help inform how motivation shapes Neural processing in the healthy human brain.
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Neural Coding for instruction based task sets in human frontoparietal and visual cortex
Cerebral Cortex, 2016Co-Authors: Paul S Muhlekarbe, John S Duncan, Wouter De Baene, Daniel J Mitchell, Marcel BrassAbstract:Task preparation has traditionally been thought to rely upon persistent representations of instructions that permit their execution after delays. Accumulating evidence suggests, however, that accurate retention of task knowledge can be insufficient for successful performance. Here, we hypothesized that instructed facts would be organized into a task set; a temporary Coding scheme that proactively tunes sensorimotor pathways according to instructions to enable highly efficient "reflex-like" performance. We devised a paradigm requiring either implementation or memorization of novel stimulus-response mapping instructions, and used multivoxel pattern analysis of neuroimaging data to compare Neural Coding of instructions during the pretarget phase. Although participants could retain instructions under both demands, we observed striking differences in their representation. To-be-memorized instructions could only be decoded from mid-occipital and posterior parietal cortices, consistent with previous work on visual short-term memory storage. In contrast, to-be-implemented instructions could also be decoded from frontoparietal "multiple-demand" regions, and dedicated visual areas, implicated in processing instructed stimuli. Neural specificity in the latter moreover correlated with performance speed only when instructions were prepared, likely reflecting the preconfiguration of instructed decision circuits. Together, these data illuminate how the brain proactively optimizes performance, and help dissociate Neural mechanisms supporting task control and short-term memory storage.
Rob De Ruyter Van Steveninck - One of the best experts on this subject based on the ideXlab platform.
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Neural Coding of natural stimuli information at sub millisecond resolution
BMC Neuroscience, 2007Co-Authors: Ilya Nemenman, Geoffrey D Lewen, William Bialek, Rob De Ruyter Van SteveninckAbstract:Our knowledge of the sensory world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion – sensitive neurons of the fly visual system as a test case. New experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight, and new mathematical methods allow us to draw more reliable conclusions about the information content of Neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of ~60 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Under these naturalistic conditions, the system's information transmission rate still increases with higher photon flux, even though individual photoreceptors are counting more than one million photons per second. Further, exploiting the relatively slow dynamics of the stimulus, the system removes redundancy and so generates a more efficient Neural code.
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Neural Coding of natural stimuli information at sub millisecond resolution
PLOS Computational Biology, 2005Co-Authors: Ilya Nemenman, Geoffrey D Lewen, William Bialek, Rob De Ruyter Van SteveninckAbstract:Sensory information about the outside world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion-sensitive neurons of the fly visual system as a test case. Our experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight. New mathematical methods allow us to draw more reliable conclusions about the information content of Neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of ∼55 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Finally, the efficiency of our entropy estimator makes it possible to uncover features of Neural Coding relevant for natural visual stimuli: first, the system's information transmission rate varies with natural fluctuations in light intensity, resulting from varying cloud cover, such that marginal increases in information rate thus occur even when the individual photoreceptors are counting on the order of one million photons per second. Secondly, we see that the system exploits the relatively slow dynamics of the stimulus to remove Coding redundancy and so generate a more efficient Neural code.
Stefano Panzeri - One of the best experts on this subject based on the ideXlab platform.
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principles of Neural Coding
Current Biology, 2013Co-Authors: Rodrigo Quian Quiroga, Stefano PanzeriAbstract:Section I Methods Physiological Foundations of Neural Signals Kevin Whittingstall and Nikos K. Logothetis Biophysics of Extracellular Spikes Costas A. Anastassiou, Gyorgy Buzsaki, and Christof Koch Local Field Potentials: Biophysical Origin and Analysis Gaute T. Einevoll, Henrik Linden, Tom Tetzlaff, Szymon Leski, and Klas H. Pettersen Spike Sorting Juan Martinez and Rodrigo Quian Quiroga Spike-Train Analysis Ines Samengo, Daniel Elijah, and Marcelo A. Montemurro Synchronization Measures Thomas Kreuz Role of Correlations in Population Coding Peter E. Latham and Yasser Roudi DeCoding and Information Theory in Neuroscience Rodrigo Quian Quiroga and Stefano Panzeri Section II Experimental Results Neural Coding of Visual Objects Charles E. Connor Coding in the Auditory System Jan Schnupp Coding in the Whisker Sensory System Mathew E. Diamond and Ehsan Arabzadeh Neural Coding in the Olfactory System Ron A. Jortner Coding across Sensory Modalities: Integrating the Dynamic Face with the Voice Chandramouli Chandrasekaran and Asif A. Ghazanfar Population Coding by Place Cells and Grid Cells Jill K. Leutgeb, Emily A. Mankin, and Stefan Leutgeb Coding of Movement Intentions Hansjorg Scherberger, Rodrigo Quian Quiroga, and Richard A. Andersen Neural Coding of Short-Term Memory Stefanie Liebe and Gregor Rainer Role of Temporal Spike Patterns in Neural Codes Rasmus S. Petersen Adaptation and Sensory Coding Miguel Maravall Sparse and Explicit Neural Coding Peter Foldiak Information Coding by Cortical Populations Kenneth D. Harris Information Content of Local Field Potentials: Experiments and Models Alberto Mazzoni, Nikos K. Logothetis, and Stefano Panzeri Principles of Neural Coding from EEG Signals Fernando H. Lopes da Silva Gamma-Band Synchronization and Information Transmission Martin Vinck, Thilo Womelsdorf, and Pascal Fries DeCoding Information from fMRI Signals Jakob Heinzle and John-Dylan Haynes Section III Theoretical and In Silico Approaches Dynamics of Neural Networks Nicolas Brunel Learning and Coding in Neural Networks Timothee Masquelier and Gustavo Deco Ising Models for Inferring Network Structure from Spike Data John A. Hertz, Yasser Roudi, and Joanna Tyrcha Vocal Learning with Inverse Models Richard H. R. Hahnloser and Surya Ganguli Computational Models of Visual Object Recognition Gabriel Kreiman Coding in Neuromorphic VLSI Networks Giacomo Indiveri Open-Source Software for Studying Neural Codes Robin A. A. Ince
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Neural Coding and contextual influences in the whisker system
Biological cybernetics, 2009Co-Authors: Rasmus S. Petersen, Stefano Panzeri, Miguel MaravallAbstract:A fundamental problem in neuroscience, to which Prof. Segundo has made seminal contributions, is to understand how action potentials represent events in the external world. The aim of this paper is to review the issue of Neural Coding in the context of the rodent whiskers, an increasingly popular model system. Key issues we consider are: the role of spike timing; mechanisms of spike timing; deCoding and context-dependence. Significant insight has come from the development of rigorous, information theoretic frameworks for tackling these questions, in conjunction with suitably designed experiments. We review both the theory and experimental studies. In contrast to the classical view that neurons are noisy and unreliable, it is becoming clear that many neurons in the subcortical whisker pathway are remarkably reliable and, by virtue of spike timing with millisecond-precision, have high bandwidth for conveying sensory information. In this way, even small (~200 neuron) subcortical modules are able to support the sensory processing underlying sophisticated whisker-dependent behaviours. Future work on Neural Coding in cortex will need to consider new findings that responses are highly dependent on context, including behavioural and internal states.
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correcting for the sampling bias problem in spike train information measures
Journal of Neurophysiology, 2007Co-Authors: Stefano Panzeri, Riccardo Senatore, Marcelo A Montemurro, Rasmus S. PetersenAbstract:Information Theory enables the quantification of how much information a neuronal response carries about external stimuli and is hence a natural analytic framework for studying Neural Coding. The ma...
