Correlation entropy of synaptic input-output dynamics

Kleppe, Ingo; Robinson, Hugh P. C.

doi:10.1103/physreve.74.041909

Cited by 8 publications

(7 citation statements)

References 25 publications

(48 reference statements)

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“…This has been shown at all measurable levels of neural activity, from the synapse level (Katz and Miledi, 1970; Kleppe and Robinson, 2006) and ion channels (Steinmetz et al, 2000; White et al, 2000), and hemodynamic responses (Neumann et al, 2003). Such fluctuations in neural signal have sometimes been regarded as detrimental for neural processes and “noise” is often treated as a nuisance variable in analyses (Faisal et al, 2008).…”

Section: Introductionmentioning

confidence: 84%

Brain signal variability relates to stability of behavior after recovery from diffuse brain injury

et al. 2012

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Variability or noise is an unmistakable feature of neural signals; however such fluctuations have been regarded as not carrying meaningful information or as detrimental for neural processes. Recent empirical and computational work has shown that neural systems with a greater capacity for information processing are able to explore a more varied dynamic repertoire, and the hallmark of this is increased irregularity or variability in the neural signal. How this variability in neural dynamics affects behavior remains unclear. Here, we investigated the role of variability of magnetoencephalography signals in supporting healthy cognitive functioning, measured by performance on an attention task, in healthy adults and in patients with traumatic brain injury. As an index of variability, we calculated multiscale entropy, which quantifies the temporal predictability of a time series across progressively more coarse time scales. We found lower variability in traumatic brain injury patients compared to controls, arguing against the idea that greater variability reflects dysfunctional neural processing. Furthermore, higher brain signal variability indicated improved behavioral performance for all participants. This relationship was statistically stronger for people with brain injury, demonstrating that those with higher brain signal variability were also those who had recovered the most cognitive ability. Rather than impede neural processing, cortical signal variability within an optimal range enables the exploration of diverse functional configurations, and may therefore play a vital role in healthy brain function.

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Section: Introductionmentioning

confidence: 84%

Brain signal variability relates to stability of behavior after recovery from diffuse brain injury

et al. 2012

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“…In particular, it has been shown how the random but not independent synaptic transmission between subsequent synaptic event conveys information (in the formal sense of uncertainty reduction) about the timing of previous presynaptic spikes [25], [26].…”

Section: Introductionmentioning

confidence: 99%

History-Dependent Excitability as a Single-Cell Substrate of Transient Memory for Information Discrimination

2010

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Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron “sees” through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.

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“…Von Newman entropy, originally derived for quantum mechanics applications, has been applied to EEG data [ 35 ]. Kolmogorov entropy has been used on EEG data for epileptic seizure prediction [ 36 ] and on cell patch-clamp recordings [ 37 ]. Success in finding an algorithm that can objectively quantify pathologic motor patterns will help to identify infants who would benefit from therapeutic intervention, as well as provide an important research tool for assessment of various interventions for developmentally delayed infants.…”

Section: Discussionmentioning

confidence: 99%

Use of information entropy measures of sitting postural sway to quantify developmental delay in infants

Deffeyes

Harbourne

DeJong

et al. 2009

J NeuroEngineering Rehabil

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Correlation entropy of synaptic input-output dynamics

Cited by 8 publications

References 25 publications

Brain signal variability relates to stability of behavior after recovery from diffuse brain injury

Brain signal variability relates to stability of behavior after recovery from diffuse brain injury

History-Dependent Excitability as a Single-Cell Substrate of Transient Memory for Information Discrimination

Use of information entropy measures of sitting postural sway to quantify developmental delay in infants

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