Spike-timing pattern operates as gamma-distribution across cell types, regions and animal species and is essential for naturally-occurring cognitive states

Abstract

Spike-timing patterns are crucial for synaptic plasticity and neural peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/145813 doi: bioRxiv preprint first posted online Jun. 3, 2017; 3 Significance Statement: Spike-timing patterns are crucial for synaptic plasticity and neural computation, yet their statistical patterns in various brain regions which cross different mammalian species have not been examined in a systematic manner. We have examine statistical behaviors of spike-time irregularity in 13 different cortical and subcortical regions from mouse, hamster, cat and monkey brains. We show that spiketiming patterns of various projection neurons all conform to the gamma distribution model. Furthermore, there are significant regional variations of the spike-timing's regularities. In coupling with different NMDA receptor compositions across different regions, such intrinsic statistical characteristics of spiking-time distribution will likely influence not only how spike-timing patterns drive synaptic plasticity but also how cell assemblies in various circuits perform neural computation. peer-reviewed)

Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo

“…In the present study, focusing on only the longterm behavior of postsynaptic events, we derived approximate-analytic solutions from the Shouval's model. Our results found from the analytic solutions indicate that the synaptic weight by FDP depends not only on input frequency but also on input pattern, shape parameter in gamma process input, calcium decay time constant, and background synaptic activity, which have been suggested to vary in vivo depending on the location, the internal state, and the external environment of the neuron [41][42][43]51,53,54 . We now discuss the relevance of our study to some related prior works.…”

“…postsynaptic calcium level and synaptic weight as a function of the average frequency of gamma process input. We studied the postsynaptic calcium concentration and synaptic load of neurons receiving gamma process inputs, which is one of the firing patterns observed in brain 51,53 . Since the analytic solutions are qualitatively consistent with the simulation results so far presented in the present paper, we discuss the plasticity of synapses receiving gamma process input by only the analytic solutions.…”

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

“… Second, this self-information code inherently relies on the ISI variability-probability to convey information, whereas neuronal variability is typically viewed as noise that undermines real-time decoding in the classic rate-code or temporal-code models. The ISI variability is a basic phenomenon ( Softky and Koch 1993 ; Stevens and Zador 1998 ; Shadlen and Movshon 1999 ; Li and Tsien 2017 ), and did not grow larger from lower subcortical regions to higher cognition cortices ( Li et al 2018 ). The importance of spike variability is evident from the fact the diminished variability (i.e., rhythmic firing) underlies anesthesia-induced unconsciousness ( Fig.…”

“…Accordingly, we devised a general decoding strategy—termed ISI -based C ell- A ssembly D ecoding (iCAD) method—consisting of the following 3 major steps (Fig. 1 ): To convert ISI variations into real-time variability surprisals : Because ISI variability in many neural circuits conforms to the gamma distribution ( Maimon and Assad 2009 ; Li et al 2018 ), we first fitted a single neuron’s ISIs with a gamma-distribution model, which can assign each neuron’s ISI with a probability. Subsequently, a spike train emitted by a neuron can be transformed into a surprisal-based ternary code (positive surprisal as 1, ground state as 0, negative surprisal as −1) to describe the dynamic evolution in self-information states (Fig.…”

“…The importance of spike variability is evident from the fact the diminished variability (i.e., rhythmic firing) underlies anesthesia-induced unconsciousness ( Fig. S2 ) ( Fox et al 2017 ; Kuang et al 2010 ; Li et al 2018 ). Third, the robustness of this ISI-based surprisal code also comes from its ternary nature of coding (positive or negative surprisals, plus the ground state).…”