Feedback stabilizes propagation of synchronous spiking in cortical neural networks

Abstract

Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation.neural coding | attention | cerebral cortex | synfire | spike timing T he firing rates of cortical neurons carry information about sensory inputs and motor actions (1). Precisely timed action potentials related to stimuli and behavior have also been observed in the cerebral cortex (2-4), and the mechanisms underlying precisely timed spike initiation have been studied in cortical neurons in vitro (5). However, the information carried by the precise timing of spikes would have to propagate through a hierarchy of cortical areas (6) and noise could disrupt millisecond precision during the transmission.Previous modeling studies have demonstrated that synchronized volleys of spikes are essential for reliably driving the cortex by sparse thalamic inputs (7) and can indeed propagate through the layers of a feedforward network without compromising the temporal precision (8, 9). Furthermore, the temporal precision of a spike volley sharpens as it propagates through the network (10, 11). However, the results of these modeling studies suggest that only sufficiently strong stimuli that evoke spike volleys with a large number of spikes and a small dispersion of spike times would successfully propagate through the feedforward networks without degrading the temporal precision, whereas neural activities that are too weak or too dispersed will die out. This is a critical limitation on the propagation of synchronous spiking compared with the propagation of firing rates; stimuli evoking even low firing rate activity can successfully propagate through multilayer neural networks (12-14).Here we show that when feedback connections are added to a multilayer feedforward model the propagation of synchronous spiking through the network layers is significantly enhanced without compromising temporal precision.In our model with feedback projections, the state space of the model was divided into two areas: ...

Development of cortical motor circuits between childhood and adulthood: A navigated TMS‐HdEEG study

“…In our study, TMS of the motor cortex produced a robust and highly significant increase in phase locking in children and adolescents compared with adults, while the ERSP measures showed no statistically significant developmental differences. Thus, various factors, including differences in (GABAergic) inhibitory modulation of the evoked oscillations [Cho et al, 2015;Ferrarelli et al, 2008;Fukui et al, 2010;Julkunen et al, 2013], in signal variability, or in neural network connectivity [Haider et al, 2010;Lippe et al, 2009;Miyauchi et al, 2016;Moldakarimov et al, 2015;Vakorin et al, 2011], may explain the agerelated differences in ITC in our study. The ITC increment of children was not frequency specific and covered a large time range.…”

A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex

“…This attenuation can be substantially decreased by changing model parameters (M. Joglekar & X.-J. Wang, personal communication), and may also be removed by synchronous firing (Diesmann et al, 1999) or more sophisticated feedback projections (Moldakarimov et al, 2015). Third, we only consider cortico-cortical connections.…”

Motor learning requires myelination to reduce asynchrony and spontaneity in neural activity

“…The abnormally high basal activity observed in PLP‐tg mice may be caused by occasionally occurring synchronous events of temporally‐spread synaptic transmission from many distant areas. These abnormalities would weaken the propagation of information and STDP within cortical networks (Moldakarimov, Bazhenov, & Sejnowski, ; Pajevic et al, ) and may deprive neurons of the ability to regulate activity‐dependent myelination in respect to white matter plasticity (Hines et al, ; Mensch et al, ; Wake et al, ; Wake et al, ). During development, myelination changes the CV of thalamocortical pathways to increase synchronicity of the cortical activity, which may facilitate cortical information processing (Kimura & Itami, ; Salami et al, ).…”

“…The abnormally high basal activity observed in PLP-tg mice may be caused by occasionally occurring synchronous events of temporally-spread synaptic transmission from many distant areas. These abnormalities would weaken the propagation of information and STDP within cortical networks (Moldakarimov, Bazhenov, & Sejnowski, 2015;Pajevic et al, 2014) and may deprive neurons of the ability to regulate activity-dependent myelination in respect to white matter plasticity The frequency and amplitude of task-related and spontaneous Ca 2+ transients in PV neurons on Day 1 (n = 115 from five fields in four WT/PV-Cre mice, n = 145 from nine fields in five PLP-tg/PV-Cre mice, task-related: frequency, p = .051, Wilcoxon rank sum test; amplitude, p = .63, unpaired t test; spontaneous: frequency, p = .72, Wilcoxon rank sum test; amplitude, p = .86, unpaired t test). Violin plot shows the mean (black lines) and distribution of the data.…”