Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Abstract

Irregular ongoing activity in cortical networks is often modeled as arising from recurrent connectivity. Yet it remains unclear to what extent its presence corrupts sensory signal transmission and network computational capabilities. In a recurrent cortical-like network, we have determined the activity patterns that are better transmitted and self-sustained by the network. We show that reproducible spiking and subthreshold dynamics can be triggered if the statistics of the imposed external drive are consistent with patterns previously seen in the ongoing activity. A subset of neurons in the network, constrained to replay temporal pattern segments extracted from the recorded ongoing activity of the same network, reliably drives the remaining, free-running neurons to call the rest of the pattern. Comparison with surrogate Poisson patterns indicates that the efficiency of the recall and completion process depends on the similarity between the statistical properties of the input with previous ongoing activity The reliability of evoked dynamics in recurrent networks is thus dependent on the stimulus used, and we propose that the similarity between spontaneous and evoked activity in sensory cortical areas could be a signature of efficient transmission and propagation across cortical networks.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons

“…It should be noted that such networks are not able to maintain self-sustained activity by themselves. Several studies previously reported that these ongoing and reverberating regimes could be observed in networks with conductance-based synapses (Vogels and Abbott 2005;Kumar et al 2008;El Boustani and Destexhe 2009;Marre et al 2009), but they were all achieved in networks without any propagation delays. We empirically noticed that the linear propagation time taken into account here (see Section 2) increases the average synaptic delay and therefore the neuron density that would have been necessary to observe such a spontaneous regime.…”

“…In this paper, we chose to study a more realistic two-dimensional network of integrate-and-fire neurons, which is more relevant biologically since it includes propagation delays (Bringuier et al 1999;Benucci et al 2007) and conductance-based synapses (Vogels and Abbott 2005;Cessac and Viéville 2008;Kumar et al 2008;Marre et al 2009). We provide a detailed numerical study of its spatio-temporal correlations for Gaussian connectivity profiles, previously introduced in the context of information processing (Mehring et al 2003).…”

Stable Learning in Stochastic Network States

“…In this case, synaptic weights directly change the network dynamics and vice versa (Lubenov and Siapas, 2008). For appropriate parameters, balanced networks of integrate-and-fire neurons can display asynchronous irregular (AI) states (Brunel, 2000;Vogels and Abbott, 2005;Kumar et al, 2008;El Boustani and Destexhe, 2009;Marre et al, 2009) that produce spike discharge patterns similar to those reported in awake animals (Steriade, 2001;El Boustani et al, 2007). Recurrent neuronal networks have been studied with weight-independent STDP rules (Izhikevich et al, 2004;Morrison et al, 2007;Kang et al, 2008;Lubenov and Siapas, 2008;Gilson et al, 2010).…”

Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs

“…Although there have been theoretical studies for over a decade that attempted to characterize the spontaneous activity (Amit & Brunel, 1997;Destexhe & Contreras, 2006), the relationship between the spontaneous and evoked activities as well as its response against external input has not yet been fully elucidated (Marre, Yger, Davison, & Fregnac, 2009). To analyze this relationship, we have focused on memories of the input/output (I/O) map, one of the principle functions of the neural system, and proposed a novel viewpoint of memory (Kurikawa & Kaneko, 2011), which we have termed ''memories as bifurcations''.…”