Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model

Abstract

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.

Scaling of a Large-Scale Simulation of Synchronous Slow-Wave and Asynchronous Awake-Like Activity of a Cortical Model With Long-Range Interconnections

“…The present implementation of DPSNN demonstrated to be efficient for homogeneous bidimensional grids of neural columns and for their mapping of up to 1,024 processes, and this facilitates a set of interesting scientific applications. However, further optimization could improve DPSNN performance, either in the perspective of moving simulations toward million-core exascale platforms or targeting real-time simulations at smaller scale (Simula et al, 2019), in particular addressing sleep-induced optimizations in cognitive tasks like classification (Capone et al, 2019a). For instance, we expect that the delivery of spiking messages will be a key element to be further optimized (e.g., using a hierarchical communication strategy).…”

Real-Time Cortical Simulations: Energy and Interconnect Scaling on Distributed Systems

“…This network is able to enter both an asynchronous awake-like regime and a deep-sleep-like slow wave activity, by tuning the values of SFA and stimulation. Within the Wavescales experiment, a similar model with SFA is extended to study the interactions between Slow Waves Activity, memory association and synaptic homeostasis in a thalamo-cortical model applied to the classification of MNIST handwritten digits [19]. In this paper, synapses inject instantaneous post-synaptic currents while synaptic plasticity is disabled.…”

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models

“…Moreover, a large effort has been made in order to derive population descriptions from the specificity of the network model under consideration. This bottom-up approach permits to obtain a dimensionally reduced mean f ield description of the network population dynamics in different regimes (Amit and Brunel, 1997;Brunel and Hakim, 1999;Capone et al, 2019b;di Volo et al, 2014;El Boustani and Destexhe, 2009;Montbrió et al, 2015;Ohira and Cowan, 1993;Renart et al, 2004;Schwalger et al, 2017;Tort-Colet et al, 2019;Tsodyks and Sejnowski, 1995;Van Vreeswijk and Sompolinsky, 1996;Vreeswijk and Sompolinsky, 1998). On one hand mean f ield models permit a simpler, reduced picture of the dynamics of a population of neurons, thus allowing to unveil mechanisms determining specific observed phenomena (di Volo and Torcini, 2018;Jercog et al, 2017;Reig and Sanchez-Vives, 2007).…”