The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.

Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience / Golosio, Bruno; DE LUCA, Chiara; Capone, Cristiano; Pastorelli, Elena; Stegel, Giovanni; Tiddia, Gianmarco; DE BONIS, Giulia; Stanislao Paolucci, Pier. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - 17:6(2021). [10.1371/journal.pcbi.1009045]

Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience

Chiara De Luca
;
Cristiano Capone;Elena Pastorelli;Giulia De Bonis;
2021

Abstract

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.
2021
memory consolidation; plasticity; pattern
01 Pubblicazione su rivista::01a Articolo in rivista
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience / Golosio, Bruno; DE LUCA, Chiara; Capone, Cristiano; Pastorelli, Elena; Stegel, Giovanni; Tiddia, Gianmarco; DE BONIS, Giulia; Stanislao Paolucci, Pier. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - 17:6(2021). [10.1371/journal.pcbi.1009045]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1491776
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