An oscillatory neural network model that demonstrates the benefits of multisensory learning

Abstract

Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.

A spiking neural network model of spatial and visual mental imagery

“…However, the SPA does present a trade-off between cognitive flexibility and biophysical accuracy, though recent efforts such as BioSpaun (Eliasmith et al 2016) have helped narrow this gap by incorporating more detailed neuron models. Nevertheless, there is a debate to be had over top-down versus bottom-up approaches to neuromodelling (Eliasmith and Trujillo 2014), as well as evidence that low-level biophysical properties play a role in cognitive processing, such as attention (Zhang et al 2019) and learning (Rao 2018).…”

Effects of aerobic exercise on sad emotion regulation in young women: an electroencephalograph study

“…Exclusion criteria were a history of neurological, psychiatric disorders (including premenstrual dysphoric disorder, as evaluated using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition); chronic cardiorespiratory illnesses; limb movement disorder; use of cortisol medications; and personality disorders. Furthermore, all subjects were not athletes who believed to have additive effects on spatio-temporal processing (Rao 2018;Isoglu-Alkac et al 2018;Bayazit and Ungur 2018) or brain connectivity (Tharawadeepimuk and Wongsawat 2017). Before the experiment, each participant was asked to refrain from alcohol for at least 48 h and from caffeine/tobacco for 12 h.…”

Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits

“…possessed by humans and animals [ 5 ]. Physiologically, learning is believed to accompany with structural changes of neural circuits in the brain and is frequently observed to associate with the emergence of gamma band oscillations [ 6 – 8 ]. Through learning processes, structural clusters are formed in neural networks in both the prefrontal cortex and hippocampus, which are behaviorally relevant for the recall of short-term memory and the later consolidation of long-term memory [ 9 , 10 ].…”

“…Although it is commonly accepted that gamma oscillations emerge during learning, their relation is not yet clear. First, the mechanism underlying gamma oscillation in neural circuits is still under active debate [ 8 , 11 – 15 ]. Second, the learning process in the neural circuit level is often phenomenally explained through plasticity mechanism [ 16 – 18 ], while plasticity mechanisms in neural circuits are diverse and their effects are elusive.…”