Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task

Jie Mei; Rouzbeh Meshkinnejad; Yalda Mohsenzadeh

doi:10.1016/j.isci.2023.106026

Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task

iScience. 2023 Jan 23;26(2):106026. doi: 10.1016/j.isci.2023.106026. eCollection 2023 Feb 17.

Authors

Jie Mei^{1

2}, Rouzbeh Meshkinnejad^{2

3}, Yalda Mohsenzadeh^{1

2

3}

Affiliations

¹ Western Institute for Neuroscience, University of Western Ontario, London, ON N6A 5B7, Canada.
² Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.
³ Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada.

Abstract

In recent years, the biological underpinnings of adaptive learning have been modeled, leading to faster model convergence and various behavioral benefits in tasks including spatial navigation and cue-reward association. Furthermore, studies have investigated how the neuromodulatory system, a major driver of synaptic plasticity and state-dependent changes in the brain neuronal activities, plays a role in training deep neural networks (DNNs). In this study, we extended previous studies on neuromodulation-inspired DNNs and explored the effects of neuromodulatory components on learning and single unit activities in a spatial learning task. Under the multiscale neuromodulatory framework, plastic components, dropout probability modulation, and learning rate decay were added to the single unit, layer, and whole network levels of DNN models, respectively. We observed behavioral benefits including faster learning and smaller error of ambulation. We then concluded that neuromodulatory components can affect learning trajectories, outcomes, and single unit activities, in a component- and hyperparameter-dependent manner.

Keywords: Behavioral neuroscience; Neural networks.