A STUDY OF BIOLOGICALLY PLAUSIBLE NEURAL NETWORK: THE ROLE AND INTERACTIONS OF BRAIN-INSPIRED MECHANISMS IN CONTINUAL LEARNING

Abstract

Humans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic forgetting. There are considerable differences in the complexity of synapses, the processing of information, and the learning mechanisms in biological neural networks and their artificial counterpart, which may explain the mismatch in performance. We consider a biologically plausible framework that constitutes separate populations of exclusively excitatory and inhibitory neurons which adhere to Dale's principle, and the excitatory pyramidal neurons are augmented with dendritic-like structures for context-dependent processing of stimuli. We then conduct a comprehensive study on the role and interactions of different mechanisms inspired by the brain including sparse non-overlapping representations, Hebbian learning, synaptic consolidation, and replay of past activations that accompanied the learning event. Our study suggests that the employing of multiple complementary mechanisms in a biologically plausible architecture, similar to the brain, may be effective in enabling continual learning in ANNs.

1. INTRODUCTION

The human brain excels at continually learning from a dynamically changing environment whereas standard artificial neural networks (ANNs) are inherently designed for training from stationary i.i.d. data. Sequential learning of tasks in continual learning (CL) violates this strong assumption, resulting in catastrophic forgetting. Although ANNs are inspired by biological neurons (Fukushima, 1980) , they omit numerous details of design principles and learning mechanisms in the brain. These fundamental differences may account for the mismatch in performance and behavior. Biological neural networks are characterized by considerably more complex synapses and dynamic context-dependent processing of information. Also, the individual neurons have a specific role. Each presynaptic neuron has an exclusive excitatory or inhibitory impact on its postsynaptic partners, as postulated by Dale's principle (Strata et al., 1999) . Furthermore, distal dendritic segments in pyramidal neurons, which comprises the majority of excitatory cells in the neocortex, receive additional context information and enable context-dependent processing of information. This, in conjunction with inhibition, allows the network to learn task-specific patterns and avoid catastrophic forgetting (Yang et al., 2014; Iyer et al., 2021; Barron et al., 2017) . Furthermore, the replay of nonoverlapping and sparse neural activities of previous experiences in the neocortex and hippocampus is considered to play a critical role in memory formation, consolidation, and retrieval (Walker & Stickgold, 2004; McClelland et al., 1995) . To protect information from erasure, the brain employs synaptic consolidation in which plasticity rates are selectively reduced in proportion to strengthened synapses (Cichon & Gan, 2015) . Thus, we study the role and interactions of different mechanisms inspired by the brain in a biologically plausible framework in a CL setup. The underlying model constitutes separate popula-1 We will make the code available upon acceptance. 1

