Classifying and Predicting Dynamics with Bioinspired Machine Learning

Abstract

Artificial neural networks train by finding optimal weights, which are the strengths of connections between neuronal nodes. These optimal values are found by minimizing the error between the neural network’s output and what we know should be the true output for given inputs used during training. However, much of the real world is governed by non-stationary dynamics, wherein the underlying statistics of the dynamics drift in time in an arbitrary way. When an artificial neural network (ANN) is trained with a loss function that is uninformed of the different probability spaces present in the training data (e.g., through contextual tokens), the ANN will treat the entire data as if it had come from one stationary probability distribution—in effect, as one ‘event’—and cannot differentiate between the different dynamics without instruction. While this outstanding problem has motivated the study of adaptive dynamical networks, where biological learning rules influence the network weights, the monotonic nature of these rules (that is, where weights increase or decrease monotonically to converge to some local minimum of the loss), including backpropagation, poses yet another problem: the loss of stability. Yet the living neural networks of brains can rapidly infer contextual changes in real-time, adapt their behavior in accordance with this new environment, and even induce how the organism should act in an unencountered environment. In this thesis, I introduce a learning paradigm that associates learning with the coordination of oscillations of link strength. The paradigm is inspired by the physics of oscillatory rhythms of the mechanical structures that support synapses. I find that it yields rapid adaptation and learning in neural networks while maintaining robustness. Links can rapidly change their coordination of oscillations, endowing the network with the ability to sense subtle context changes in an unsupervised manner. In other words, the network generates the missing contextual tokens required to perform as a generalist AI architecture, capable of predicting dynamics in multiple contexts. Furthermore, the oscillations themselves allow the network to extrapolate dynamics to never-seen-before contexts. My oscillation-based learning paradigm provides a starting point for novel models of learning and cognition. Because it is agnostic to the specific details of the neural network architecture, our study also opens the door for introducing rapid adaptation and learning capabilities into leading AI models.