Title: A Parsimonious Dynamical Model for Structural Learning in the Human Brain,
Abstract: The human brain is capable of diverse feats of intelligence. A particularly
salient example is the ability to deduce structure from time-varying auditory
and visual stimuli, enabling humans to master the rules of language and to
build rich expectations of their physical environment. The broad relevance of
this ability for human cognition motivates the need for a first-principles
model explicating putative mechanisms. Here we propose a general framework for
structural learning in the brain, composed of an evolving, high-dimensional
dynamical system driven by external stimuli or internal processes. We
operationalize the scenario in which humans learn the rules that generate a
sequence of stimuli, rather than the exemplar stimuli themselves. We model
external stimuli as seemingly disordered chaotic time series generated by
complex dynamical systems; the underlying structure being deduced is then that
of the corresponding chaotic attractor. This approach allows us to demonstrate
and theoretically explain the emergence of five distinct phenomena reminiscent
of cognitive functions: (i) learning the structure of a chaotic system purely
from time series, (ii) generating new streams of stimuli from a chaotic system,
(iii) switching stream generation among multiple learned chaotic systems,
either spontaneously or in response to external perturbations, (iv) inferring
missing data from sparse observations of the chaotic system, and (v)
deciphering superimposed input from different chaotic systems. Numerically, we
show that these phenomena emerge naturally from a recurrent neural network of
Erdos-Renyi topology in which the synaptic strengths adapt in a Hebbian-like
manner. Broadly, our work blends chaotic theory and artificial neural networks
to answer the long standing question of how neural systems can learn the
structure underlying temporal sequences of stimuli.