Reliable storage and recall of aperiodic spatiotemporal activity patterns using scaffolded attractors

Spatiotemporal patterns of neural activity have increasingly come to be seen as important for encoding information in the nervous system, motivating the development of various neurocomputational models. In this paper, we present a simple recurrent neural network model motivated by the need to understand the basis of voluntary motor control. For a given individual, any specific voluntary movement is ultimately encoded as an aperiodic spatiotemporal pattern of activation across a set of muscles, and presumably in spinal and cortical, motor neurons. Over time, such patterns can become stereotypical for the individual, and determine the “style” of specific movements - e.g., how they walk or write an “A”. Experimental studies also indicate that these activity patterns may themselves be constructed as linear combinations of a few fixed spatiotemporal basis patterns of activity called motor synergies. For this to work, it is essential that neural systems be able to represent spatiotemporal activity patterns that are stimulus-specific, aperiodic (i.e., not rhythmic), transient (i.e., lasting only briefly), and robust (i.e., at least somewhat tolerant of errors and noise). The model we describe achieves this by using the dynamics of a recurrent neural network with two classes of primary neurons: Fast neurons that rapidly identify the patterns to be produced based on the stimulus and set up a “scaffolding” for it; and slow neurons that eventually instantiate the relevant spatiotemporal activity pattern. We show that this minimal system exhibits many of the properties needed for the flexible construction of complex, aperiodic movements.