Building Neurocognitive Networks with a Distributed Functional Architecture

Abstract

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

Time Scale Hierarchies in the Functional Organization of Complex Behaviors

“…This vision is in line with reports of network dynamics, dynamical models as well as biological data indicating that the ensemble dynamics of populations of neurons may effectively reduce to a structured flow in phase space (i.e., a functional mode). For instance, recent as well as ongoing work in our lab progresses in designing large scale neural networks of firing rate populations or spiking neurons coding for SFMs and functional architectures [41], [75], [76]. Other (computational) examples in which a network dynamics generates low-dimensional topological objects in phase space are provided in [95].…”

“…We first briefly review the formulation of Structured Flows on Manifolds (SFM) [41], [42], [75], [76] which readswhere the so-called ‘smallness’ parameter µ is constrained as 0< µ <<1, g (.) defines the manifold, f (.)…”

“…The former provide the necessary non-linearities whereas the latter allow for the emergence of the flow on the manifold. Ongoing work [75], [76] attempts to encode SFM into networks of spiking neural populations.…”

“…Such a functional mode decomposition based on a competition scheme follows previous work on the Synergetic Computer [28], and is well established in the literature of biological competition [27]. An alternative to the WTA competition could be the winner-less competition based on transient heteroclinic sequences [52], [53], as also used in [75].…”

The Rediscovery of Slowness: Exploring the Timing of Cognition

“…The separation of timescales of neural functioning has also been put forward in modern computational theories of information processing in human behaviour [7][8][9] and brain disorders such as epilepsy [10].…”