Evolved motor primitives and sequences in a hierarchical recurrent neural network

Abstract. This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot. Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems. A further experiment tests the evolved controller’s adaptability to changes in its environment. The biological plausibility of the model’s essential principles is discussed.

Differences in the emergent coding properties of cortical and striatal ensembles

The function of a given brain region is often defined by the coding properties of its individual neurons, yet how this information is combined at the ensemble level is an equally important consideration. We recorded multiple neurons from the anterior cingulate cortex (ACC) and the dorsal striatum (DS) simultaneously as rats performed different sequences of the same three actions. Sequence and lever decoding was markedly similar on a per-neuron basis in the two regions. At the ensemble level, sequence-specific representations in the DS appeared synchronously, but transiently, along with the representation of lever location, whereas these two streams of information appeared independently and asynchronously in the ACC. As a result, the ACC achieved superior ensemble decoding accuracy overall. Thus, the manner in which information was combined across neurons in an ensemble determined the functional separation of the ACC and DS on this task