Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches.

Models that describe qualitatively and quantitatively the activity of entire groups of spiking neurons are becoming increasingly important for biologically realistic large-scale network simulations. At the systems and areas modeling level, it is necessary to switch the basic descriptional level from single spiking neurons to neuronal assemblies. In this article, we present and review work that allows a macroscopic description of the assembly activity. We show that such macroscopic models can be used to reproduce in a quantitatively exact manner the joint activity of groups of spike-response or integrate-and-fire neurons. We also show that integral as well as differential equation models of neuronal assemblies can be understood within a single framework, which allows a comparison with the commonly used assembly-averaged graded-response type of models. The presented framework thus enables the large-scale neural network modeler to implement networks using computational units beyond the single spiking neuron without losing much biological accuracy. This article explains the theoretical background as well as the capabilities and the implementation details of the assembly approach.