Cross-modal and scale-free action representations through enaction.

Embodied action representation and action understanding are the first steps to understand what it means to communicate. We present a biologically plausible mechanism to the representation and the recognition of actions in a neural network with spiking neurons based on the learning mechanism of spike-timing-dependent plasticity (STDP). We show how grasping is represented through the multi-modal integration between the vision and tactile maps across multiple temporal scales. The network evolves into a small-world organization with scale-free dynamics promoting efficient inter-modal binding of the neural assemblies with accurate timing. Finally, it acquires the qualitative properties of the mirror neuron system to trigger an observed action performed by someone else.

Information transfer at multiple scales.

In the study of complex systems a fundamental issue is the mapping of the networks of interaction between constituent subsystems of a complex system or between multiple complex systems. Such networks define the web of dependencies and patterns of continuous and dynamic coupling between the system's elements characterized by directed flow of information spanning multiple spatial and temporal scales. Here, we propose a wavelet-based extension of transfer entropy to measure directional transfer of information between coupled systems at multiple time scales and demonstrate its effectiveness by studying (a) three artificial maps, (b) physiological recordings, and (c) the time series recorded from a chaos-controlled simulated robot. Limitations and potential extensions of the proposed method are discussed.