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The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

The brain correlates of words and their referent actions and objects appear to be strongly coupled neuron ensembles or assemblies distributed over defined cortical areas. In this work we describe the implementation of a cell assembly-based model of several visual, language, planning, and motor areas to enable a robot to understand and react to simple spoken commands. The essential idea is that different cortical areas represent different aspects of the same entity, and that the long-range cortico-cortical projections represent hetero-associative memories that translate between these aspects or representations.

We briefly review the long-standing ideas about the use of synchronicity in the brain, which rely on Donald Hebb's views on cell assemblies and synaptic plasticity. More recently the distinction among several timescales in the description of neural activity has become a focus of theoretical discussion. Phillips & Singer's target article is criticized mainly because it does not distinguish these timescales properly and hence does not really address the questions so intensely debated today.

Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.