Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons

The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. © 2014 Toutounji and Pasemann

Evolving neurocontrollers for balancing an inverted pendulum

Evolving neurocontrollers for balancing an inverted pendulum b

Synchronized chaos and other coherent states for two coupled neurons

Synchronized chaos and other coherent states for two coupled neurons b

Structure and Dynamics of Recurrent Neuromodules

The article calls attention to complex dynamical phenomena in artificial neural systems, which are - as is asserted - of relevance also for understanding biological brain functions. Examples of various dynamical effects (hysteresis, oscillations, deterministic chaos, synchronization and coherence) are discussed in terms of the discrete dynamics of small recurrent networks. The relevance of a dynamical systems approach for understanding the emergence of higher level information processing or cognitive abilities of biological and artificial neural systems is discussed. submitted for publication. 1 Introduction Biological brains have a massively recurrent connectivity, i.e. there are very many directed closed signal processing loops on different scales of the brain: between single neurons as well as between hypercolumns and different brain areas which are functionally discernible. They involve excitatory as well as inhibitory connections. From the dynamical systems point of view, thi..