Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot.

The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature.

A computational model for the modulation of the prepulse inhibition of the acoustic startle reflex.

The acoustic startle reflex (ASR), a defensive response, is a contraction of the skeletal and facial muscles in response to an abrupt, intense (>80 db) auditory stimulus, which has been extensively studied in rats and humans. Prepulse inhibition (PPI) of ASR is the normal suppression of the startle reflex when an intense stimulus is preceded by a weak non-starting pre-stimulus. PPI, a measure of sensory motor gating, is impaired in various neuropsychiatric disorders, including schizophrenia, and is modulated by cognitive and emotional contexts such as fear and attention.