RESUMO
Closed-loop activity-dependent stimulation is a powerful methodology to assess information processing in biological systems. In this context, the development of novel protocols, their implementation in bioinformatics toolboxes and their application to different description levels open up a wide range of possibilities in the study of biological systems. We developed a methodology for studying biological signals representing them as temporal sequences of binary events. A specific sequence of these events (code) is chosen to deliver a predefined stimulation in a closed-loop manner. The response to this code-driven stimulation can be used to characterize the system. This methodology was implemented in a real time toolbox and tested in the context of electric fish signaling. We show that while there are codes that evoke a response that cannot be distinguished from a control recording without stimulation, other codes evoke a characteristic distinct response. We also compare the code-driven response to open-loop stimulation. The discussed experiments validate the proposed methodology and the software toolbox.
RESUMO
A discrete model of an ensemble of identical stochastic integrate-and-fire neurons is used to study the patterns of activity in populations of neurons that exchange excitatory messages. In a regime with small interactions among the units, the effect of the message exchange is to reduce the dispersion of the firing period of the individual neurons. In a strong interaction regime, a number of activity clusters emerge in the ensemble. Neurons in each cluster fire periodically and in synchrony with each other. The number of these self-sustained firing states characterized by distinct firing patterns towards which the network can evolve is very large. Because of their stability with respect to intrinsic fluctuations in the dynamics of the stochastic neurons, these states could, in principle, be used to encode and process large amounts of information.
