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Francisco B Rodríguez

Publications and source records attributed to Francisco B Rodríguez.

2 recordsLinked to original sources

Neural signatures: multiple coding in spiking-bursting cells.

Recent experiments have revealed the existence of neural signatures in the activity of individual cells of the pyloric central pattern generator (CPG) of crustacean. The neural signatures consist of cell-specific spike timings in the bursting activity of the neurons. The role of these intraburst neural fingerprints is still unclear. It has been reported previously that some muscles can reflect small changes in the spike timings of the neurons that innervate them. However, it is unclear to what extent neural signatures contribute to the command message that the muscles receive from the motoneurons. It is also unknown whether the signatures have any functional meaning for the neurons that belong to the same CPG or to other interconnected CPGs. In this paper, we use realistic neural models to study the ability of single cells and small circuits to recognize individual neural signatures. We show that model cells and circuits can respond distinctly to the incoming neural fingerprints in addition to the properties of the slow depolarizing waves. Our results suggest that neural signatures can be a general mechanism of spiking-bursting cells to implement multicoding.

Action Potentials↗

Analysis of perfect mappings of the stimuli through neural temporal sequences.

The analysis of an optimal neural system that maps stimuli into unique sequences of activations of fundamental atoms or functional clusters (FCs) is carried out. We say that it is perfect because the system maps with an injective function every stimulus in minimum time with the least number of FCs, such that every FC is activated only once. The neural system has the possibility to sustain several sequences in parallel. In this framework, we study the capacity achievable by the system, minimal completion time and complexity in terms of the number of parallel sequences. We show that the maximum capacity of the system is achieved without using parallel sequences at the expense of long completion times. However, when the capacity value is fixed, the largest possible number of parallel sequences is optimal because it requires short completion times. The complexity measure adds to important points: (i) the largest complexity of the system is achieved without parallel sequences, and (ii) the capacity estimation is a good estimation of the complexity of the system.

Algorithms↗