Temporal structure of neural activity and modelling of information processing in the brain
Emergent neural computational architectures based on neuroscience
Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Controlling neurological disease at the edge of instability
Quantitative neuroscience
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If an oscillating neural circuit is forced by another suchcircuit via a composite signal, the phase lag induced by theforcing can be changed by changing the relative strengths ofcomponents of the coupling. We consider such circuits, with theforced and forcing oscillators receiving signals with some givenphase lag. We show how such signals can be transformed into analgorithm that yields connection strengths needed to produce thatlag. The algorithm reduces the problem of producing a given phaselag to one of producing a kind of synchrony with a "teaching"signal; the algorithm can be interpreted as maximizing thecorrelation between voltages of a cell and the teaching signal. Weapply these ideas to regulation of phase lags in chains ofoscillators associated with undulatory locomotion.