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Even if the computational mechanisms outlined in this work are general and apply to both living and model neurons, and might be relevant even to non-neural systems with history-dependent dynamics [60], the detailed analysis were carried out with linear models with one (in the case of the IF neuron) or two (for the GIF neuron) time scales.
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In practice, due to the finite speed of information processing and the inherent communication time of neurons, time delays are frequently encountered in many biological and artificial neural networks.
To obtain embryonic cortical neurons, time-mated Wistar rats were euthanized by CO2 and cervical dislocation.
To account for synaptic delays between neurons, the time bin of neuron J was delayed by d bins, as described below.
x i ( t ) corresponds to the state of the i th neuron at time t.
Oscillatory networks are a special class of neural networks where each neuron exhibits time periodic behavior.
Additionally, the consideration of these mechanics deserves a spatially extended modeling of the neuron, with time delays.
This effect is more pronounced at low input rates, because the model neuron has time to converge almost completely to its rest state in between synaptic events.
(B ) Maximal intensity of EGFRGFP in filopodia within one neuron over time (2 min), before and after treatment (indicated by dotted line) with Dyngo.
u(t) and y(t) are used to represent, respectively, the net input and the output of a given neuron at time t.
In the presence of IFNalpha, we observed an increase of beta-galactosidase positive infected neurons over time to about 50% positive neurons at 5dpi (Fig. 3C).
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