Influence of the precise timing of each spike on a frequential neuron

Using the spike pattern represented in B and applying a
random noise filter of maximum ±3ms gave the
configuration represented by D. This filter involved the
random modification of the firing times within a maximum
range of 3ms before or after the original spike timing. The
total number of spikes remained constant and hence also the
average firing rate. Fig 3E shows the response for this
particular input. The membrane potential increased gradually
until reaching a maximum of about 115mV above the resting
potential.

Frequential neuron, qualitative results.

I developed a C++ software to simulate the dendritic computation implemented by equations 1 to 5. This software simulates a single neuron with a single dendrite. This neuron receives inputs from a neuronal layer called receptive field (RF) neurons all of which are spiking point neurons. Using this software, we calculated the membrane potential at each position on the dendrite and at the soma for differing numbers of synapses, synaptic positions and spike input patterns.

The configuration tested first was that of a frequential neuron with an 80μm long dendrite, a passive propagation velocity: v=1 μm.ms-1, and one RF neuron as input projecting 20 regularly spaced synapses. Each synapse had a synaptic weight of 6 mV, a rise time constant of 1ms and a general time constant of 2ms.

A first spike train of 33 spikes was used to stimulate the dendrite computing (DC) neuron: B. C shows the response for this particular input. The membrane potential increased gradually until reaching a maximum at about 115mV above the resting potential that was arbitrarily set to 0mV.

Temporal neuron, qualitative results

11 RF neurons were used as the input, all projecting to the
same DC neuron with a total of 42 synapses on its dendrite.
The precise morphology of this network has been elaborated
according to the rules explained in section II.D and Fig 2. A
depicts the original input activity. Each line of the raster plot
represents the temporal activity of one of the 11 RF neurons.
The bottom-most line represents the sum of the 11 lines
above. A depicts the response of the DC neuron for this
input: the membrane activity remained relatively weak
during most of the input duration then increased sharply to a
maximum of 130.9 mV above the resting potential.