Form Of Spike Trains Research Articles

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network.

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested.

Bio-inspired Tactile FA-I Spiking Generation under Sinusoidal Stimuli

For the design and development of advanced prosthetic limbs, many attempts have been made to restore the function of mechanoreceptors using artificial tactile sensors. Mechanoreceptors in human skin, which make dexterous manipulation possible, respond to the mechanical stimuli in the form of spike trains. In this paper, a bio-inspired approach to replicate the Fast Adapting type I (FA-I) mechanoreceptor is developed, where piezoelectric materials, such as polyvinylidene difluoride (PVDF) films, are used to generate continuous analog electrical signals; then the analog signals are successfully converted into spike trains using the spiking neuron model. By comparing with spike trains measured from the glabrous skin of macaque monkeys, it was found that this approach can mimic FA-I afferent spiking activities in terms of both the average inter-spike interval and the first spike latency. Spike features of the FA-I mechanoreceptors, such as the variability, frequency dependent responses, and population activity, were also explored, which may play a vital role in the understanding of the functionality of FA-I mechanoreceptors and the development of advanced prosthetic limbs.

Homogenous chaotic network serving as a rate/population code to temporal code converter.

At present, it is obvious that different sections of nervous system utilize different methods for information coding. Primary afferent signals in most cases are represented in form of spike trains using a combination of rate coding and population coding while there are clear evidences that temporal coding is used in various regions of cortex. In the present paper, it is shown that conversion between these two coding schemes can be performed under certain conditions by a homogenous chaotic neural network. Interestingly, this effect can be achieved without network training and synaptic plasticity.

Studying spike trains using a van Rossum metric with a synapse-like filter

Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.