Advances in Electrical Engineering and Computational Science

Volume 39 of the series Lecture Notes in Electrical Engineering pp 681-692

Spiking Neurons and Synaptic Stimuli: Neural Response Comparison Using Coincidence-Factor

  • Mayur SarangdharAffiliated withDepartment of Computer Science, University of Hull
  • , Chandrasekhar KambhampatiAffiliated withDepartment of Computer Science, University of Hull

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In this chapter, neural responses are generated by changing the Inter-Spike-Interval (ISI) of the stimulus. These responses are subsequently compared and a coincidence factor is obtained. Coincidence-factor, a measure of similarity, is expected to generate a high value for higher similarity and a low value for dissimilarity. It is observed that these coincidence-factors do not have a consistent trend over a simulation time window. Also, the lower-bound limit for faithful behaviour of coincidence factor shifts towards the right with the increase in the reference ISI of the stimulus. In principle, if two responses have a very high similarity, then their respective stimuli should be very similar and could possibly be considered the same. However, as results show, two spike trains generated by highly-varying stimuli have a high coincidence-factor. This is due to limitations imposed by the one-dimensional comparison of coincidence-factor.


Spiking Neuron Synaptic Stimuli Neural Response Coincidence-Factor Inter-Spike-Interval