The Role of Synchrony in Neocortical Processing and Synaptic Plasticity

Spiking neural networks for cortical neuronal spike train decoding

Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.

Epileptiform spikes desynchronize and diminish fast (gamma) activity of the brain. An "anti-binding" mechanism?

Fast (20-100 Hz) rhythms of electrical activity of the brain have been suggested to be important for perception and cognition providing a mechanism for temporal binding of neural activities underlying mental representations. Also, fast rhythms often precede epileptiform discharges in patients and some experimental models. Generalized slow (2-3 Hz) spike activity after systemic kainic acid (KA) in the rat has been shown to be preceded by intense gamma activity. A relationship between the intensified gamma rhythms and the subsequent spike activity was studied during kainate-induced acute epileptogenesis. Power, multiple coherence and phase were analyzed at frequencies 1-100 Hz in the EEG recorded from the hippocampal-neocortical structures of the rat. Gamma rhythms, extremely intense and highly coherent at the onset of discharges, were followed by a slow rhythm of epileptiform spikes/sharp waves. During this spike activity and immediately afterwards, the gamma power and coherence were significantly decreased. These data show an antagonism between gamma rhythms and spike activity and ability of the latter to desynchronize and suppress the former. They are supportive to the hypothesis that epileptiform spike activity may result from the extreme activation of the "anti-binding" mechanism controlling temporal binding at high frequencies. It is suggested that when fast activity is abnormally intensified, "over-binding" with global synchrony of gamma rhythms can occur in the neural networks. It may lead to inadequate synaptic modifications. To prevent this process, epileptiform discharge develops as a protective mechanism suppressing fast activity. This proposal has implications for our understanding of temporal binding in the brain and how its excessive activation may precipitate the development of pathological states.