Effects of phase on homeostatic spike rates

Abstract

Recent experimental results by Talathi et al. (Neurosci Lett 455:145-149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.

Interaction between connectivity and oscillatory currents in a heterogeneous neuronal network

“…Here significant enhancement is obtained throughout most the entire range of driving frequencies and the mean phase coherence increase is shifted towards higher frequencies ( Figure 12), with no activity ratio peaks at specific frequencies.This suggests that a mere change of balance between excitatory and inhibitory currents due to formation of additional synapses [53] or background spikes rates [47], cannot generate the same results.…”

“…At the same time it also allows the enhanced region for higher activity leading then to stronger inhibition and more thorough shutdown of other network regions 9A. Recent computational work by Fisher et al [47] has shown that target spiking rates can be achieved in networks with different balances of excitatory and inhibitory neurons. Such a system might also respond variably to different input frequencies, as our result suggests.…”

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

“…For example, in biomedicine, vast electroencephalogram (EEG) or electrocorticogram (ECoG) data are available for the analysis, detection, and possibly prediction of epileptic seizures (e.g., Refs. [9][10][11][12][13][14][15][16]). In a modern infrastructure viewed as a complex dynamical system, large scale sensor networks can be deployed to measure a number of physical signals to monitor the behaviors of the system in continuous time [17][18][19].…”

Phase shift in hippocampal circadian rhythm during the latent period of epileptic rats

“…Recent experiments by our group in a chronic limbic epilepsy animal model have lead to the hypothesis that epilepsy following brain injury may emerge as a circadian disorder [1,2]. Specifically, we extracted two distinct classes of population spikes (PS) from CA1 local field potential data.…”