Publication

Abstract : Biological systems in general are information processing units since they receive stimuli, process information, and adapt to the demands of the environment. The dynamics of such systems are regulated on different spatiotemporal scales using the rich set of information inherently available. Various attempts have been made to measure and quantify the information processing capabilities of such systems. The current work uses entropy-based estimations to quantify the information content of one of the most complex biological systems-the nervous system. The brain’s capacity to adjust to new knowledge is known as plasticity. Synapses are the bridge between neurons that help neurons to communicate with each other. The transformations that occur at the synapses are known as Synaptic plasticity. Long-term potentiation (LTP) and long-term depression (LTD), the two basic types of synaptic plasticity, are biochemical mechanisms associated with learning and memory. This study analyses the activities in neurons during LTP and LTD conditions with the help of information-theoretical estimations performed on the spike train. The spike trains are recorded from the computational model of a neuron during simulated LTP and LTD conditions. Information-theoretic estimations like Entropy and Mutual information (MI) were calculated from the spike trains during LTP, Normal, and LTD conditions. In the analysis, MI in the LTP state was found to be considerably higher than in the LTD state, indicating an increase in activity in the neuron during the LTP state. This study also gave insight into the optimal window size for subsequent analysis of the spike trains.