Publications

Abstract : Coordination in neural activity is thought to be a key element in propagation of information. Coordinated spiking in neurons is an effective signaling mechanism and helps in self-organization of the network over time. Using computational techniques, this study investigates the parameters affecting the coordinated activity of a population of granular layer neurons of the cerebellum. This study investigates the spike train coordination using two basic measures: spike correlations and spike synchrony. The values were quantified from modeled granule neurons of the cerebellum under various excitatory and inhibitory synaptic balances and under induced plasticity states. Increased excitation from mossy fibers (MFs) increased the output correlation of granule neurons, while increased inhibition from Golgi neuron reduced the output correlations. Synchronous firing of granular layer population under in-vivo like inputs was increased with the increase in excitation and inhibition. Synchrony was also estimated under different induced plasticity states of the modeled neurons. The firing was more synchronous under the long-term potentiation (LTP) state compared to control state and was more asynchronous under long-term depression (LTD) state compared to control. Since plasticity signifies the learning activity in the neurons and is affected by temporal coordination in spiking, these results have greater relevance in understanding the nature of neural code.