Biomedical subjects

Amitabha Bose

Publications and source records attributed to Amitabha Bose.

4 recordsLinked to original sources

PubMed2004

Localized activity patterns in excitatory neuronal networks.

The existence of localized activity patterns, or bumps, has been investigated in a variety of spatially distributed neuronal network models that contain both excitatory and inhibitory coupling between cells. Here we show that a neuronal network with purely excitatory synaptic coupling can exhibit localized activity. Bump formation ensues from an initial transient synchrony of a localized group of cells, followed by the emergence of desynchronized activity within the group. Transient synchrony is shown to promote recruitment of cells into the bump, while desynchrony is shown to be good for curtailing recruitment and sustaining oscillations of those cells already within the bump. These arguments are based on the geometric structure of the phase space in which solutions of the model equations evolve. We explain why bump formation and bump size are very sensitive to initial conditions and changes in parameters in this type of purely excitatory network, and we examine how short-term synaptic depression influences the characteristics of bump formation.

Computer Simulation↗

PubMed2003

Contribution of synaptic depression to phase maintenance in a model rhythmic network.

In many rhythmic neuronal networks that operate in a wide range of frequencies, the time of neuronal firing relative to the cycle period (the phase) is invariant. This invariance suggests that when frequency changes, firing time is precisely adjusted either by intrinsic or synaptic mechanisms. We study the maintenance of phase in a computational model in which an oscillator neuron (O) inhibits a follower neuron (F) by comparing the dependency of phase on cycle period in two cases: when the inhibitory synapse is depressing and when it is nondepressing. Of the numerous ways of changing the cycle period, we focus on three cases where either the duration of the active state, the inactive state, or the duty cycle of neuron O remains constant. In each case, we measure the phase at which neuron F fires with respect to the onset of firing in neuron O. With a nondepressing synapse, this phase is generally a monotonic function of cycle period except in a small parameter range in the case of the constant inactive duration. In contrast, with a depressing synapse, there is always a parameter regime in which phase is a cubic function of cycle period: it decreases at short cycle periods, increases in an intermediate range, and decreases at long cycle periods. This complex shape for the phase-period relationship arises because of the interaction between synaptic dynamics and intrinsic properties of the postsynaptic neuron. By choosing appropriate parameters, the cubic shape of the phase-period curve results in a small variation in phase for a large interval of periods. Consequently, we find that although a depressing synapse does not produce perfect phase maintenance, in most cases it is superior to a nondepressing synapse in promoting a constant phase difference.

Models, Neurological↗

PubMed2002

Burst synchrony patterns in hippocampal pyramidal cell model networks.

Types of. mechanisms for and stability of synchrony are discussed in the context of two-compartment CA3 pyramidal cell and interneuron model networks. We show how the strength and timing of inhibitory and excitatory synaptic inputs work together to produce either perfectly synchronized or nearly synchronized oscillations, across different burst or spiking modes of firing. The analysis shows how excitatory inputs tend to desynchronize cells, and how common, slowly decaying inhibition can be used to synchronize them. We also introduce the concept of 'equivalent networks' in which networks with different architectures and synaptic connections display identical firing patterns.

Action Potentials↗

PubMedDate not supplied

The activity phase of postsynaptic neurons in a simplified rhythmic network.

Many inhibitory rhythmic networks produce activity in a range of frequencies. The relative phase of activity between neurons in these networks is often a determinant of the network output. This relative phase is determined by the interaction between synaptic inputs to the neurons and their intrinsic properties. We show, in a simplified network consisting of an oscillator inhibiting a follower neuron, how the interaction between synaptic depression and a transient potassium current in the follower neuron determines the activity phase of this neuron. We derive a mathematical expression to determine at what phase of the oscillation the follower neuron becomes active. This expression can be used to understand which parameters determine the phase of activity of the follower as the frequency of the oscillator is changed. We show that in the presence of synaptic depression, there can be three distinct frequency intervals, in which the phase of the follower neuron is determined by different sets of parameters. Alternatively, when the synapse is not depressing, only one set of parameters determines the phase of activity at all frequencies.

Animals↗