Enhancement of information transmission with stochastic resonance in hippocampal CA1 neuron models

Enhancement of information transmission with stochastic resonance in hippocampal CA1 neuron network

Stochastic resonance (SR) has been shown to improve the detection of subthreshold neural signals in uncorrelated noise. It is yet unclear if and how interactions within a population of neurons can improve information processing in neural networks. In this paper, we investigate the effect of the number of neurons on information transmission in an array of hippocampal CA1 neuron models, i.e., array-enhanced SR (AESR). In computer simulations, the sub-threshold synaptic current (signal) generated by a filtered homogeneous Poisson process was applied to a distal position in each of the apical dendrites, while the background synaptic currents (uncorrelated noise) were presented to a proximal or middle point in each of the dendrites. The transmembrane potentials were recorded at one of the somas in the array of CA1 neuron models, in order to find spike firings and likewise to estimate the total and noise entropies calculated from those spike firing times. The results show that the information rate estimated at the population of the CA1 neuron models is maximized at a specific amplitude of uncorrelated noise, implying AESR. The results further show that the maximum information rate is increased as the number of neurons is increased. It is concluded that AESR can be an important role in information processing is neural systems and that the AESR is modulated by the number of neurons within the network.

Enhancement of information transmission with stochastic resonance in hippocampal CA1 neuron models: effects of noise input location

Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we investigate the effects of the location of background noise input on information transmission in a hippocampal CA1 neuron model. In the computer simulation, random sub-threshold spike trains (signal) generated by a filtered homogeneous Poisson process were presented repeatedly to the middle point of the main apical branch, while the homogeneous Poisson shot noise (background noise) was applied to a location of the dendrite in the hippocampal CA1 model consisting of the soma with a sodium, a calcium, and five potassium channels. The location of the background noise input was varied along the dendrites to investigate the effects of background noise input location on information transmission. The computer simulation results show that the information rate reached a maximum value for an optimal amplitude of the background noise amplitude. It is also shown that this optimal amplitude of the background noise is independent of the distance between the soma and the noise input location. The results also show that the location of the background noise input does not significantly affect the maximum values of the information rates generated by stochastic resonance.