Abstract
This paper analyzes the dynamics of the cold receptor neural network model. First, it examines noise effects on neuronal stimulus in the model. FromISIplots, it is shown that there are considerable differences between purely deterministic simulations and noisy ones. TheISI-distance is used to measure the noise effects on spike trains quantitatively. It is found that spike trains observed in neural models can be more strongly affected by noise for different temperatures in some aspects; meanwhile, spike train has greater variability with the noise intensity increasing. The synchronization of neuronal network with different connectivity patterns is also studied. It is shown that chaotic and high period patterns are more difficult to get complete synchronization than the situation in single spike and low period patterns. The neuronal network will exhibit various patterns of firing synchronization by varying some key parameters such as the coupling strength. Different types of firing synchronization are diagnosed by a correlation coefficient and theISI-distance method. The simulations show that the synchronization status of neurons is related to the network connectivity patterns.
Highlights
It is expected that the processed neural information can be encoded in the structure of inter-spike-interval (ISI) series; that is, the neural firing activities can be represented by the patterns of neural spike trains [1,2,3,4]
With the addition of noise, the neural model can generate a variety of new different patterns compared to deterministic situation; synchronization status of neural network with different connection types is investigated, and the simulations show that the synchronization status is much related to the network connectivity patterns
For a qualitative computing of noise effect, the ISI distance is introduced to characterize how noise affects the variability of the neural spike train
Summary
It is expected that the processed neural information can be encoded in the structure of inter-spike-interval (ISI) series; that is, the neural firing activities can be represented by the patterns of neural spike trains [1,2,3,4]. With the addition of noise, it was observed there were a variety of spike train patterns in temperature sensitive skin receptors for information encoding [13, 14]. In this paper, ISI distance, a new method recently introduced by Kreuz et al [35, 36], is used to characterize effects of noise in a Hodgkin-Huxley type model of temperature encoding and synchronization. This is a simple approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous firing rates, which is complementary to the spike-based approaches.
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