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Register a new userInput-output behaviour of a model neuron with alternating drift
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Added by
Elvira Di Nardo Prof.
Publication date
2021
fields
Biology
and research's language is
English
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The input-output behaviour of the Wiener neuronal model subject to alternating input is studied under the assumption that the effect of such an input is to make the drift itself of an alternating type. Firing densities and related statistics are obtained via simulations of the sample-paths of the process in the following three cases: the drift changes occur during random periods characterized by (i) exponential distribution, (ii) Erlang distribution with a preassigned shape parameter, and (iii) deterministic distribution. The obtained results are compared with those holding for the Wiener neuronal model subject to sinusoidal input
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Simulating and imitating the neuronal network of humans or mammals is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by neuronal conduction characteristics in the primary visual cortex of cats, pulse-coupled neural networks (PCNNs) can exhibit synchronous oscillation behavior, which can process digital images without training. However, according to the study of single cells in the cat primary visual cortex, when a neuron is stimulated by an external periodic signal, the interspike-interval (ISI) distributions represent a multimodal distribution. This phenomenon cannot be explained by all PCNN models. By analyzing the working mechanism of the PCNN, we present a novel neuron model of the primary visual cortex consisting of a continuous-coupled neural network (CCNN). Our model inherited the threshold exponential decay and synchronous pulse oscillation property of the original PCNN model, and it can exhibit chaotic behavior consistent with the testing results of cat primary visual cortex neurons. Therefore, our CCNN model is closer to real visual neural networks. For image segmentation tasks, the algorithm based on CCNN model has better performance than the state-of-art of visual cortex neural network model. The strength of our approach is that it helps neurophysiologists further understand how the primary visual cortex works and can be used to quantitatively predict the temporal-spatial behavior of real neural networks. CCNN may also inspire engineers to create brain-inspired deep learning networks for artificial intelligence purposes.
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