Inter-layer interactions of noise-driven neural network
No Thumbnail Available
Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
None
The role of neurons in the brain as an information processing system has attracted considerable attentions and motivated the development of various research techniques. Computationally simulating the interaction among neurons is very useful in investigating the complexity of a neural network. Developed neural networks may exhibit complex dynamic behaviors, such as synchronization and clustered synchronous firing. In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a nonsynchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, uniform, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.
The role of neurons in the brain as an information processing system has attracted considerable attentions and motivated the development of various research techniques. Computationally simulating the interaction among neurons is very useful in investigating the complexity of a neural network. Developed neural networks may exhibit complex dynamic behaviors, such as synchronization and clustered synchronous firing. In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a nonsynchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, uniform, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.
Description
Keywords
biological neural networks, inter-layer interactions, noise-driven synchronization, spike-timing-dependent plasticity, synchronous firing, computer simulation, developing neural networks, repair mechanism of neural networks, biological neural networks, inter-layer interactions, noise-driven synchronization, spike-timing-dependent plasticity, synchronous firing, computer simulation, developing neural networks, repair mechanism of neural networks