ArticleCognitive neurodynamics2016
Bursting dynamics remarkably improve the performance of neural networks on liquid computing.
Article in Cognitive neurodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Chaotic time series prediction using phase space reconstruction based conceptor network.Cognitive neurodynamics · 2020Article
- Effects of synaptic integration on the dynamics and computational performance of spiking neural network.Cognitive neurodynamics · 2020Article
- Energy expenditure computation of a single bursting neuron.Cognitive neurodynamics · 2019Article
- A decision-making model based on a spiking neural circuit and synaptic plasticity.Cognitive neurodynamics · 2017Article
- A plausible neural circuit for decision making and its formation based on reinforcement learning.Cognitive neurodynamics · 2017Article
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3 authors.
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Abstract
Burst firings are functionally important behaviors displayed by neural circuits, which plays a primary role in reliable transmission of electrical signals for neuronal communication. However, with respect to the computational capability of neural networks, most of relevant studies are based on the spiking dynamics of individual neurons, while burst firing is seldom considered. In this paper, we carry out a comprehensive study to compare the performance of spiking and bursting dynamics on the capability of liquid computing, which is an effective approach for intelligent computation of neural networks. The results show that neural networks with bursting dynamic have much better computational performance than those with spiking dynamics, especially for complex computational tasks. Further analysis demonstrate that the fast firing pattern of bursting dynamics can obviously enhance the efficiency of synaptic integration from pre-neurons both temporally and spatially. This indicates that bursting dynamic can significantly enhance the complexity of network activity, implying its high efficiency in information processing.
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