Recent experimental and theoretical studies show that energy efficiency, which measures the amount of infor- mation processed by a neuron with per unit of energy consumption, plays an important role in the evolution o...Recent experimental and theoretical studies show that energy efficiency, which measures the amount of infor- mation processed by a neuron with per unit of energy consumption, plays an important role in the evolution of neural systems. Here we calculate the information rates and energy efficieneies of the Hodgkin-Huxley （HH） neuron model at different temperatures in a noisy environment. It is found that both the information rate and energy efficiency are maximized by certain temperatures. Though the information rate and energy efficiency cannot be maximized simultaneously, the neuron holds a high information processing capacity at the tempera- ture corresponding to the maximal energy efficiency. Our results support the idea that the energy efficiency is a selective pressure that influences the evolution of nervous systems.展开更多
The map-based neuron models have received attention as valid phenomenological neuron models due to their computational efficiency and flexibility to generate rich patterns. Here we evaluate the information capacity an...The map-based neuron models have received attention as valid phenomenological neuron models due to their computational efficiency and flexibility to generate rich patterns. Here we evaluate the information capacity and transmission of the Courbage-Nekorkin Vdovin （CNV） map-based neuron model with a bursting and tonic firing mode in response to external pulse inputs, in both temporal and rate coding schemes. We find that for both firing modes, the CNV model could capture the essential behavior of the stochastic Hodgkin-Huxley model in information transmission for the temporal coding scheme, with regard to the dependence of total entropy, noise entropy, information rate, and coding efficiency on the strength of the input signal. However, in tonic firing mode, it fails to replicate the input strength-dependent information rate in the rate coding scheme. Our results suggest that the CNV map-based neuron model could capture the essential behavior of information processing of typical conductance-based neuron models.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11105062,11275003,11265014 and 11275084the Fundamental Research Funds for the Central Universities under Grant No LZUJBKY-2015-119
文摘Recent experimental and theoretical studies show that energy efficiency, which measures the amount of infor- mation processed by a neuron with per unit of energy consumption, plays an important role in the evolution of neural systems. Here we calculate the information rates and energy efficieneies of the Hodgkin-Huxley （HH） neuron model at different temperatures in a noisy environment. It is found that both the information rate and energy efficiency are maximized by certain temperatures. Though the information rate and energy efficiency cannot be maximized simultaneously, the neuron holds a high information processing capacity at the tempera- ture corresponding to the maximal energy efficiency. Our results support the idea that the energy efficiency is a selective pressure that influences the evolution of nervous systems.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11564034,11105062 and 61562075the Fundamental Research Funds for the Central Universities under Grant Nos lzujbky-2015-119 and 31920130008
文摘The map-based neuron models have received attention as valid phenomenological neuron models due to their computational efficiency and flexibility to generate rich patterns. Here we evaluate the information capacity and transmission of the Courbage-Nekorkin Vdovin （CNV） map-based neuron model with a bursting and tonic firing mode in response to external pulse inputs, in both temporal and rate coding schemes. We find that for both firing modes, the CNV model could capture the essential behavior of the stochastic Hodgkin-Huxley model in information transmission for the temporal coding scheme, with regard to the dependence of total entropy, noise entropy, information rate, and coding efficiency on the strength of the input signal. However, in tonic firing mode, it fails to replicate the input strength-dependent information rate in the rate coding scheme. Our results suggest that the CNV map-based neuron model could capture the essential behavior of information processing of typical conductance-based neuron models.
