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- W2053266518 abstract "A neuron extensively receives both inhibitory and excitatory inputs. What is the ratio r between these two types of input so that the neuron can most accurately read out input information (rate)? We explore the issue in this paper provided that the neuron is an ideal observer - decoding the input information with the attainment of the Cramer-Rao inequality bound. It is found that, in general, adding certain amounts of inhibitory inputs to a neuron improves its capability of accurately decoding the input information. By calculating the Fisher information of an integrate-and-fire neuron, we determine the optimal ratio r for decoding the input information from an observation of the efferent interspike intervals. Surprisingly, the Fisher information can be zero for certain values of the ratio, seemingly implying that it is impossible to read out the encoded information at these values. By analysing the maximum likelihood estimate of the input information, it is concluded that the input information is in fact most easily estimated at the points where the Fisher information vanishes." @default.
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- W2053266518 date "2001-09-10" @default.
- W2053266518 modified "2023-09-27" @default.
- W2053266518 title "Optimally decoding the input rate from an observation of the interspike intervals" @default.
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- W2053266518 doi "https://doi.org/10.1088/0305-4470/34/37/304" @default.
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