Optimization by Vector Space Methods
Optimization by Vector Space Methods
Differences in spiking patterns among cortical neurons
Neural Computation
Information Geometry of Interspike Intervals in Spiking Neurons
Neural Computation
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Parameters of spike trains observed in a short time window
Neural Computation
The noncoherent rician fading Channel-part I: structure of the capacity-achieving input
IEEE Transactions on Wireless Communications
The capacity of discrete-time memoryless Rayleigh-fading channels
IEEE Transactions on Information Theory
On the discreteness of capacity-achieving distributions
IEEE Transactions on Information Theory
Mutual Information and Conditional Mean Estimation in Poisson Channels
IEEE Transactions on Information Theory
Information transfer by energy-efficient neurons
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
A mathematical theory of energy efficient neural computation and communication
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
On the relation between encoding and decoding of neuronal spikes
Neural Computation
Hi-index | 0.00 |
Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.