Another contribution by synaptic failures to energy efficient processing by neurons
Energy efficient use of axons requires achieving a prespecified firing rate. We hypothesize that the failure process of quantal synaptic transmission helps a neuron better approximate this desirable firing rate by moving the neuron's input excitation distribution closer to a Gaussian. If there are many statistically independent inputs per neuron, quantal failures do not help but, essentially, are harmless for achieving the desired firing rate. However, such statistical independence is unrealistic. An input distribution reflecting statistical dependence is a mixture distribution. For certain mixtures, failures can improve the Gaussian approximation and more precisely produce the desired firing rate.
skewness kurtosis neural network central limit theorem quantal transmission