Simple codes versus efficient codes

Abstract

Transmission of information is an important function of cortical neurons, so it is conceivable that they have evolved to transmit information efficiently, with low noise and high temporal precision. Such precision is consistent with the output generated by various working models that mimick neuronal activity, from simple integrate-and-fire models to elaborate numerical simulations of realistic-looking neurons. But our current inability to match this data with neurons' detailed spike-generating mechanisms in vivo allows us a wide latitude in interpreting the significance of the various components of their spike code. One extreme hypothesis, the ‘simple’ model, is that each neuron is noisy and slow, performing a simple computation and transmitting a small amount of information. A competing hypothesis, the ‘efficient’ model, postulates that a neuron transmits large amounts of information through precise, complex, single-spike computations. Both hypotheses are broadly consistent with the available data. The conflict may only be resolved with the development of new measurement techniques that will allow one to investigate directly the properties that make a neuron efficient — that is, to be able to measure highly transient, localized events inside the thinnest dendrites, which are currently experimentally inaccessible.