Frequency decoding of periodically timed signals in a random neural network
Frequency discrimination is a fundamental task of the auditory system. The mammalian cochlea provides a place code in which different frequencies are detected at distinct spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus—they exhibit phase locking—and thus provide temporal information about the tone's frequency. Humans employ this temporal information for the discrimination of low frequencies. How might such temporal information be read out in the brain? We employed statistical and numerical methods to demonstrate that a recurrent random neural network in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such a network is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that humans can discriminate.
Periodic stimulation of a neural network at 400 Hz (left) excites a coterie of neurons (orange) that are interconnected with delays near the period of the stimulus. Other neurons remain inactive (blue). A 500 Hz stimulus (right) is detected by a distinct constellation of cells.