  Neural oscillations can enhance feature recognition, modulate interactions
between neurons, and improve learning and memory. Simulational studies have
shown that coherent oscillations give rise to windows in time during which
information transfer can be enhanced in neuronal networks. Unanswered questions
are: 1) What is the transfer mechanism? And 2) how well can a transfer be
executed? Here, we present a pulse-based mechanism by which graded current
amplitudes may be exactly propagated from one neuronal population to another.
The mechanism relies on the downstream gating of mean synaptic current
amplitude from one population of neurons to another via a pulse. Because
transfer is pulse-based, information may be dynamically routed through a neural
circuit. We demonstrate the amplitude transfer mechanism in a realistic network
of spiking neurons and show that it is robust to noise in the form of pulse
timing inaccuracies, random synaptic strengths and finite size effects. In
finding an exact, analytical solution to a fundamental problem of information
coding in the brain, graded information transfer, we have isolated a basic
mechanism that may be used as a building block for fast, complex information
processing in neural circuits.
