Phase-resetting as a tool of information transmission

Abstract

Models of information transmission in the brain largely rely on firing rate codes. The abundance of oscillatory activity in the brain suggests that information may be also encoded using the phases of ongoing oscillations. Sensory perception, working memory and spatial navigation have been hypothesized to use phase codes, and cross-frequency coordination and phase synchronization between brain areas have been proposed to gate the flow of information. Phase codes generally require the phase of the oscillations to be reset at specific reference points for consistent coding, and coordination between oscillators requires favorable phase resetting characteristics. Recent evidence supports a role for neural oscillations in providing temporal reference windows that allow for correct parsing of phase-coded information.

Figure 1. Phase-resetting explained using the Wilson-Cowan model

A. Excitatory (E) and inhibitory (I) activity and the simulated local field potential (LFP). Phase 0 is the peak of the E activity. B. Phase is marked on the cycle in the plane of the firing rates of the two populations. Magenta and green arrows indicate the direction of an external excitation (applied to the excitatory population). C. An external perturbation (vertical colored arrows) phase shifts the perturbed (colored) traces for the excitatory poplation compared to unperturbed (black) traces by the amount shown by horizontal arrows. (C1) An input at phase 0.05 causes a delay. (C2). An input at phase 0.4 causes an advance. The old phase just prior to the stimulus is repeated on the unperturbed (black) waveform at multiples of the cycle period (vertical dashed line) after the input, but the new phase on the colored traces at that point differs from the old phase by the phase shift. D. The PRC plots the phase shift as a function of the phase of the input perturbation. Slopes outside the stabilizing range (−2 to 0) are indicated in red. (D1) Weak input. (D2) Strong input. The arrows correspond to the perturbations in B and C. E. The phase transition curve plots the new phase (modulo one) versus the old phase. E1. For a weak input, the range of new phases is equal to that of old phases. E2. For a strong input, the range of new phases can be much smaller than the range of old phases.

Figure 2. Phase-locking explained using the Wilson-Cowan model

A. Phase response curve for strong input as in Figure 1D2. B. Forcing with a periodic train of stimuli (open triangles) at different P_F/P_i ratios produces phase-locking only in the middle three traces, in which the corresponding point on the PRC exists and a slope within the stable range (see inset). Red indicates an unstable slope. Note that the position of the triangles within the forced cycle shifts as the forcing frequency changes.