What do we gain from gamma? Local dynamic gain modulation drives enhanced efficacy and efficiency of signal transmission

Abstract

Gamma oscillations in neocortex are hypothesized to improve information transmission between groups of neurons. We recently showed that optogenetic drive of fast-spiking interneurons (FS) at 40 Hz in mouse neocortex in vivo modulates the spike count and precision of sensory evoked responses. At specific phases of alignment between stimuli and FS activation, total evoked spike count was unchanged compared to baseline, but precision was increased. In the present study, we used computational modeling to investigate the origin of these local transformations, and to make predictions about their impact on downstream signal transmission. We replicated the prior experimental findings, and found that the local gain observed can be explained by mutual inhibition of fast-spiking interneurons, leading to more robust sensory-driven spiking in a brief temporal window post-stimulus, increasing local synchrony. Enhanced spiking in a second neocortical area, without a net increase in overall driven spikes in the first area, resulted from faster depolarization of target neurons due to increased pre-synaptic synchrony. In addition, we found that the precise temporal structure of spiking in the first area impacted the gain between cortical areas. The optimal spike distribution matched the "window of opportunity" defined by the timing of inhibition in the target area: spiking beyond this window did not contribute to downstream spike generation, leading to decreased overall gain. This result predicts that efficient transmission between neocortical areas requires a mechanism to dynamically match the temporal structure of the output of one area to the timing of inhibition in the recipient zone.

Keywords: cortex; gain; gamma; inhibition; model; sensory; synchrony.

Figure 1

Model architecture and connectivity. Connectivity is shown for one pyramidal (P) cell (left) and one fast-spiking inhibitory (FS) cell (right) in X2 (marked in yellow). Blue triangles represent P cells, red circles represent F cells. Both cells types connect to both cells types locally within each stage and receive input from a pool of excitatory cells from the previous stage.

Figure 2

Timing-dependent impact of inhibition on sensory responses. (A) Stimulation paradigm for repetitive inhibition. A single sensory stimulus was given at different phases in between two light pulses embedded in a light train at 40 Hz. (B) Population X1 response at baseline (top) and different phases during repetitive inhibition. (C) Spike count for each phase of stimulation. Dashed line indicates baseline condition. (D) Spike synchrony, defined as inter-quartile range of spike times across the population (smaller numbers denote more synchrony). (E) Stimulation paradigm for single inhibition. A single sensory stimulus was given at different delays relative to a single light pulse. (F) Population X1 response at baseline (top) and different delays for a post-stimulus (left) and pre-stimulus (right) light pulse. (G) Spike count for each phase of stimulation. Filled circles correspond to pre-stimulus inhibition, open circles mark post-stimulus inhibition. (H) Spike synchrony. *p < 0.05, **p < 0.01; error bars, mean ± S.E.M.

Figure 3

Mechanism of timing-dependent gain modulation. (A) Left: actual population X1 responses during repetitive inhibition (re-plotted from Figure 2B). Right: predicted population X1 responses combining independent effects of pre- and post-stimulus inhibition on the spike response packet. (B) X1 population responses for FS cells (top) and P cells (bottom) for the baseline (left) and 12-ms delay (right) condition. Arrow in upper left panel indicates the fast FS population spike during baseline missing for the delay condition. (C) Average membrane potential of X1 P cells. Dashed lines indicate periods during which slope is measured. Arrow indicates impact of IPSP induced by FS population spike indicated in B. (D) PSP slope for both times indicated in C for both conditions. (E) Same as (B), but for a model variant without FS-FS inhibition. (F) Spike count dependent on delay for model without FS-FS inhibition (analogous to Figure 2C). *p < 0.05, **p < 0.01; error bars, mean ± S.E.M.

Figure 4

Relationship between X1 spike counts and X2 spike counts and gain. (A) X1 spike counts for repetitive inhibition. (B) X2 spike counts. (C) X2 gain. Arrows in A–C mark the 12-ms delay condition. (D) X2 population responses for FS cells (top) and P cells (bottom) for the baseline (left) and 12-ms delay (right) condition. (E) Average membrane potential of X2 P cells. Dashed lines indicate periods during which slope is measured. (F) PSP slope indicated time in E for both conditions. (G–L) Same as (A–F), but for single post-stimulus inhibition. *p < 0.05, **p < 0.01; error bars, mean ± S.E.M.

Figure 5

X2 gain depends on X1 packet shape. (A) Population responses for X1 (top) and X2 (bottom) for repetitive (left) and single post-stimulus inhibition (right). (B) Impact of individual spikes of the X1 response. Shading and the curve below the PSTH indicate the X2 response after deletion of spikes in the corresponding bin. (C) Same as (A), but after deleting all spikes that did not strongly contribute to the X2 response.