Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study

Abstract

Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma‐frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation‐inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation‐inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

“…In the granular layer of the cerebellar cortex, previous studies have indicated that neural oscillations relate to the feedback loop between GoCs and GCs [ 19 , 30 , 35 , 53 ]. Moreover, oscillations can be modulated by the balance of excitation and inhibition [ 33 , 34 ] and feedforward inhibition [ 31 , 32 ]. Consistent with these findings, our results indicate that feedback inhibition is crucial for generating oscillation that can be further modulated by the balance of excitation and inhibition inputs.…”

“…Several mechanisms have been proposed for synchronous firing, including feedforward inhibition from inhibitory neurons to excitatory neurons [9,19,20], feedback inhibition where excitation dominates inhibition [21], opposite bursting patterns of excitatory and inhibitory neurons [22][23][24][25], and gap junctions between inhibitory neurons [26][27][28][29][30]. Furthermore, oscillations can also be modulated by the balance of excitation and inhibition in cells [31][32][33][34].…”

Recurrent Excitatory Feedback From Mossy Cells Enhances Sparsity and Pattern Separation in the Dentate Gyrus via Indirect Feedback Inhibition

“…This circuit function has been termed “winner-take-all,” or “lateral” inhibition, whereby the neurons receiving the highest level of excitatory input recruit feedback inhibition that suppresses neighboring neurons which are receiving less excitation. However, previous modeling work has shown that feedback inhibition alone is not able to prevent the number of active output neurons from increasing as the total amount of afferent input grows (Rennó-Costa et al, 2019 ). Furthermore, it is not clear if the extremely low fraction of active output neurons in circuits with ultrasparse representations like the hippocampal dentate gyrus is sufficient to activate the level of feedback inhibition necessary to support “winner-take-all” competition.…”

“…Inhibition from interneurons that receive the same incoming afferent inputs as the output neurons is termed “feedforward inhibition," and inhibition from cells that receive input from the output neurons themselves is called “feedback inhibition.” These classes of interneurons have been implicated in specific computational functions in neural circuits. One important function proposed for feedforward inhibition is “background subtraction,” whereby inhibition grows in proportion to and cancels the average level of input to the circuit, enabling only large fluctuations in inputs above the average level to drive circuit output (Grienberger et al, 2017 ; Rennó-Costa et al, 2019 ). Here we ask whether this background subtraction mechanism can support a constant level of output given a wide range in the total number of active inputs.…”

“…Feedback inhibition has been proposed to regulate the maximum number of output neurons that respond to the same pattern of input (de Almeida et al, 2009 ; Stefanelli et al, 2016 ; Rennó-Costa et al, 2019 ). This circuit function has been termed “winner-take-all,” or “lateral” inhibition, whereby the neurons receiving the highest level of excitatory input recruit feedback inhibition that suppresses neighboring neurons which are receiving less excitation.…”