Top-down inputs drive neuronal network rewiring and context-enhanced sensory processing in olfaction

Abstract

Much of the computational power of the mammalian brain arises from its extensive top-down projections. To enable neuron-specific information processing these projections have to be precisely targeted. How such a specific connectivity emerges and what functions it supports is still poorly understood. We addressed these questions in silico in the context of the profound structural plasticity of the olfactory system. At the core of this plasticity are the granule cells of the olfactory bulb, which integrate bottom-up sensory inputs and top-down inputs delivered by vast top-down projections from cortical and other brain areas. We developed a biophysically supported computational model for the rewiring of the top-down projections and the intra-bulbar network via adult neurogenesis. The model captures various previous physiological and behavioral observations and makes specific predictions for the cortico-bulbar network connectivity that is learned by odor exposure and environmental contexts. Specifically, it predicts that after learning the granule-cell receptive fields with respect to sensory and with respect to cortical inputs are highly correlated. This enables cortical cells that respond to a learned odor to enact disynaptic inhibitory control specifically of bulbar principal cells that respond to that odor. Functionally, the model predicts context-enhanced stimulus discrimination in cluttered environments ('olfactory cocktail parties') and the ability of the system to adapt to its tasks by rapidly switching between different odor-processing modes. These predictions are experimentally testable. At the same time they provide guidance for future experiments aimed at unraveling the cortico-bulbar connectivity. Author summaryIn mammalian sensory processing, extensive top-down feedback from higher brain areas reshapes the feedforward, bottom-up information processing. The structure of the top-down connectivity, the mechanisms leading to its specificity, and the functions it supports are still poorly understood. Using computational modeling, we investigated these issues in the olfactory system. There, the granule cells of the olfactory bulb, which is the first brain area to receive sensory input from the nose, are the key players of extensive structural changes to the network through the addition and also the removal of granule cells as well as through the formation and removal of their connections. This structural plasticity allows the system to learn and to adapt its sensory processing to its PLOS 1/21 odor environment. Crucially, the granule cells combine bottom-up sensory input from the nose with top-down input from higher brain areas, including cortex. Our biophysically supported computational model predicts that, after learning, the granule cells enable cortical neurons that respond to a learned odor to gain inhibitory control of principal neurons of the olfactory bulb, specifically of those that respond to the learned odor. Functionally, this allows top-down input to enhance odor discrimination in cluttered envir...

Cortical feedback and gating in odor discrimination and generalization

“…Context profoundly shapes odor perception [16, 17, 22, 44, 92, 100], and previous studies have demonstrated the critical role of cortical feedback to the OB in the formation of odor-context associations [47, 56]. Cortical feedback can also enhance odor discrimination [2, 21, 52, 57, 68] and generalization [49]. Some studies have implicated feedback in the generation of beta oscillations, thought to be associated with olfactory learning [42, 43, 59, 60, 65].…”

“…Theoretical and computational studies have also explored possible mechanisms by which feedback may impose these effects. Models of top-down, direct cortical feedback to the bulb which include plasticity in the feedback and in cortex have been able to reproduce odor association with visual context [56] and differences in cortical reorganization during passive vs. active learning [100]; demonstrate adaptation to specific olfactory environments and odor tasks when guided by neurogenesis [2]; and explain differential responses to the same odor under different contexts [49]. Other models have demonstrated further effects of neuromodulation on OB activity, such as normalization of output neuron response [52], increasing spike synchrony [20, 50], and general enhancement of odor discrimination [51, 52].…”

Connectivity and dynamics in the olfactory bulb

“…Thus, our results suggest that a primary determinant of the effect of cortical feedback on the bulb output is the network architecture of GCs, as opposed to which of these cells are specifically targeted. This implies that theories of feedback and neurogenesis in the bulb that rely on specific targeting of GCs and MCs [12,28,93] may require additional components beyond the basic MC-GC network to be feasible.…”

“…However, the sheer number of neurons, encompassing tens of thousands of excitatory cells and millions of inhibitory cells [1]; intricate network architecture [2][3][4]; and complex spiking dynamics [5][6][7][8][9] make detailed biophysical simulation impractical at large scale. Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [10][11][12][13][14][15][16][17][18][19][20][21][22] and often study smaller networks on the order of hundreds or even tens of neurons [10,11,13,14,19,[22][23][24], allowing for highly complex, conductance-based neuronal frameworks [7]. Other approaches use rate-based or population equations [12,21,25] thereby facilitating models with larger numbers of neurons.…”

“…Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [10][11][12][13][14][15][16][17][18][19][20][21][22] and often study smaller networks on the order of hundreds or even tens of neurons [10,11,13,14,19,[22][23][24], allowing for highly complex, conductance-based neuronal frameworks [7]. Other approaches use rate-based or population equations [12,21,25] thereby facilitating models with larger numbers of neurons.…”