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Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

ABSTRACT: Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.

SUBMITTER: Tsai KT

PROVIDER: S-EPMC5966413 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

Tsai Kuo-Ting KT Hu Chin-Kun CK Li Kuan-Wei KW Hwang Wen-Liang WL Chou Ya-Hui YH

Scientific reports 20180523 1

Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within th ...[more]

PMID: 29795277

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Project description:Key pointsInhibitory-interneuron networks, consisting of multiple forms of circuit motifs including reciprocal (inhibitory interneurons inhibiting other interneurons) and feedforward (inhibitory interneurons inhibiting principal neurons) connections, are crucial in processing sensory information. The present study applies a statistical method to in vivo multichannel spike trains of dorsal cochlear nucleus neurons to disentangle reciprocal and feedforward-inhibitory motifs. After inducing input-specific plasticity, reciprocal and feedforward inhibition are found to be differentially regulated, and the combined effect synergistically modulates circuit output. The findings highlight the interplay among different circuit motifs as a key element in neural computation.AbstractInhibitory interneurons play an essential role in neural computations by utilizing a combination of reciprocal (interneurons inhibiting each other) and feedforward (interneuron inhibiting the principal neuron) inhibition to process information. To disentangle the interplay between the two inhibitory-circuit motifs and understand their effects on the circuit output, in vivo recordings were made from the guinea pig dorsal cochlear nucleus, a cerebellar-like brainstem circuit. Spikes from inhibitory interneurons (cartwheel cell) and principal output neurons (fusiform cell) were compared before and after manipulating their common multimodal input. Using a statistical model based on the Cox method of modulated renewal process of spike train influence, reciprocal- and feedforward-inhibition motifs were quantified. In response to altered multimodal input, reciprocal inhibition was strengthened while feedforward inhibition was weakened, and the two motifs combined to modulate fusiform cell output and acoustic-driven responses. These findings reveal the cartwheel cell's role in auditory and multimodal processing, as well as illustrated the balance between different inhibitory-circuit motifs as a key element in neural computation.

Project description:Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients. Here we show that these dynamical behaviours may in fact be understood as hallmarks of the specific representation and algorithm that the cortex employs to perform probabilistic inference. We demonstrate that a particular family of probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally maps onto the dynamics of excitatory-inhibitory neural networks. Specifically, we constructed a model of an excitatory-inhibitory circuit in primary visual cortex that performed HMC inference, and thus inherently gave rise to oscillations and transients. These oscillations were not mere epiphenomena but served an important functional role: speeding up inference by rapidly spanning a large volume of state space. Inference thus became an order of magnitude more efficient than in a non-oscillatory variant of the model. In addition, the network matched two specific properties of observed neural dynamics that would otherwise be difficult to account for using probabilistic inference. First, the frequency of oscillations as well as the magnitude of transients increased with the contrast of the image stimulus. Second, excitation and inhibition were balanced, and inhibition lagged excitation. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.

Dataset Information

Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

Publications

Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

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