Fast Recruitment of Recurrent Inhibition in the Cat Visual Cortex

2017

Front. Neural Circuits

Competitive interactions are believed to underlie many types of cortical processing, ranging from memory formation, attention and development of cortical functional organization (e.g., development of orientation maps in primary visual cortex). In the latter case, the competitive interactions happen along the cortical surface, with local populations of neurons reinforcing each other, while competing with those displaced more distally. This specific configuration of lateral interactions is however in stark contrast with the known properties of the anatomical substrate, i.e., excitatory connections (mediating reinforcement) having longer reach than inhibitory ones (mediating competition). No satisfactory biologically plausible resolution of this conflict between anatomical measures, and assumed cortical function has been proposed. Recently a specific pattern of delays between different types of neurons in cat cortex has been discovered, where direct mono-synaptic excitation has approximately the same delay, as the combined delays of the disynaptic inhibitory interactions between excitatory neurons (i.e., the sum of delays from excitatory to inhibitory and from inhibitory to excitatory neurons). Here we show that this specific pattern of delays represents a biologically plausible explanation for how short-range inhibition can support competitive interactions that underlie the development of orientation maps in primary visual cortex. We demonstrate this statement analytically under simplifying conditions, and subsequently show using network simulations that development of orientation maps is preserved when long-range excitation, direct inhibitory to inhibitory interactions, and moderate inequality in the delays between excitatory and inhibitory pathways is added.

Section: Resultsmentioning

confidence: 64%

“…A recent study by Ohana et al (2012) revealed a specific pattern of transmission delays between different neural type combinations. Specifically they found that the excitatory to excitatory connections are slow, while the excitatory to inhibitory connections are fast.…”

Section: Introductionmentioning

confidence: 95%

Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps

2017

Front. Neural Circuits

“…To reflect this, for all intra-cortical connectivity a distance-dependent delay with propagation constant of 0.3 ms −1 (Bringuier et al 1999;Frégnac 2012;Chavane et al 2011) was used, which corresponds to the slow propagation of action potentials along the intra-V1 (lateral) un-myelinated axons. The delays in the feed-forward thalamo-cortical pathway are drawn from a uniform distribution within the range (1.4,2.4) ms. Second, Ohana et al (2012) have recently shown that delays of synaptic transmission in cat visual cortex are dependent on both pre-and post-synaptic neural type, with the notable feature of slow excitatory to excitatory and fast excitatory to inhibitory transmission. Distance-dependent axonal propagation delay is unlikely to explain these results as these experiments were performed in nearby neurons (Ohana et al 2012).…”

Section: Delaysmentioning

confidence: 99%

“…The delays in the feed-forward thalamo-cortical pathway are drawn from a uniform distribution within the range (1.4,2.4) ms. Second, Ohana et al (2012) have recently shown that delays of synaptic transmission in cat visual cortex are dependent on both pre-and post-synaptic neural type, with the notable feature of slow excitatory to excitatory and fast excitatory to inhibitory transmission. Distance-dependent axonal propagation delay is unlikely to explain these results as these experiments were performed in nearby neurons (Ohana et al 2012). These pair-specific synaptic integration delays are in the order of only a few ms, but are important for local integration (within the same column) and for the precise timing of spike control by E/I interaction.…”

Section: Delaysmentioning

confidence: 99%

A comprehensive data-driven model of cat primary visual cortex

Monier

Frégnac

et al. 2018

Preprint

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code.This integrative model offers numerous insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. Significance statementComputational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most previous models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to commit to detailed models, but constrain them with a broad range of anatomical and functional data. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of V1, that is constrained by an unprecedented range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision. Keywordsvisual cortex, cortical microcircuit, large-scale models, comprehensive modeling, layered network 2 resting-conductance regime emerges in the model. Under visual stimulation, the same model exhibits diversity of the interplay between evoked excitation and inhibition; stimulus-locked subthreshold variability; contrast-invariant orientation tuning; size tuning; stimulus-dependent firing precision; and a realistic distribution of Simple/Complex receptive fields.This study advances our ability to capture the function of V1 within a single model, and provides insights into how the studied properties interac...

“…The delays in the feed-forward thalamo-cortical pathway are drawn from a uniform distribution within the (1.4,2.4) ms −1 range. Second, Ohana et al [56] have recently shown that delays of synaptic transmission in cat visual cortex are dependent on both pre-and post-synaptic neural type, with the notable feature of slow excitatory to excitatory and fast excitatory to inhibitory transmission. Distance-dependent axonal propagation delay is unlikely to explain these results as these experiments were performed in nearby neurons [56].…”

Section: Delaysmentioning

confidence: 99%

Cortical visual prosthesis: a detailed large-scale simulation study

Sabatier

Galle

et al. 2019

Preprint

Recent advances in applying optogenetics in primates initiated the development of light based prosthetic implants for sensory restoration. Thanks to being the most well explored cortical area that is readily accessible at the surface of the brain, vision restoration via direct optogenetic activation of primary visual cortex is one of the most promising early targets for a optogenetics based prosthetic program. However, two fundamental elements of the cortical optogenetic prosthesis remain unclear. First, the exact mechanisms of neural dynamics under direct cortical stimulation, especially in the context of living, active and functionally specific intra-cortical neural circuitry, is poorly understood. Second, we lack protocols for transformation of arbitrary visual stimuli into light activation patterns that would induce perception of the said stimulus by the subject. In this study we address these issues using a large-scale spiking neural network modeling strategy of high biological fidelity. We examine the relationship between specific spatial configuration of light delivered to cortex and the resulting spatio-temporal pattern of activity evoked in the simulated cortical circuitry. Using such virtual experiments, we design a protocol for translation of a specific set of stimuli to activation pattern of a matrix of light emitting elements and provide a detailed assessment of the resulting cortical activations with respect to the natural vision control condition. In this study we restrict our focus to the grating stimulus class, which are an ideal starting point for exploration due to their thoroughly characterized representation in V1 and well-defined information content. However, we also provide an outline of a straight-forward road-map for transforming this grating centric stimulation protocol towards general strategy capable of transforming arbitrary spatio-temporal visual stimulus to a spatio-temporal pattern of light, thus enabling vision restoration via optogenetic V1 activation.