Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

Chariker, Logan; Shapley, Robert; Young, Lai Sang

doi:10.1523/jneurosci.2603-16.2016

Cited by 90 publications

(172 citation statements)

References 86 publications

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“…As evidenced by the mean and single neuron orientation tuning curves shown in 6A, most excitatory and inhibitory units in the model cortical Layer 4 and 2/3 exhibit contrast invariance of orientation tuning width, which is further confirmed by comparing the HWHH of the tuning curves at low and high contrast ( Figure 6B: most points lie close to the identity line). On average we observe very minor broadening of the tuning curves at high contrast, and it is more pronounced in inhibitory neurons: the mean HWHH differences between low and high contrasts is 0.8 • (excitatory, Overall, the orientation tuning in our model is in very good qualitative and quantitative agreement with experimental data, with both excitatory and inhibitory neurons exhibiting sharp and contrast-invariant orientation tuning across all modeled layers, in contrast to many previous modeling studies that relied on untuned inhibition, or broad contrastdependent inhibition (Troyer et al 1998;Lauritzen and Miller 2003;Chariker et al 2016).…”

Section: The Mean Tuning Widths Of Excitatory and Inhibitory Neurons supporting

confidence: 79%

A comprehensive data-driven model of cat primary visual cortex

Antolík

Monier

Frégnac

et al. 2018

Preprint

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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...

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Section: The Mean Tuning Widths Of Excitatory and Inhibitory Neurons supporting

confidence: 79%

A comprehensive data-driven model of cat primary visual cortex

Antolík

Monier

Frégnac

et al. 2018

Preprint

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“…This difference may challenge the purely feedforward explanation of adaptation in visual cortex, even if only indicating a late top-down modulation. We note that consideration of recurrent connectivity as essential to explaining V1 phenomena was emphasized in recent modeling work by Chariker et al [69], although not in the context of adaptation.…”

Section: Discussionmentioning

confidence: 89%

Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

Yarden¹,

Nelken²

2017

PLoS Comput Biol

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Stimulus-specific adaptation (SSA) occurs when neurons decrease their responses to frequently-presented (standard) stimuli but not, or not as much, to other, rare (deviant) stimuli. SSA is present in all mammalian species in which it has been tested as well as in birds. SSA confers short-term memory to neuronal responses, and may lie upstream of the generation of mismatch negativity (MMN), an important human event-related potential. Previously published models of SSA mostly rely on synaptic depression of the feedforward, thalamocortical input. Here we study SSA in a recurrent neural network model of primary auditory cortex. When the recurrent, intracortical synapses display synaptic depression, the network generates population spikes (PSs). SSA occurs in this network when deviants elicit a PS but standards do not, and we demarcate the regions in parameter space that allow SSA. While SSA based on PSs does not require feedforward depression, we identify feedforward depression as a mechanism for expanding the range of parameters that support SSA. We provide predictions for experiments that could help differentiate between SSA due to synaptic depression of feedforward connections and SSA due to synaptic depression of recurrent connections. Similar to experimental data, the magnitude of SSA in the model depends on the frequency difference between deviant and standard, probability of the deviant, inter-stimulus interval and input amplitude. In contrast to models based on feedforward depression, our model shows true deviance sensitivity as found in experiments.

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“…Theoretically, recurrent feedback inhibition is indispensable for stabilizing recurrent excitation, which serves to amplify specific input patterns and generate persistent activity (van Vreeswijk & Sompolinsky 1996, Bressloff et al 2001, Bressloff & Cowan 2002). Experimental evidence suggests that local inhibitory connections are strong and dense (Fino & Yuste 2011, Hofer et al 2011, Isaacson & Scanziani 2011) and that some fundamental cortical responses can be explained only if the cortex operates in a regime of strong and balanced recurrent connections (Marino et al 2005, Ozeki et al 2009, Stimberg et al 2009, Shushruth et al 2012, Chariker et al 2016). …”

Section: Mechanisms For Surround Modulationmentioning

confidence: 99%

Circuits and Mechanisms for Surround Modulation in Visual Cortex

Angelucci

Bijanzadeh

Nurminen

et al. 2017

Annu. Rev. Neurosci.

249

286

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Surround modulation (SM) is a fundamental property of sensory neurons in many species and sensory modalities. SM is the ability of stimuli in the surround of a neuron’s receptive field (RF) to modulate (typically suppress) the neuron’s response to stimuli simultaneously presented inside the RF, a property thought to underlie optimal coding of sensory information and important perceptual functions. Understanding the circuit and mechanisms for SM can reveal fundamental principles of computations in sensory cortices, from mouse to human. Current debate is centered over whether feedforward or intracortical circuits generate SM, and whether this results from increased inhibition or reduced excitation. Here we present a working hypothesis, based on theoretical and experimental evidence, that SM results from feedforward, horizontal, and feedback interactions with local recurrent connections, via synaptic mechanisms involving both increased inhibition and reduced recurrent excitation. In particular, strong and balanced recurrent excitatory and inhibitory circuits play a crucial role in the computation of SM.

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Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

Cited by 90 publications

References 86 publications

A comprehensive data-driven model of cat primary visual cortex

A comprehensive data-driven model of cat primary visual cortex

Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

Circuits and Mechanisms for Surround Modulation in Visual Cortex

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