Recurrent interactions in local cortical circuits

Peron, Simon; Pancholi, Ravi; Voelcker, Bettina; Wittenbach, Jason D.; Ólafsdóttir, Hildur; Freeman, Jeremy; Svoboda, Karel

doi:10.1038/s41586-020-2062-x

Cited by 108 publications

(138 citation statements)

References 51 publications

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“…Simultaneous imaging of the surrounding network has allowed us to show that, despite this exquisite behavioural sensitivity, the dominant network response matches the activation of targeted neurons with local suppression, flattening the network input-output function to maintain the level of network activation within the spontaneous range. These results support the sparse coding hypothesis (Barlow, 1972;Barth & Poulet, 2012;Kanerva, 1993;Olshausen & Field, 1996), demonstrate that the local network operates in an inhibition-stabilized regime (Denève & Machens, 2016;Murphy & Miller, 2009;Ozeki, Finn, Schaffer, Miller, & Ferster, 2009;Pehlevan & Sompolinsky, 2014;Sanzeni et al, 2020;Tsodyks et al, 1997;van Vreeswijk & Sompolinsky, 1996;Wolf et al, 2014) and suggest a high storage capacity for recurrent networks (Hopfield, 1982;Lefort et al, 2009;Peron et al, 2020). This combination of features likely maximizes perceptual sensitivity while minimizing erroneous detection of background activity, thus avoiding hallucinations (Carbon, 2014;Cassidy et al, 2018;Corlett et al, 2019;Friston, 2005), runaway excitation (Rose & Blakemore, 1974;Treiman, 2001;Ziburkus, Cressman, Barreto, & Schiff, 2006) and reducing cortical energy requirements (Schölvinck et al, 2008).…”

Section: Discussionsupporting

confidence: 54%

“…Such sensitivity is beneficial as it suggests that recurrent networks like cortical L2/3 have a high storage capacity (Hopfield, 1982;Lefort et al, 2009;Ko et al, 2011;Harris and Mrsic-Flogel, 2013;Cossell et al, 2015;Peron et al, 2020) allowing the brain to represent many patterns independently (Amit, Gutfreund and Sompolinsky, 1985a;Amit, Gutfreund and Sompolinsky, 1985b;McEliece et al, 1987;Brunel, 2016;Folli, Leonetti and Ruocco, 2017).…”

Section: Activation Of Only a Small Number Of Neurons Is Required Tomentioning

confidence: 99%

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How many neurons are sufficient for perception of cortical activity?

Dalgleish

Russell

Packer

et al. 2020

eLife

100

123

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Many theories of brain function assume that information is encoded and behaviour is controlled through sparse, distributed patterns of activity. It is therefore crucial to place a lower bound on the amount of neural activity that can drive behaviour and to understand how neuronal networks operate within these constraints. We use an all-optical approach to test this lower limit by driving behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while using two-photon calcium imaging to record the impact on the local network. By precisely titrating the number of neurons in activated ensembles we demonstrate that the lower bound for detection of cortical activity is ~14 pyramidal neurons. We show that there is a very steep sigmoidal relationship between the number of activated neurons and behavioural output, saturating at only ~37 neurons, and that this relationship can shift with learning. By simultaneously measuring activity in the local network, we show that the activation of stimulated ensembles is balanced by the suppression of neighbouring neurons. This surprising behavioural sensitivity in the face of potent network suppression supports the sparse coding hypothesis and suggests that perception of cortical activity balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.

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Section: Discussionsupporting

confidence: 54%

Section: Activation Of Only a Small Number Of Neurons Is Required Tomentioning

confidence: 99%

How many neurons are sufficient for perception of cortical activity?

Dalgleish

Russell

Packer

et al. 2020

eLife

100

123

View full text Add to dashboard Cite

show abstract

“…The inactivations displayed in Fig. 7e,f assume that connectivity-defined sub-populations have been previously identified and made accessible for perturbations [Peron et al, 2020]. In a more realistic setting, the neurons belonging to the relevant sub-populations need first to be functionally identified, and our model provides a direct guidance for that preliminary step.…”

Section: Predictions For Inactivations Of Specific Sub-populationsmentioning

confidence: 99%

The role of population structure in computations through neural dynamics

Dubreuil

Valente

Beirán

et al. 2020

Preprint

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AbstractNeural computations are currently investigated using two competing approaches: sorting neurons into functional classes, or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained with machine-learning tools on neuroscience tasks, here we show that the dimensionality of the dynamics and cell-class structure play fundamentally complementary roles. While various tasks can be implemented by increasing the dimensionality in networks consisting of a single global population, flexible input-output mappings instead required networks to be organized into several sub-populations. Our analyses revealed that the subpopulation structure enabled flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the dynamical landscape of collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity and inactivation experiments.

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“…Perturbation of a whole cell type may not reflect biologically relevant dynamics. With recent perturbations (Chettih and Harvey 2019;Packer et al 2012;Yang et al 2018;Peron et al 2020;Carrillo-Reid et al 2019) targeting smaller populations or even individual neurons, we investigated how perturbations of sub-populations may affect our V1 circuit. While we are not addressing the question of whether a percept emerges from the subjective perspective of an animal as a result of such targeted perturbations, we are investigating how the external "optogenetic" perturbation limited to neurons that share common response preference affects network dynamics.…”

Section: Functional Population Perturbationmentioning

confidence: 99%

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Cai

Billeh

Chettih

et al. 2020

Preprint

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Investigating how visual inputs are encoded in visual cortex is important for elucidating the roles of cell populations in circuit computations. We here use a recently developed, large-scale model of mouse primary visual cortex (V1) and perturb both single neurons as well as functional-and cell-type defined population of neurons to mimic equivalent optogenetic perturbations. First, perturbations were performed to study the functional roles of layer 2/3 excitatory neurons in inter-laminar interactions. We observed activity changes consistent with the canonical cortical model (Douglas and Martin 1991). Second, single neuron perturbations in layer 2/3 revealed a center-surround inhibition-dominated effect, consistent with recent experiments. Finally, perturbations of multiple excitatory layer 2/3 neurons during visual stimuli of varying contrasts indicated that the V1 model has both efficient and robust coding features. The circuit transitions from predominantly broad like-to-like inhibition at high contrasts to predominantly specific liketo-like excitation at low contrasts. These in silico results demonstrate how the circuit can shift from redundancy reduction to robust codes as a function of stimulus contrast.

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Recurrent interactions in local cortical circuits

Cited by 108 publications

References 51 publications

How many neurons are sufficient for perception of cortical activity?

How many neurons are sufficient for perception of cortical activity?

The role of population structure in computations through neural dynamics

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

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