Cracking the Function of Layers in the Sensory Cortex

Adesnik, Hillel; Naka, Alexander

doi:10.1016/j.neuron.2018.10.032

Cited by 91 publications

(89 citation statements)

References 153 publications

(194 reference statements)

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“…Previous approaches have emphasized structural aspects based on layers and the broad classification of excitatory and inhibitory neurons. 109,34 Modern anatomical, physiological and genetic techniques allow an unprecedented level of detail to be brought to the analysis and understanding of cortical microcircuits 79,1 . In particular, several neuron classes can now be identified based on distinct gene expression, morphology, physiology and connectivity.…”

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confidence: 99%

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Durá-Bernal

Neymotin

Suter

et al. 2017

Preprint

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We developed a biologically detailed multiscale model of mouse primary motor cortex (M1) microcircuits, incorporating data from several recent experimental studies. The model simulates at scale a cylindrical volume with a diameter of 300 m and cortical depth 1350 m of M1. It includes over 10,000 cells distributed across cortical layers based on measured cell densities, with close to 30 million synaptic connections. Neuron models were optimized to reproduce electrophysiological properties of major classes of M1 neurons. Layer 5 corticospinal and corticostriatal neuron morphologies with 700+ compartments reproduced cell 3D reconstructions, and their ionic channel distributions were optimized within experimental constraints to reproduce in vitro recordings. The network was driven by the main long-range inputs to M1: posterior nucleus (PO) and ventrolateral (VL) thalamus PO, primary and secondary somatosensory cortices (S1, S2), contralateral M1, secondary motor cortex (M2), and orbital cortex (OC). The network local and long-range connections depended on pre-and post-synaptic cell class and cortical depth. Data was based on optogenetic circuit mapping studies which determined that connection strengths vary within layer as a function of the neuron's cortical depth. The synaptic input distribution across cell dendritic trees -likely to subserve important neural coding functions -was also mapped using optogenetic methods and incorporated into the model. We employed the model to study the effect on M1 of increased activity from each of the long-range inputs, and of different levels of H-current in pyramidal tract-projecting (PT) corticospinal neurons. Microcircuit dynamics and information flow were quantified using firing rates, oscillations, and information transfer measures (Spectral Granger causality). We evaluated the response to short pulses and long time-varying activity arising from the different long-range inputs. We also studied the interaction between two simultaneous long-range inputs. Simulation results support the hypothesis that M1 operates along a continuum of modes characterized by the degree of activation of intratelencephalic (IT), corticothalamic (CT) and PT neurons. The particular subset of M1 neurons targeted by different long-range inputs and the level of H-current in PT neurons regulated the different modes. Downregulation of H-current facilitated synaptic integration of inputs and increased PT output activity. Therefore, VL, cM1 and M2 inputs with downregulated H-current promoted an PT-predominant mode; whereas PO, S11 . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/201707 doi: bioRxiv preprint first posted online Oct. 11, 2017; and S2 inputs with upregulated H-current favored an IT-predominant mode, but a mixed mode where upper layer IT cells in turn activated PT cells if H-c...

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confidence: 99%

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Durá-Bernal

Neymotin

Suter

et al. 2017

Preprint

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“…Subpopulations of neurons serving distinct functions is a fundamental concept in neural networks (Adesnik and Naka, 2018;Zeng and Sanes, 2017). Layer 2-3 neurons dramatically differ from layer 5 PT neurons in their input-output pattern, connectivity and intrinsic electrophysiological properties (Anderson et al, 2010;Harris and Shepherd, 2015;Hooks et al, 2011;Tsubo et al, 2013), thus it is not surprising that these different neuronal populations may perform different computations in motor cortex.…”

Section: Discussionmentioning

confidence: 99%

Cell-type specific outcome representation in primary motor cortex

Lavzin

Levy

Benisty

et al. 2020

Preprint

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Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, "success"and "failure" related neurons that develop with training and report end-result of trials.In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial's outcome.Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

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“…These inputs are relatively local when compared to the input to ntsr1 cells of L6, which integrate more non-visual, multi-modal input. Thus, in upper layers within the cortical column, processing of similar long-range information in upper layers likely combines with translaminar processing to shape diverse cortical function (D'Souza and Burkhalter, 2017;Adesnik and Naka, 2018).…”

Section: Discussionmentioning

confidence: 99%

Excitatory and inhibitory L2/3 neurons in mouse primary visual cortex are balanced in their input connectivity

Brown

Cossell

Margrie

2020

Preprint

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Quantitatively characterising brain-wide connectivity of neural circuits is of vital importance in understanding the function of the mammalian cortex. Here we have designed an analytical approach to examine data from hierarchical segmentation ontologies, and applied it in the comparison of long-range presynaptic connectivity onto excitatory and inhibitory neurons in layer 2/3 (L2/3) of mouse primary visual cortex (V1). We find that long-range connections onto these two general cell classes in L2/3 originate from highly similar brain regions, and in similar proportions, when compared to input to layer 6. These anatomical data suggest that distal information received by excitatory and inhibitory networks is highly homogenous in L2/3.

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Cracking the Function of Layers in the Sensory Cortex

Cited by 91 publications

References 153 publications

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Cell-type specific outcome representation in primary motor cortex

Excitatory and inhibitory L2/3 neurons in mouse primary visual cortex are balanced in their input connectivity

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