Dataset Information

A generative growth model for thalamocortical axonal branching in primary visual cortex.

ABSTRACT: Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.

SUBMITTER: Kassraian-Fard P

PROVIDER: S-EPMC7018004 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A generative growth model for thalamocortical axonal branching in primary visual cortex.

Kassraian-Fard Pegah P Pfeiffer Michael M Bauer Roman R

PLoS computational biology 20200213 2

Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral ge ...[more]

PMID: 32053598

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Project description:Unified visual perception relies on the integration of bottom-up and top-down inputs in the primary visual cortex (V1), with top-down inputs known to provide behavior-related modulation on visual processing. However, the organization of top-down inputs in V1 remains unclear. Here, using optogenetics-assisted circuit mapping, we characterized how multiple top-down inputs from higher-order cortical and thalamic areas engage excitatory and inhibitory neurons in V1. Systematic layer- and cell-type-specific profiling of the innervation properties of top-down inputs ultimately revealed that each top-down input employs a unique laminar profile when innervating V1. These profiles partially overlap in superficial layers, bypass layer 4 (L4), and clearly segregate upon reaching deep layers. Specifically, inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) preferentially activate L6 neurons, while inputs from the ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) activate L5 neurons. Having defined the inputs, we conducted independent optogenetic activation studies and discovered that ORBvl and LP inputs selectively activate two types of pyramidal neurons (Pyrs) in L5: Pyr <-- ORBvl and Pyr <-- LP neurons, each with specific electrophysiological properties and gene expression profiles. Retrograde mapping subsequently revealed that Pyr <-- ORBvl neurons preferentially innervate subcortical areas and Pyr <-- LP neurons innervate cortical areas, indicating parallel processing of ORBvl and LP inputs in Pyr-type-specific subnetworks. Further, we found mutual inhibition between these two subnetworks, as LP inputs indirectly inhibit Pyr <-- ORBvl neurons and ORBvl inputs inhibit Pyr <-- LP neurons through local inhibitory neurons. Our study thus deepens understanding of the neuronal mechanisms involved in top-down modulation of visual processing by providing a valuable resource characterizing the layer- and cell-type-specific organization of top-down inputs in V1 and by revealing that L5 Pyr-type-specific subnetworks engage in parallel processing of corticocortical and thalamocortical top-down inputs.

Project description:Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

Dataset Information

A generative growth model for thalamocortical axonal branching in primary visual cortex.

Publications

A generative growth model for thalamocortical axonal branching in primary visual cortex.

Similar Datasets

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