Biology subjects

Perin, R.

Publications and source records attributed to Perin, R..

8 recordsLinked to original sources

bioRxiv2026

Uncovering the Basis of Human ConnectomeComplexity: The Role of Neuronal Morphology

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

neuroscience↗

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2020

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Long-term potentiation (LTP) and long-term depression (LTD) of pyramidal cell connections are among the key mechanisms underlying learning and memory in the brain. Despite their important role, only a few of these connections have been characterized in terms of LTP/LTD dynamics, such as the one between layer 5 thick-tufted pyramidal cells (L5-TTPCs). Comparing the available evidence on different pyramidal connection types reveals a large variability of experimental outcomes, possibly indicating the presence of connection-type-specific mechanisms. Here, we show that a calcium-based plasticity rule regulating L5-TTPC synapses holds also for several other pyramidal-to-pyramidal connections in a digital model of neocortical tissue. In particular, we show that synaptic physiology, cell morphology and innervation patterns jointly determine LTP/LTD dynamics without requiring a different model or parameter set for each connection type. We therefore propose that a similar set of plasticity mechanisms is shared by seemingly very different neocortical connections and that only a small number of targeted experiments is required for generating a complete map of synaptic plasticity dynamics in the neocortex.

neuroscience↗

bioRxiv2019

Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks

Why do cortical microcircuits in a variety of brain regions express similar, highly nonrandom, network motifs? To what extent this structure is innate and how much of it is molded by plasticity and learning processes? To address these questions, we developed a general network science framework to quantify the contribution of neurons geometry and their embedding in cortical volume to the emergence of three-neuron network motifs. Applying this framework to a dense in silico reconstructed cortical microcircuits showed that the innate asymmetric neurons geometry underlies the universally recurring motif architecture. It also predicted the spatial alignment of cells composing the different triplets-motifs. These predictions were directly validated via in vitro 12-patch whole-cell recordings (7,309 triplets) from rat somatosensory cortex. We conclude that the local geometry of neurons imposes an innate, already structured, global network architecture, which serves as a skeleton upon which fine-grained structural and functional plasticity processes take place.

neuroscience↗

bioRxiv2019

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

Previous studies based on the Quantal Model for synaptic transmission suggested that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily-releasable pool (NRRP), from paired whole-cell recordings of layer 5 thick tufted pyramidal cell (L5_TTPC) connections in the somatosensory neocortex. Our approach extends the work of Loebel and colleagues to take advantage of a recently reported data-driven biophysical model of neocortical tissue. Using this approach, we estimated NRRP to be between two to three for connections between L5-TTPC. To constrain NRRP values for other connections in the microcircuit, we developed and validated a generalization approach using data on post-synaptic potential (PSP) coefficient of variations (CVs) from literature and matching to in silico experiments. Our study shows that synaptic connections in the neocortex generally are mediated by MVR and provides a data-driven approach to constrain the MVR model parameters of the microcircuit.

neuroscience↗

bioRxiv2018

Objective Classification of Neocortical Pyramidal Cells

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies. This is because of a lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies: the shapes of somata, dendrites, and axons. Even for neurons that are visually different to non-experts, there is no common ground to consistently distinguish morphological types. We found that objective classification is possible with methods from algebraic topology, and that the dendritic arbor is sufficient for reliable identification of distinct types of PCs. We also provide a solution for the more challenging problem of whether two similar neurons belong to different types or to a continuum of the same type. Using this scheme, we objectively identify seventeen types of PCs in the rat somatosensory cortex. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

neuroscience↗

bioRxiv2017

On The Structure Of Cortical Micro-Circuits Inferred From Small Sample Sizes

The structure in cortical micro-circuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue we sought to reproduce the non-random features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity and those with broad degree distributions. To our surprise we found that all these qualitatively distinct topologies could account equally well for all reported non-random features, despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks which differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters which can be reliably estimated given small sample sizes, and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and non-spatial, hierarchical clustering.\n\nSignificance StatementThe connectivity of cortical micro-circuits exhibits features which are inconsistent with a simple random network. Here we show that several classes of network models can account for this non-random structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes many networks may be indistinguishable, despite being globally distinct. We develop a connectivity measure which successfully classifies networks even when estimated locally, with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on non-spatial, asymmetric clustering.

neuroscience↗