Biology subjects

Sanzeni, A.

Publications and source records attributed to Sanzeni, A..

8 recordsLinked to original sources

bioRxiv2024

Connectome-based models of feature selectivity in a cortical circuit

Feature selectivity, the ability of neurons to respond preferentially to specific stimuli, is a defining property of cortical computation. Competing theories attribute selectivity to structured, tuning-dependent "like-to-like" feedforward or recurrent connections, whereas others propose that it can emerge without specific structure in randomly connected, inhibition-dominated networks. The relative contribution of these mechanisms remains unclear. Here, we investigate the circuit basis of feature selectivity in mouse visual cortex, focusing on how orientation selectivity arises in layer 2/3 neurons driven by input from layer 4. We developed a data-driven modeling framework integrating network modeling with functional imaging and synaptic-resolution connectomics from the MICrONS dataset. Our analyses show that randomness in connectivity is the dominant source of selectivity, while structured "like-to-like" feedforward and recurrent connections play comparable secondary roles in amplifying it. These findings refine classical theories of cortical selectivity and demonstrate how connectome-constrained modeling can reveal the circuit principles underlying cortical computation.

neuroscience↗

bioRxiv2024

Inter- and Intrahemispheric Sources of Vestibular Signals to V1

Head movements are sensed by the vestibular organs. Unlike classical senses, signals from vestibular organs are not conveyed to a dedicated cortical area but are broadcast throughout the cortex. Surprisingly, the routes taken by vestibular signals to reach the cortex are still largely uncharted. Here we show that the primary visual cortex (V1) receives real-time head movement signals -- direction, velocity, and acceleration -- from the ipsilateral pulvinar and contralateral visual cortex. The ipsilateral pulvinar provides the main head movement signal, with a bias toward contraversive movements (e.g. clockwise movements in left V1). Conversely, the contralateral visual cortex provides head movement signals during ipsiversive movements. Crucially, head movement variables encoded in V1 are already encoded in the pulvinar, suggesting that those variables are computed subcortically. Thus, the convergence of inter- and intrahemispheric signals endows V1 with a rich representation of the animals head movements.

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bioRxiv2022

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons, yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low/high rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

neuroscience↗

bioRxiv2020

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order [Formula]. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of order 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

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bioRxiv2019

Response nonlinearities in networks of spiking neurons

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities contradict the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the observed diversity of non-linearities observed in cortical networks. AUTHOR SUMMARYModels of cortical networks are often studied in the strong coupling limit, where the so-called balanced state emerges. In this strong coupling limit, networks exhibit without fine tuning, a number of ubiquitous properties of cortex, such as the irregular nature of neuronal firing. However, it fails to account for nonlinear summation of inputs, since the strong coupling limit leads to a linear network transfer function. We show that, in networks of spiking neurons, nonlinearities at response-onset and saturation emerge at finite coupling. Critically, for realistic parameter values, both types of nonlinearities are observed at experimentally observed rates. Thus, we propose that these models could explain experimentally observed nonlinearities.

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bioRxiv2019

Progressive recruitment of distal MEC-4 channels determines touch response strength in C. elegans

Touch deforms, or strains, the skin beyond the immediate point of contact. The spatiotemporal nature of the touch-induced strain fields depend on the mechanical properties of the skin and the tissues below. Somatosensory neurons that sense touch branch out within the skin and rely on a set of mechano-electrical transduction channels distributed within their dendrites to detect mechanical stimuli. Here, we sought to understand how tissue mechanics shape touch-induced mechanical strain across the skin over time and how individual channels located in different regions of the strain field contribute to the overall touch response. We leveraged C. elegans touch receptor neurons (TRNs) as a simple model amenable to in vivo whole-cell patch clamp recording and an integrated experimental-computational approach to dissect the mechanisms underlying the spatial and temporal dynamics that we observed. Consistent with the idea that strain is produced at a distance, we show that delivering strong stimuli outside the anatomical extent of the neuron is sufficient to evoke MRCs. The amplitude and kinetics of the MRCs depended on both stimulus displacement and speed. Finally, we found that the main factor responsible for touch sensitivity is the recruitment of progressively more distant channels by stronger stimuli, rather than modulation of channel open probability. This principle may generalize to somatosensory neurons with more complex morphologies.\n\nSummaryThrough experiment and simulation, Katta et al. reveal that pushing faster and deeper recruits more and more distant mechano-electrical transduction channels during touch. The net result is a dynamic receptive field whose size and shape depends on tissue mechanics, stimulus parameters, and channel distribution within sensory neurons.

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bioRxiv2018

Tissue mechanics and somatosensory neural responses govern touch sensation in C. elegans

The sense of touch hinges on tissues transducing stimuli applied to the skin and somatosensory neurons converting mechanical inputs into currents. Like mammalian Pacinian corpuscles, the light-touch response of the prime model organism C. elegans adapts rapidly, and is symmetrically activated by the onset and offset of a step indentation. Here, we propose a quantitative model that combines transduction of stimuli across the skin and subsequent gating of mechanoelectrical channels. For mechanics, we use an elastic model based on geometrically-nonlinear deformations of a pressurized cylindrical shell. For gating, we build upon consequences of the dermal layers thinness and tangential stimuli. Our model demonstrates how the onset-offset symmetry arises from the coupling of mechanics and adaptation, and accounts for experimental neural responses to a broad variety of stimuli. Predicted effects of modifications in the mechanics or the internal pressure of the body are tested against mechanical and neurophysiological experiments.

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bioRxiv2016

Complete coverage of space favors modularity of the grid system in the brain

Grid cells in the entorhinal cortex fire when animals that are exploring a certain region of space occupy the vertices of a triangular grid that spans the environment. Different neurons feature triangular grids that differ in their properties of periodicity, orientation and ellipticity. Taken together, these grids allow the animal to maintain an internal, mental representation of physical space. Experiments show that grid cells are modular, i.e. there are groups of neurons which have grids with similar periodicity, orientation and ellipticity. We use statistical physics methods to derive a relation between variability of the properties of the grids within a module and the range of space that can be covered completely (i.e. without gaps) by the grid system with high probability. Larger variability shrinks the range of representation, providing a functional rationale for the experimentally observed co-modularity of grid cell periodicity, orientation and ellipticity. We obtain a scaling relation between the number of neurons and the period of a module, given the variability and coverage range. Specifically, we predict how many more neurons are required at smaller grid scales than at larger ones.

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