Biology subjects

Palmigiano, A.

Publications and source records attributed to Palmigiano, A..

11 recordsLinked to original sources

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2025

Theory of interaction between untuned modulatory inputs and tuned sensory inputs

How does the brain integrate sensory inputs with non-feature-tuned signals, such as those arising from behavioral state changes or neuromodulation? Here, we show that the dynamics of disordered E/I networks with structured, feature-dependent connectivity can be well characterized by an effective model describing interactions between the responses of cells who prefer the current sensory stimulus ("matched" cells) and the responses of cells firing at the baseline. This effective network exhibits strong feedback from the baseline onto the matched responses but weak reverse projections. Thus, an untuned stimulus not only directly drives matched cells, but also indirectly drives them via modulation of the baseline. We demonstrate through a linear response analysis that the baseline effect on the matched response is suppressive if the network is strongly coupled and feedback-inhibition dominated. In particular, in this regime, feature-dependent networks produce "rate reshuffling", wherein untuned optogenetic excitation yields large changes in the individual responses of matched cells without significantly changing their overall firing rate distribution, as the optogenetically-induced baseline response suppresses the matched response. Finally, if multiple sensory stimuli are presented, yielding sublinear response summation ("normalization"), the influence of the baseline on the matched responses is weakened. Thus, an untuned (e.g., optogenetic) stimulus is less suppressive to multiple stimuli than to a single stimulus, making normalization effectively weaker in the presence of an untuned stimulus. Our framework provides the first theory of the interaction of untuned modulatory and tuned sensory inputs, reconciles prior experiments, and provides testable predictions about tuned-untuned interactions in cortical processing. Significance StatementSensory cortex receives both tuned sensory inputs, and untuned signals e.g. from global stimulus changes, behavioral state changes, or neuromodulation. We lack a theory of how these inputs are integrated. We demonstrate reduction of circuit models to a model of interactions between baseline and sensory-stimulus-matched responses, and develop an exact linear response analysis of activity perturbation by untuned inputs. We show baseline-to-matched coupling is strong, while matched-to-baseline is weak, and the former is suppressive given sufficiently strong and inhibition-dominated connectivity. We extend the theory to multiple tuned stimuli. The theory offers a mechanistic explanation of previous surprising observations ("rate reshuffling"), yields new predictions, and provides a general framework for understanding the impact of modulatory influences on sensory processing.

neuroscience↗

bioRxiv2024

Exact linear theory of perturbation response in a space- and feature-dependent cortical circuitmodel

What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here we propose a novel analytical approach: a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity. Importantly and unlike previous approaches, the solution is valid in regimes of strong as well as weak intra-cortical coupling. Analysis reveals the structure of connectivity implied by various features of single-cell perturbation responses, such as the surprisingly narrow spatial radius of nearby excitation beyond which inhibition dominates, the number of transitions between mean excitation and inhibition thereafter, and the dependence of these responses on feature preferences. Comparison of these results to existing optogenetic perturbation data yields constraints on cell-type-specific connection strengths and their tuning dependence. Finally, we provide experimental predictions regarding the response of inhibitory neurons to single-cell perturbations and the modulation of perturbation response by neuronal gain. Significance StatementThe cerebral cortex is strongly recurrently connected with complex wiring rules. This circuitry can now be probed by studying responses to optogenetic perturbations of one or small numbers of cells. However, we currently lack a general theory connecting these responses to underlying circuitry. Here we develop a novel, exactly solvable theory to determine responses to small perturbations from the underlying connectivity. Analysis of these equations reveals simple rules that govern perturbation response patterns. Comparison with experimental data yields new constraints on the connectivity parameters. The theory yields predictions for the responses of unmeasured cell types and in new experimental conditions.

neuroscience↗

bioRxiv2024

Secondary deficiency of neuraminidase 1 contributes to CNS pathology in neurological mucopolysaccharidoses via hypersialylation of brain glycoproteins

Mucopolysaccharidoses (MPS) are lysosomal storage diseases caused by defects in catabolism of glycosaminoglycans. MPS I, II, III and VII are associated with lysosomal accumulation of heparan sulphate and manifest with neurological deterioration. Most of these neurological MPS currently lack effective treatments. Here, we report that, compared to controls, neuraminidase 1 (NEU1) activity is drastically reduced in brain tissues of neurological MPS patients and in mouse models of MPS I, II, IIIA, IIIB and IIIC, but not of other neurological lysosomal disorders not presenting with heparan sulphate storage. We further show that accumulated heparan sulphate disrupts the lysosomal multienzyme complex of NEU1 with cathepsin A (CTSA), {beta}-galactosidase (GLB1) and glucosamine-6-sulfate sulfatase (GALNS) necessary to maintain enzyme activity, and that NEU1 deficiency is linked to partial deficiencies of GLB1 and GALNS in cortical tissues and iPSC-derived cortical neurons of neurological MPS patients. Increased sialylation of N-linked glycans in brain samples of human MPS III patients and MPS IIIC mice implicated insufficient processing of brain N-linked sialylated glycans, except for polysialic acid, which was reduced in the brains of MPS IIIC mice. Correction of NEU1 activity in MPS IIIC mice by lentiviral gene transfer ameliorated previously identified hallmarks of the disease, including memory impairment, behavioural traits, and reduced levels of the excitatory synapse markers VGLUT1 and PSD95. Overexpression of NEU1 also restored levels of VGLUT1-/PSD95-positive puncta in cortical neurons derived from iPSC of an MPS IIIA patient. Together, our data demonstrate that heparan sulphate-induced secondary NEU1 deficiency and aberrant sialylation of glycoproteins implicated in synaptogenesis, memory, and behaviour constitute a novel pathological pathway in neurological MPS spectrum crucially contributing to CNS pathology. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/587986v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@3905c3org.highwire.dtl.DTLVardef@1a9672corg.highwire.dtl.DTLVardef@b4911forg.highwire.dtl.DTLVardef@a3b190_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Boosting of neural circuit chaos at the onset of collective oscillations

Neuronal spiking activity in cortical circuits is often temporally structured by collective rhythms. Rhythmic activity has been hypothesized to regulate temporal coding and to mediate the flexible routing of information flow across the cortex. Spiking neuronal circuits, however, are non-linear systems that, through chaotic dynamics, can amplify insignificant microscopic fluctuations into network-scale response variability. In nonlinear systems in general, rhythmic oscillatory drive can induce chaotic behavior or boost the intensity of chaos. Thus, neuronal oscillations could rather disrupt than facilitate cortical coding functions by flooding the finite population bandwidth with chaotically-boosted noise. Here we tackle a fundamental mathematical challenge to characterize the dynamics on the attractor of effectively delayed network models. We find that delays introduce a transition to collective oscillations, below which ergodic theory measures have a stereotypical dependence on the delay so far only described in scalar systems and low-dimensional maps. We demonstrate that the emergence of internally generated oscillations induces a complete dynamical reconfiguration, by increasing the dimensionality of the chaotic attractor, the speed at which nearby trajectories separate from one another, and the rate at which the network produces entropy. We find that periodic input drive leads to a dramatic increase of chaotic measures at a the resonance frequency of the recurrent network. However, transient oscillatory input only has a moderate role on the collective dynamics. Our results suggest that simple temporal dynamics of the mean activity can have a profound effect on the structure of the spiking patterns and therefore on the information processing capability of neuronal networks.

neuroscience↗

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↗

bioRxiv2022

Targeted cortical stimulation reveals principles of cortical contextual interactions

Cross-orientation suppression is a classic form of contextual normalization in visual cortex. There is substantial evidence that feed-forward processes contribute to cross-orientation suppression, but binocular studies suggest there may also be a contribution from cortical circuits. We used a combined imaging/optogenetic stimulation system to provide patterned optogenetic stimulation to cells in orientation columns that either matched or were orthogonal to the preferred visual orientation of neurons recorded with electrodes. When visual or optogenetic stimulation of columns preferring one orientation was combined with optogenetic stimulation of columns preferring the orthogonal orientation, we observed less suppression than when orthogonal stimulation was provided visually, suggesting that cortical circuits do not provide a large fraction of visual cross-orientation suppression. Integration of visual and optogenetic signals was linear when neurons exhibited low firing rates and became sublinear when neurons exhibited higher firing rates. We probed the nature of sublinearities in cortex by examining the influence of optogenetic stimulation of cortical interneurons. We observed a range of responses, including evidence for paradoxical responses in which interneuron stimulation caused a decrease in inhibitory firing rate, presumably due to the withdrawal of recurrent excitation. These results are compatible with cortical circuits that exhibit strong recurrent excitation with stabilizing inhibition that provides normalization, albeit normalization that is too weak across columns to account for cross-orientation suppression.

neuroscience↗

bioRxiv2021

Antagonistic inhibitory subnetworks control cooperation and competition across cortical space

The cortical microcircuit can dynamically adjust to dramatic changes in the strength, scale, and complexity of its input. In the primary visual cortex (V1), pyramidal cells (PCs) integrate widely across space when signals are weak, but narrowly when signals are strong, a phenomenon known as contrast-dependent surround suppression. Theoretical work has proposed that local interneurons could mediate a shift from cooperation to competition of PCs across cortical space, underlying this computation. We combined calcium imaging and electrophysiology to constrain a stabilized supralinear network model that explains how the four principal cell types in layer 2/3 (L2/3) of mouse V1- somatostatin (SST), parvalbumin (PV), and vasoactive intestinal peptide (VIP) interneurons, and PCs- transform inputs from layer 4 (L4) PCs to encode drifting gratings of varying size and contrast. Using bidirectional optogenetic perturbations, we confirmed key predictions of the model. Our data and modeling showed that recurrent amplification drives a transition from a positive PC[->]VIP{dashv}SST{dashv}PC feedback loop at small size and low contrast to a negative PC[->]SST{dashv}PC feedback loop at large size and high contrast to contribute to this flexible computation. This may represent a widespread mechanism for gating competition across cortical space to optimally meet task demands.

neuroscience↗

bioRxiv2020

Structure and variability of optogenetic responses identify the operating regime of cortex

The visual cortex receives non-sensory inputs containing behavioral and brain state information. Here we propose a parallel between optogenetic and behavioral modulations of activity and characterize their impact on cell-type-specific V1 processing under a common theoretical framework. We infer cell-type-specific circuitry from large-scale V1 recordings and demonstrate that, given strong recurrent excitation, the cell-type-specific responses imply key aspects of the known connectivity. In the inferred models, parvalbumin-expressing (PV), but not other, interneurons have responses to perturbations that we show theoretically imply that their activity stabilizes the circuit. We infer inputs that explain locomotion-induced changes in firing rates and find that, contrary to hypotheses of simple disinhibition, locomotory drive to VIP cells and to SOM cells largely cancel, with enhancement of excitatory-cell visual responses likely due to direct locomotory drive to them. We show that this SOM/VIP cancellation is a property emerging from V1 connectivity structure.

neuroscience↗

bioRxiv2020

Generalized paradoxical effects in excitatory/inhibitory networks

An inhibition-stabilized network (ISN) is a network of excitatory and inhibitory cells at a stable fixed point of firing rates for a given input, for which the excitatory subnetwork would be unstable if inhibitory rates were frozen at their fixed point values. It has been shown that in a low-dimensional model (one unit per neuronal subtype) of an ISN with a single excitatory and single inhibitory cell type, the inhibitory unit shows a "paradoxical" response, lowering (raising) its steady-state firing rate in response to addition to it of excitatory (inhibitory) input. This has been generalized to an ISN with multiple inhibitory cell types: if input is given only to inhibitory cells, the steady-state inhibition received by excitatory cells changes paradoxically, that is, it decreases (increases) if the steady-state excitatory firing rates decrease (increase). We generalize these analyses of paradoxical effects to low-dimensional networks with multiple cell types of both excitatory and inhibitory neurons. The analysis depends on the connectivity matrix of the network linearized about a given fixed point, and its eigenvectors or "modes". We show the following: (1) A given cell type shows a paradoxical change in steady-state rate in response to input it receives, if and only if the network with that cell type omitted has an odd number of unstable modes. Excitatory neurons can show paradoxical responses when there are two or more inhibitory subtypes. (2) More generally, if the cell types are divided into two nonoverlapping subsets A and B, then subset B has an odd (even) number of modes that show paradoxical response if and only if subset A has an odd (even) number of unstable modes. (3) The net steady-state inhibition received by any unstable mode of the excitatory subnetwork will change paradoxically, i.e. in the same direction as the change in amplitude of that mode. In particular, this means that a sufficient condition to determine that a network is an ISN is if, in response to an input only to inhibitory cells, the firing rates of and inhibition received by all excitatory cell types all change in the same direction. This in turn will be true if all E cells and all inhibitory cell types that connect to E cells change their firing rates in the same direction.

neuroscience↗

bioRxiv2019

Interrogating theoretical models of neural computation with deep inference

1A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon - whether behavioral or a pattern of neural activity - and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

neuroscience↗