Biology subjects

Dipoppa, M.

Publications and source records attributed to Dipoppa, M..

12 recordsLinked to original sources

bioRxiv2026

Parvalbumin interneuron maturation reorganizes cortical population activity structure during development

During brain development, spontaneous cortical activity undergoes a dramatic transformation from highly synchronous network events to less correlated and sparse neuronal firing that is thought to underlie more flexible neural coding. However, how the structure of population activity changes across this transition, what circuit mechanisms drive this reorganization, and the implications for information encoding have not been fully established. Using longitudinal in vivo calcium imaging in early postnatal mice, we demonstrate that cortical network desynchronization during the second postnatal week is accompanied by a redistribution of fractional variance from dominant, low-dimensional modes into many weaker dimensions, and a growing influence of GABAergic interneurons (INs) on the network. All-optical circuit interrogation revealed that parvalbumin (PV) INs, but not somatostatin (SST) INs, are sufficient to desynchronize the network and shift the distribution of population variance. Through targeted ablation of a fraction of PV INs, we show dimensionality is highly correlated with stimulus decoding accuracy. Furthermore, PV INs undergo a sharp increase in transcriptional maturation between postnatal day (P) 8 and 11, during which they preferentially enrich for synaptic genes. Lastly, a novel, biologically plausible, multi-cell-type computational model reproduced the developmental desynchronization and rise in dimensionality. Strikingly, without being constrained by perturbation data, the model accurately predicted the effects of each IN class on the network that we observed empirically.

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bioRxiv2025

A universal power law optimizes energy and representation fidelity in visual adaptation

Sensory systems continuously adapt their responses based on the probability of encountering a given stimulus. In the mouse primary visual cortex (V1), the population response magnitude is a power law of the stimulus probability in the environment. For a given stimulus type (e.g., oriented gratings), the power law's exponent is invariant to changes in statistical environments, enabling predictions of population responses to new environments. Here, we aim to provide a normative explanation for the power law behavior. We develop an efficient coding model where neurons adjust their firing rates through optimization of a weighted objective, hypothesizing that the neural population adapts to enhance stimulus detection and discrimination while reducing overall neural activity. We show that a model balancing representational fidelity and energy efficiency matches the power law observed experimentally for a wide range of parameters, while models of adaptation with alternative coding objectives and resource constraints are unable to reproduce this empirical observation. Furthermore, we account for the invariance of the power law's exponent across environmental changes by linking it to the dependence of tuning curve modulation on stimulus probability. Finally, we explain how variations in the exponent with different stimulus types (e.g., natural stimuli) result from changes in the minimal distances between neural representations, in agreement with experimental findings. We conclude that a universal power law of adaptation can be explained as a trade-off between representation fidelity and energy cost.

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bioRxiv2025

Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual Cortex

In sensory cortex of brain it is often the case that neurons are spatially organized by their functional properties. A hallmark of primary visual cortex (V1) in higher mammals is a columnar functional map, where neurons tuned to different stimuli features are regularly organized in space. However, rodent visual cortex is at odds with this rule and lacks any spatially ordered functional architecture, and rather neuron feature preference is haphazardly organized in patterns termed salt-and-pepper. This sharp contrast in feature organization between the visual cortices of rodents and higher mammals has been a persistent mystery, fueled in part by abundant evidence of conserved cortical physiology between species. In this work, we applied a novel GCaMP indicator that are localized in the nucleus of neurons during two-photon imaging in mouse V1, which enabled us to overcome most spurious spatially correlated activity due to fluorescence contamination, and to ensure a faithful observation of functional organization over space. We found that the orientation tuning properties of distant neuron pairs (> 20 {micro}m) are irregularly and randomly organized, while neuron pairs that are extremely close (< 20 {micro}m) have strongly correlated orientation tuning, indicating a narrow yet strong spatially clustered organization of orientation preference, which we term micro-clustered organization. Exploring a circuit-based model of recurrently coupled mouse V1 we derived two key predictions for the micro-cluster: spatially localized recurrent connections over a comparable narrow spatial scale, and common relative spatial spreads of balanced excitation and inhibition in the network over broad spatial scales. These predictions are validated by both anatomical and optogenetic-based physiological circuit mapping experiments. Altogether, our work takes an important step in building a circuit-based theory of visual processing in mouse V1 over spatial scales that are often ignored, yet contain powerful synaptic interactions.

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bioRxiv2024

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. This improvement arose mainly from how adaptation reshaped the geometry of the mean population responses, with changes in the noise structure playing a minor role. Locomotion modulated the population responses along a direction largely independent of adaptation, allowing the adaptation-induced increase in discriminability to persist during locomotion. Finally, artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints reproduced the increase in discriminability and the decrease in responses. The paradoxical effects of adaptation are thus consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.

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bioRxiv2024

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in different degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast. Our findings reveal that, for a given stimulus, the population response can be described by a vector function r(gec), where the gain ge is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well coordinated among members of a neural population.

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bioRxiv2023

On the contrast response function of adapted neural populations

The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. How these two variables combine to influence the overall response of cortical populations remains unknown. Here we show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power-law that factors the intensity of a stimulus and its probability.

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bioRxiv2023

A power law of cortical adaptation

How do neural populations adapt to the time-varying statistics of sensory input? To investigate, we measured the activity of neurons in primary visual cortex adapted to different environments, each associated with a distinct probability distribution over a stimulus set. Within each environment, a stimulus sequence was generated by independently sampling form its distribution. We find that two properties of adaptation capture how the population responses to a given stimulus, viewed as vectors, are linked across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response directions are largely invariant. These rules can be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

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bioRxiv2022

Unifying model for three forms of contextual modulation including feedback input from higher visual areas

Sensory systems must combine local features with context to infer meaning. Accordingly, context profoundly influences neural responses. We developed a unified circuit model demonstrating how feedforward and feedback inputs are integrated to produce three forms of contextual effects in mouse primary visual cortex (V1). First, reanalyzing existing data, we discovered that increasing stimulus size only weakly increases the area of V1 neural response, conflicting with previous models of surround suppression (SS). Second, through modeling, we found that, in Layer 2/3, (1) SS and its contrast dependence are largely inherited from Layer 4; (2) Inverse responses (IR) - size-tuned responses to a gray "hole" in a full-field grating - are driven by feedback connections provided they are sufficiently wide; (3) Cross-orientation surround facilitation is induced by the summation of feedback input driving IR with the feedforward-driven classical center response. The model accounts for many previous findings and makes multiple testable predictions. HighlightsO_LIOne model explains three different types of contextual modulation C_LIO_LIThe widths of spatial response patterns grow much more slowly than stimulus size. C_LIO_LIInverse responses depend on the geometry of feedback response fields and projections C_LIO_LISummation of classical and inverse response accounts for surround facilitation. C_LI

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bioRxiv2021

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed the exquisite diversity of cortical inhibitory neurons1-7, but it is not known whether these fine molecular subtypes have correspondingly diverse activity patterns in the living brain. Here, we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We used transcriptomic clusters (t-types)4 to classify inhibitory neurons imaged in layers 1-3 using a three-level hierarchy of 5 Families, 11 Classes, and 35 t-types. Visual responses differed significantly only across Families, but modulation by brain state differed at all three hierarchical levels. Nevertheless, this diversity could be predicted from the first transcriptomic principal component, which predicted a cell types brain state modulation and correlations with simultaneously recorded cells. Inhibitory t-types with narrower spikes, lower input resistance, weaker adaptation, and less axon in layer 1 as determined in vitro8 fired more in resting, oscillatory brain states. Transcriptomic types with the opposite properties fired more during arousal. The former cells had more inhibitory cholinergic receptors, and the latter more excitatory receptors. Thus, despite the diversity of V1 inhibitory neurons, a simple principle determines how their joint activity shapes state-dependent cortical processing.

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bioRxiv2021

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

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bioRxiv2020

A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex

Context guides perception by influencing the saliency of sensory stimuli. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The mechanisms that remove suppression when stimulus and surround differ remain unclear. Here we use optical recordings, manipulations, and computational modelling to show that a disinhibitory circuit consisting of vasoactive-intestinal-peptide-expressing (VIP) and somatostatin-expressing (SOM) inhibitory neurons modulates responses in mouse visual cortex depending on the similarity between stimulus and surround. When the stimulus and the surround are similar, VIP neurons are inactive and SOM neurons suppress excitatory neurons. However, when the stimulus and the surround differ, VIP neurons are active, thereby inhibiting SOM neurons and relieving excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit which contributes to contextual modulation and may regulate perceptual saliency.

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bioRxiv2019

Understanding the functional and structural differences across excitatory and inhibitory neurons

One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.

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