Biology subjects

Tolias, A.

Publications and source records attributed to Tolias, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Inhibition benefits neural system identification

Neural system identification approaches use empirical data to fit the stimulus-response functions of neurons. Augmented by deep neural networks, such models have achieved high predictive performance and allow to perform in silico experiments to test hypotheses. Yet, many of these methods ignore common features in visual systems, such as inhibitory interactions between neurons, which are essential for nonlinear neural computation. Here, we incorporate inhibition as an inductive bias into a deep model for neural prediction and investigate the influence of inhibition on the learned transfer functions. To this end, we employ difference-of-Gaussian (subtraction) and within-channel divisive normalization (division), which have been proposed to relate inhibition to neural processing, in deep networks for predicting visual responses. We observe that incorporating such operations maintains the predictive performance and encourages the learning of biologically plausible kernels reminiscent of neural representation in early vision. Additionally, our in silico experiments demonstrate that implementing either sub-tractive or divisive operation benefits the learning of surround suppression but not cross-orientation inhibition. Interestingly, while division increases the sparsity of activation and reduces the sparsity of weights, subtraction has the reverse effect.

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bioRxiv2026

The functional organization of retinal input to the mouse superior colliculus

The superior colliculus integrates retinal input to drive rapid, adaptive visual behavior, yet how the functional diversity of retinal ganglion cell types is represented in superior colliculus remains poorly understood. Using chronic two-photon calcium imaging of retinal ganglion cell axonal boutons in awake mice, we recorded over 200,000 boutons across superficial superior colliculus layers -- a scale that enabled systematic comparison with large-scale ex vivo retinal datasets. This revealed that the superior colliculus receives a near-complete sampling of retinal ganglion cell functional diversity. Functionally distinct response types were organized in systematic laminar gradients: not only response properties such as direction selectivity and contrast suppression, but retinal response types themselves varied systematically with depth. To probe how this organized input encodes natural scenes, we trained a "digital twin" deep network model on natural movie responses and validated its generalization to parametric stimuli, including cell type identification. Leveraging this model to generate predicted responses to looming stimuli, we identify a discrete subset of retinal response types tuned for collision detection at low angular thresholds -- a specialization embedded within a broader, non-specialized retinal population. The digital twin is made publicly available as a community resource. Together, these findings provide a comprehensive functional map of retinal drive to the superior colliculus and an in silico platform for linking retinal cell types to behaviorally relevant superior colliculus computations.

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bioRxiv2025

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features--one that enhances and one that suppresses activity--around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models--deep learning-based predictive models of biological neurons--we were able to systematically identify each neurons preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

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bioRxiv2025

Temporal coding carries more stable cortical visual representations than firing rate over time

The brains ability to stably represent recurring visual scenes is crucial for behavior. Previous studies have used slow dynamic (1-5 seconds) rate code measurements to study visual tuning, revealing varying degrees of gradual activity changes over time or "representational drifts." However, it remains unclear if there is an underlying neural code that maintains the encoding of information stable over time. In this study, we extracted structures in fast (tens of milliseconds) temporal responses and explored the role of such temporal codes in supporting the stability of visual representations. We tracked the spiking activity of the same visual cortical populations in male mice for 15 consecutive days using custom-developed, large-scale, ultraflexible electrode arrays. Across various types of stimuli, we found that neurons exhibited varying degrees of day-to-day stability in their firing rate-based tuning. The across day stability correlated with tuning reliability. Notably, accounting for spiking temporal dynamics increased single neuron tuning stability, especially for less reliable neurons. Temporal coding further improved population representation discriminability and decoding accuracy. The stability of temporal codes was more correlated with network functional connectivity than rate coding. These results show that temporal coding is crucial for stably encoding sensory stimuli, suggesting its significant role in ensuring consistent sensory experiences.

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bioRxiv2024

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-m-thick, mechanically flexible micro-electrocorticography (ECoG) BCI, integrating a 256x256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

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bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

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bioRxiv2022

It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

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bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

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bioRxiv2022

Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex

More than a dozen excitatory cell types have been identified in the mouse primary visual cortex (V1) based on transcriptomic, morphological and in vitro electrophysiological features. However, the functional landscape of excitatory neurons with respect to their responses to visual stimuli is currently unknown. Here, we combined large-scale two-photon imaging and deep learning neural predictive models to study the functional organization of mouse V1 using digital twins. Digital twins enable exhaustive in silico functional characterization providing a bar code summarizing the input-output function of each neuron. Clustering the bar codes revealed a continuum of function with around 30 modes. Each mode represented a group of neurons that exhibited a specific combination of stimulus selectivity and nonlinear response properties such as cross-orientation inhibition, size-contrast tuning and surround suppression. These non-linear properties were expressed independently spanning all possible combinations across the population. This combinatorial code provides the first large-scale, data-driven characterization of the functional organization of V1. This powerful approach based on digital twins is applicable to other brain areas and to complex non-linear systems beyond the brain.

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bioRxiv2022

Robust deep learning object recognition models rely on low frequency information in natural images

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.

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bioRxiv2021

Behavioral state tunes mouse vision to ethological features through pupil dilation

Sensory processing changes with behavioral context to increase computational flexibility. In the visual system, active behavioral states enhance sensory responses but typically leave the preferred stimuli of neurons unchanged. Here we find that behavioral state does modulate stimulus selectivity in mouse visual cortex in the context of colored natural scenes. Using population imaging, behavior, pharmacology, and deep neural networks, we identified a shift of color selectivity towards ultraviolet stimuli exclusively caused by pupil dilation, resulting in a dynamic switch from rod to cone photoreceptors, extending their role beyond night and day vision. This facilitated the detection of ethological stimuli, such as aerial predators against the twilight sky. In contrast to previous studies that have used pupil dilation as an indirect measure of brain state, our results suggest that the brain uses pupil dilation itself to differentially recruit rods and cones on short timescales to tune visual representations to behavioral demands.

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bioRxiv2021

DataJoint Elements: Data Workflows for Neurophysiology

A new resource--DataJoint Elements--provides modular designs for assembling complete workflow solutions to organize data and computations for common neurophysiology experiments. The designs are derived from working solutions developed in leading research groups using the open-source DataJoint framework to integrate data collection and analysis in collaborative workflows.

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bioRxiv2020

Generalization in data-driven models of primary visual cortex

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. gener-alizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neurons receptive field po-sition. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16.

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bioRxiv2020

Signal separability in integrated neurophotonics

A new modality Photonic probes record fluorescent signals by using arrays of light emitters and detectors embedded in neural tissue. Neither the emitted nor collected light fields are focused. Instead, in proposed configurations, hundreds of emitters will form rapid sequences of structured illumination patterns--providing sufficient spatial and temporal differentiation of neural signals for computational demixing. Here we define criteria for evaluating probe designs for achieving better signal separability. We find that probe geometry has profound, often unintuitive, effects on the separability of neural signals, providing initial design guidelines to achieve separation of individual cells in densely labeled populations.

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bioRxiv2020

EASE: EM-Assisted Source Extraction from calcium imaging data

Combining two-photon calcium imaging (2PCI) and electron microscopy (EM) provides arguably the most powerful current approach for connecting function to structure in neural circuits. Recent years have seen dramatic advances in obtaining and processing CI and EM data separately. In addition, several joint CI-EM datasets (with CI performed in vivo, followed by EM reconstruction of the same volume) have been collected. However, no automated analysis tools yet exist that can match each signal extracted from the CI data to a cell segment extracted from EM; previous efforts have been largely manual and focused on analyzing calcium activity in cell bodies, neglecting potentially rich functional information from axons and dendrites. There are two major roadblocks to solving this matching problem: first, dense EM reconstruction extracts orders of magnitude more segments than are visible in the corresponding CI field of view, and second, due to optical constraints and non-uniform brightness of the calcium indicator in each cell, direct matching of EM and CI spatial components is nontrivial. In this work we develop a pipeline for fusing CI and densely-reconstructed EM data. We model the observed CI data using a constrained nonnegative matrix factorization (CNMF) framework, in which segments extracted from the EM reconstruction serve to initialize and constrain the spatial components of the matrix factorization. We develop an efficient iterative procedure for solving the resulting combined matching and matrix factorization problem and apply this procedure to joint CI-EM data from mouse visual cortex. The method recovers hundreds of dendritic components from the CI data, visible across multiple functional scans at different depths, matched with densely-reconstructed three-dimensional neural segments recovered from the EM volume. We publicly release the output of this analysis as a new gold standard dataset that can be used to score algorithms for demixing signals from 2PCI data. Finally, we show that this database can be exploited to (1) learn a mapping from 3d EM segmentations to predict the corresponding 2d spatial components estimated from CI data, and (2) train a neural network to denoise these estimated spatial components. This neural network denoiser is a stand-alone module that can be dropped in to enhance any existing 2PCI analysis pipeline.

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bioRxiv2020

Distinct organization of two cortico-cortical feedback pathways

Neocortical feedback is critical for processes like attention, prediction, and learning. A mechanistic understanding of its function requires deciphering its cell-type wiring logic. Recent studies revealed a disinhibitory circuit between motor and sensory areas in mice, where feedback preferentially targets vasointestinal peptide-expressing interneurons, in addition to pyramidal cells. It is unknown whether this circuit motif is a general cortico-cortical feedback organizing principle. Combining multiple simultaneous whole-cell recordings with optogenetics we found that in contrast to this wiring rule, feedback between the hierarchically organized visual areas (lateral-medial to V1) preferentially activated somatostatin-expressing interneurons. Functionally, both feedback circuits temporally sharpened feed-forward excitation by eliciting a transient increase followed by a prolonged decrease in pyramidal firing rate under sustained feed-forward input. However, under feed-forward transient input, the motor-sensory feedback facilitated pyramidal cell bursting while visual feedback increased spike time precision. Our findings argue for multiple feedback motifs implementing different dynamic non-linear operations.

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bioRxiv2020

Phenotypic variation within and across transcriptomic cell types in mouse motor cortex

Cortical neurons exhibit astounding diversity in gene expression as well as in morphological and electrophysiological properties. Most existing neural taxonomies are based on either transcriptomic or morpho-electric criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells. Here we used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (Vip, Pvalb, Sst, etc.) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well-separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neural types in the neocortex do not always form discrete entities. Instead, neurons follow a hierarchy consisting of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.

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bioRxiv2019

Precision calcium imaging of dense neural populations via a cell body-targeted calcium indicator

Methods for one-photon fluorescent imaging of calcium dynamics in vivo are popular due to their ability to simultaneously capture the dynamics of hundreds of neurons across large fields of view, at a low equipment complexity and cost. In contrast to two-photon methods, however, one-photon methods suffer from higher levels of crosstalk between cell bodies and the surrounding neuropil, resulting in decreased signal-to-noise and artifactual correlations of neural activity. Here, we address this problem by engineering cell body-targeted variants of the fluorescent calcium indicator GCaMP6f. We screened fusions of GCaMP6f to both natural as well as engineered peptides, and identified fusions that localized GCaMP6f to within approximately 50 microns of the cell body of neurons in live mice and larval zebrafish. One-photon imaging of soma-targeted GCaMP6f in dense neural circuits reported fewer artifactual spikes from neuropil, increased signal-to-noise ratio, and decreased artifactual correlation across neurons. Thus, soma-targeting of fluorescent calcium indicators increases neuronal signal fidelity and may facilitate even greater usage of simple, powerful, one-photon methods of population imaging of neural calcium dynamics.

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