Biology subjects

Shea-Brown, E.

Publications and source records attributed to Shea-Brown, E..

At least 19 recordsLinked to original sources

bioRxiv2025

Temporal Deconvolution of Mesoscale Recordings

1Mesoscale calcium recordings, such as wide-field imaging, enable high-temporal-resolution recordings of neuronal activity across extensive brain regions. However, because these recordings capture light emitted by calcium indicator fluorescence, the underlying neural activity is obscured by the temporal dynamics of the indicators. Here, we develop and evaluate four deconvolution methods to recover neuronal spiking rates from fluorescence traces recorded in wide-field imaging. The methods--Dynamically-Binning (using adaptive discrete-time bins), Continuously-Varying (estimating smooth spiking rates), First-Differences (providing efficient estimation), and a modified Wiener Filter (robust to large fluorescence magnitude variations)-consistently outperform the previously used adapted Lucy-Richardson algorithm on both synthetic data and simultaneous fluorescence-spike recordings. Critically, we demonstrate that using raw fluorescence signals in advanced analysis methods can yield distorted results, including spuriously inflated correlations between brain regions activity. This highlights the necessity of accurate temporal deconvolution for reliable interpretation of brain activity.

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bioRxiv2025

Global and local origins of trial-to-trial spike count variability in visual cortex

Sensory neuron spiking responses vary across repeated presentations of the same stimuli, but whether this trial-to-trial variability represents noise versus unidentified signals remains unresolved. Some of the variability can be attributed to correlations between neural activity and arousal, locomotion, and other overt movements. We hypothesized that correlations with global activity factors, i.e., patterns of neural activity observable in other brain regions, may explain additional variability in spike count responses of visual cortical neurons. To test this, we used Neuropixels 2.0 probes to record neural activity in mouse primary visual cortex (V1) while subjects passively viewed images. We recorded videos of behavior alongside neural activity from other brain regions, either spiking activity of neural populations in anterior cingulate area (ACA) or widefield calcium signals from across the dorsal cortex. We then used a model based on reduced rank regression to partition the explainable variability of visual cortical responses by source. Some of the trial-to-trial variability in V1 spike counts was attributable to locally shared patterns of activity uncorrelated with either behavior or global activity patterns. Locally shared activity patterns explained trial-to-trial variability that was in excess of Poisson spike generation. Of the parts of variability attributable to non-local sources, global cortical activity predicted significantly more V1 spike count variability than behavioral factors. Additionally, behavioral factors explained little variability uniquely and comprised a geometric subspace of the globally predictable V1 activity. Finally, optogenetically perturbing ACA directly impacted V1 activity, and ACA activity patterns predicted V1 spike count variability even on trials without overt behaviors. Our data indicate that globally shared factors from other cortical areas contribute substantially to shared spike count variability in V1, with only a minority of shared variability confined to local V1 circuits.

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bioRxiv2025

Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers

A key challenge in analyzing neuroscience datasets is the profound variability they exhibit across sessions, animals, and data modalities-i.e., heterogeneity. Several recent studies have demonstrated performance gains from pretraining neural foundation models on multi-session datasets, seemingly overcoming this challenge. However, these studies typically lack finegrained data scaling analyses. It remains unclear how different sources of heterogeneity influence model performance as the amount of pretraining data increases, and whether all sessions contribute equally to downstream performance gains. In this work, we systematically investigate how data heterogeneity impacts the scaling behavior of neural data transformers (NDTs) in neural activity prediction. We found that explicit sources of heterogeneity, such as brain region mismatches among sessions, reduced scaling benefits of neuron-level and region-level activity prediction performances. For tasks that do exhibit consistent scaling, we identified implicit data heterogeneity arising from cross-session variability. Through our proposed session-selection procedure, models pretrained on as few as five selected sessions outperformed those pretrained on the entire dataset of 84 sessions. Our findings challenge the direct applicability of traditional scaling laws to neural data and suggest that prior reports of multi-session scaling benefits may need to be re-examined in the light of data heterogeneity. This work both highlights the importance of incremental data scaling analyses and suggests new avenues toward optimally selecting pretraining data when developing foundation models on large-scale neuroscience datasets.

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bioRxiv2024

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

AbstractRecurrent neural networks exhibit chaotic dynamics when the variance in their connection strengths exceed a critical value. Recent work indicates connection variance also modulates learning strategies; networks learn "rich" representations when initialized with low coupling and "lazier"solutions with larger variance. Using Watts-Strogatz networks of varying sparsity, structure, and hidden weight variance, we find that the critical coupling strength dividing chaotic from ordered dynamics also differentiates rich and lazy learning strategies. Training moves both stable and chaotic networks closer to the edge of chaos, with networks learning richer representations before the transition to chaos. In contrast, biologically realistic connectivity structures foster stability over a wide range of variances. The transition to chaos is also reflected in a measure that clinically discriminates levels of consciousness, the perturbational complexity index (PCIst). Networks with high values of PCIst exhibit stable dynamics and rich learning, suggesting a consciousness prior may promote rich learning. The results suggest a clear relationship between critical dynamics, learning regimes and complexity-based measures of consciousness.

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bioRxiv2022

Heterogeneity in Neuronal Dynamics is Learned by Gradient Descent for Temporal Processing Tasks

Individual neurons in the brain have complex intrinsic dynamics that are highly diverse. We hypothesize that the complex dynamics produced by networks of complex and heterogeneous neurons may contribute to the brains ability to process and respond to temporally complex data. To study the role of complex and heterogeneous neuronal dynamics in network computation, we develop a rate-based neuronal model, the generalized-leaky-integrate-and-firing-rate (GLIFR) model, which is a rate-equivalent of the generalized-leaky-integrate-and-fire model. The GLIFR model has multiple dynamical mechanisms which add to the complexity of its activity while maintaining differentiability. We focus on the role of after-spike currents, currents induced or modulated by neuronal spikes, in producing rich temporal dynamics. We use machine learning techniques to learn both synaptic weights and parameters underlying intrinsic dynamics to solve temporal tasks. The GLIFR model allows us to use standard gradient descent techniques rather than surrogate gradient descent, which has been utilized in spiking neural networks. After establishing the ability to optimize parameters using gradient descent in single neurons, we ask how networks of GLIFR neurons learn and perform on temporally challenging tasks, such as sinusoidal pattern generation and sequential MNIST. We find that these networks learn a diversity of parameters, which gives rise to diversity in neuronal dynamics. We also observe that training networks on the sequential MNIST task leads to formation of cell classes based on the clustering of neuronal parameters. GLIFR networks have mixed performance when compared to vanilla recurrent neural networks but appear to be more robust to random silencing. When we explore these performance gains further, we find that both the ability to learn heterogeneity and the presence of after-spike currents contribute. Our work both demonstrates the computational robustness of neuronal complexity and diversity in networks and demonstrates a feasible method of training such models using exact gradients.

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bioRxiv2021

A biologically inspired architecture with switching units can learn to generalize across backgrounds

Humans and other animals navigate different landscapes and environments with ease, a feat that requires the brains ability to rapidly and accurately adapt to different visual domains, generalizing across contexts/backgrounds. Despite recent progress in deep learning applied to classification and detection in the presence of multiple confounds including contextual ones [25, 30], there remain important challenges to address regarding how networks can perform context-dependent computations and how contextually-invariant visual concepts are formed. For instance, recent studies have shown artificial networks that repeatedly misclassified familiar objects set on new backgrounds, e.g. incorrectly labelling known animals when they appeared in a different setting [3]. Here, we show how a bio-inspired network motif can explicitly address this issue. We do this using a novel dataset which can be used as a benchmark for future studies probing invariance to backgrounds. The dataset consists of MNIST digits of varying transparency, set on one of two backgrounds with different statistics: a Gaussian noise or a more naturalistic background from the CIFAR-10 dataset. We use this dataset to learn digit classification when contexts are shown sequentially, and find that both shallow and deep networks have sharply decreased performance when returning to the first background after experience learning the second - the catastrophic forgetting phenomenon in continual learning. To overcome this, we propose an architecture with additional " switching" units that are activated in the presence of a new background. We find that the switching network can learn the new context even with very few switching units, while maintaining the performance in the previous context - but that they must be recurrently connected to network layers. When the task is difficult due to high transparency, the switching network trained on both contexts outperforms networks without switching trained on only one context. The switching mechanism leads to sparser activation patterns, and we provide intuition for why this helps to solve the task. We compare our architecture with other prominent learning methods, and find that elastic weight consolidation is not successful in our setting, while progressive nets are more complex but less effective. Our study therefore shows how a bio-inspired architectural motif can contribute to task generalization across context.

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bioRxiv2021

CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex

Convolutional neural networks trained on object recognition derive inspiration from the neural architecture of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the deep hierarchical organization of primates, the visual system of the mouse has a shallower arrangement. Since mice and primates are both capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a novel framework for building a biologically constrained convolutional neural network model of the mouse visual cortex. The architecture and structural parameters of the network are derived from experimental measurements, specifically the 100-micrometer resolution interareal connectome, the estimates of numbers of neurons in each area and cortical layer, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. Using a well-studied image classification task as our working example, we demonstrate the computational capability of this mouse-sized network. Given its relatively small size, MouseNet achieves roughly 2/3rds the performance level on ImageNet as VGG16. In combination with the large scale Allen Brain Observatory Visual Coding dataset, we use representational similarity analysis to quantify the extent to which MouseNet recapitulates the neural representation in mouse visual cortex. Importantly, we provide evidence that optimizing for task performance does not improve similarity to the corresponding biological system beyond a certain point. We demonstrate that the distributions of some physiological quantities are closer to the observed distributions in the mouse brain after task training. We encourage the use of the MouseNet architecture by making the code freely available. Author summaryTask-driven deep neural networks have shown great potential in predicting functional responses of biological neurons. Nevertheless, they are not precise biological analogues, raising questions about how they should be interpreted. Here, we build new deep neural network models of the mouse visual cortex (MouseNet) that are biologically constrained in detail, not only in terms of the basic structure of their connectivity, but also in terms of the count and hence density of neurons within each area, and the spatial extent of their projections. Equipped with the MouseNet model, we can address key questions about mesoscale brain architecture and its role in task learning and performance.We ask, and provide a first set of answers, to: What is the performance of a mouse brain-sized - and mouse brain-structured - model on benchmark image classification tasks? How does the training of a network on this task affect the functional properties of specified layers within the biologically constrained architecture - both overall, and in comparison with recorded function of mouse neurons? We anticipate much future work on allied questions, and the development of more sophisticated models in both mouse and other species, based on the freely available MouseNet model and code which we develop and provide here.

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bioRxiv2021

Learning to Predict in Networks with Heterogeneous and Dynamic Synapses

AO_SCPLOWBSTRACTC_SCPLOWA salient difference between artificial and biological neural networks is the complexity and diversity of individual units in the latter (Tasic et al., 2018). This remarkable diversity is present in the cellular and synaptic dynamics. In this study we focus on the role in learning of one such dynamical mechanism missing from most artificial neural network models, short-term synaptic plasticity (STSP). Biological synapses have dynamics over at least two time scales: a long time scale, which maps well to synaptic changes in artificial neural networks during learning, and the short time scale of STSP, which is typically ignored. Recent studies have shown the utility of such short-term dynamics in a variety of tasks (Masse et al., 2019; Perez-Nieves et al., 2021), and networks trained with such synapses have been shown to better match recorded neuronal activity and animal behavior (Hu et al., 2020). Here, we allow the timescale of STSP in individual neurons to be learned, simultaneously with standard learning of overall synaptic weights. We study learning performance on two predictive tasks, a simple dynamical system and a more complex MNIST pixel sequence. When the number of computational units is similar to the task dimensionality, RNNs with STSP outperform standard RNN and LSTM models. A potential explanation for this improvement is the encoding of activity history in the short-term synaptic dynamics, a biological form of long short-term memory. Beyond a role for synaptic dynamics themselves, we find a reason and a role for their diversity: learned synaptic time constants become heterogeneous across training and contribute to improved prediction performance in feedforward architectures. These results demonstrate how biologically motivated neural dynamics improve performance on the fundamental task of predicting future inputs with limited computational resources, and how learning such predictions drives neural dynamics towards the diversity found in biological brains.

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bioRxiv2021

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

As aberrant network-level functional connectivity underlies a variety of neural disorders, the ability to induce targeted functional reorganization would be a profound development towards therapies for neural disorders. Brain stimulation has been shown to alter large-scale network-wide functional connectivity, but the mapping from stimulation to the modification is unclear. Here, we leverage advances in neural interfaces, interpretable machine learning, and graph theory to arrive at a model which accurately predicts stimulation-induced network-wide functional reorganization. The model jointly considers the stimulation protocol and the cortical network structure, departing from the standard approach which only considers the stimulation protocol. We validate our approach in the primary sensorimotor cortex of non-human primates using paired optogenetic stimulation through a large-scale optogenetic interface. We observe that the stimulation protocol only predicts a small portion of the induced functional connectivity changes while the network structure predicts much more, indicating that the underlying network is the primary mediator of the response to stimulation. We extract the relationships linking the stimulation and network characteristics to the functional connectivity changes and observe that the mappings diverge over frequency bands and successive stimulations. Finally, we uncover shared processes governing real-time and longer-term effects of stimulation. Our framework represents a paradigm shift for targeted neural stimulation and can be used to interrogate, improve, and develop stimulation-based interventions for neural disorders. TeaserBrain stimulation rewires the brain, but the pre-existing network structure of the brain controls the rewiring.

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bioRxiv2021

Cortical representation variability aligns with in-class variances and can help one-shot learning

Learning invariance across a set of transformations is an important step in mapping high-dimensional inputs to a limited number of classes. After understanding the set of\ invariances, can a new class be learned from one element? We propose a representation which can facilitate such learning: if the variability in representing individual elements across trials aligns with the variability among different elements in a class, then class boundaries learned from the variable representations of one element should be representative of the entire class. In this study, we test whether such a representation occurs in mouse visual systems. We use Neuropixels probes recording single unit activity in mice observing 200 repeats of natural movies taken from a set of 9 continuous clips. We observe that the trial-by-trial variability in the representation of individual frames is well aligned to the variability in representation of multiple frames from the same clip, but not well aligned to the variability among frames from different clips. Thus, the variable representations of images in the mouse cortex can be efficiently used to classify images into their clips. We compare these representations to those in artificial neural networks. We find that, when introducing noise in networks trained for classification (both feed-forward and recurrent networks), the variability in the representation of elements aligns with the in-class variance. The networks which best reproduce the in-vivo observed directions of variability were those trained on a hierarchical classification task. Taken together, these results point to a solution which the cortex can use for one-shot learning of a class: by using noise as a mechanism for generalization. This is a potential computational explanation for the high level of noise observed in the cortex.

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bioRxiv2020

A scale-dependent measure of system dimensionality

A fundamental problem in science is uncovering the effective number of dynamical degrees of freedom in a complex system, a quantity that depends on the spatio-temporal scale at which the system is observed. Here, we propose a scale-dependent generalization of a classic enumeration of latent variables, the Participation Ratio. We show how this measure relates to conventional quantities such as the Correlation dimension and Principal Component Analysis, and demonstrate its properties in dynamical systems such as the Lorentz attractor. We apply the method to neural population recordings in multiple brain areas and brain states, and demonstrate fundamental differences in the effective dimensionality of neural activity in behaviorally engaged states versus spontaneous activity. Our method applies broadly to multi-variate data across fields of science.

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bioRxiv2020

A solution to temporal credit assignment using cell-type-specific modulatory signals

Animals learn and form memories by jointly adjusting the efficacy of their synapses. How they efficiently solve the underlying temporal credit assignment problem remains elusive. Here, we re-analyze the mathematical basis of gradient descent learning in recurrent spiking neural networks (RSNNs) in light of the recent single-cell transcriptomic evidence for cell-type-specific local neuropeptide signaling in the cortex. Our normative theory posits an important role for the notion of neuronal cell types and local diffusive communication by enabling biologically plausible and efficient weight update. While obeying fundamental biological constraints, including separating excitatory vs inhibitory cell types and observing connection sparsity, we trained RSNNs for temporal credit assignment tasks spanning seconds and observed that the inclusion of local modulatory signaling improved learning efficiency. Our learning rule puts forth a novel form of interaction between modulatory signals and synaptic transmission. Moreover, it suggests a computationally efficient learning method for bio-inspired artificial intelligence.

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bioRxiv2020

Strong coupling and local control of dimensionality across brain areas

The brain contains an astronomical number of neurons, but it is their collective activity that underlies brain function. The number of degrees of freedom that this collective activity explores - its dimensionality - is therefore a fundamental signature of neural dynamics and computation (1-7). However, it is not known what controls this dimensionality in the biological brain - and in particular whether and how recurrent synaptic networks play a role (8-10). Through analysis of high-density Neuropixels recordings (11), we argue that areas across the mouse cortex operate in a sensitive regime that gives these synaptic networks a very strong role in controlling dimensionality. We show that this control is expressed across time, as cortical activity transitions among states with different dimensionalities. Moreover, we show that the control is mediated through highly tractable features of synaptic networks. We then analyze these key features via a massive synaptic physiology dataset (12). Quantifying these features in terms of cell-type specific network motifs, we find that the synaptic patterns that impact dimensionality are prevalent in both mouse and human brains. Thus local circuitry scales up systematically to help control the degrees of freedom that brain networks may explore and exploit.

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bioRxiv2020

A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connec-tome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.

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bioRxiv2020

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatio-temporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units. Author SummaryThe brain processes information at all times and much of that information is context-dependent. The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1).We find that relatively few "switching units" -- akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population -- is sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration, and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures.

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bioRxiv2020

Autoencoder networks extract latent variables and encode these variables in their connectomes

Spectacular advances in imaging and data processing techniques are revealing a wealth of information about brain connectomes. This raises an exciting scientific opportunity: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that - even with well constrained neural dynamics - there are in principle many different connectomes that could support a given computation. Here, we define a tractable setting in which the problem of inferring circuit function from circuit connectivity can be analyzed in detail: the function of input compression and reconstruction, in an autoencoder network with a single hidden layer. Here, in general there is substantial ambiguity in the weights that can produce the same circuit function, because largely arbitrary changes to "input" weights can be undone by applying the inverse modifications to the "output" weights. However, we use mathematical arguments and simulations to show that adding simple, biologically motivated regularization of connectivity resolves this ambiguity in an interesting way: weights are constrained such that the latent variable structure underlying the inputs can be extracted from the weights by using nonlinear dimensionality reduction methods.

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bioRxiv2020

Identification of multiple noise sources improves estimation of neural responses across stimulus conditions

Most models of neural responses are constructed to capture the average response to inputs but poorly reproduce the observed response variability. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system. Here, we present a new modeling framework that better captures observed features of neural response variability across stimulus conditions by incorporating multiple sources of noise. We use this model to fit responses of retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. The model reveals light level-dependent changes that could not be seen with previous models. It shows both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions. This modeling framework is general and can be applied to a variety of systems outside the retina.

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bioRxiv2020

Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging

Wide-field calcium imaging techniques allow recordings of high-resolution neuronal activity across one or more brain regions. However, since the recordings capture light emission generated by the fluorescence of the calcium indicator, the neural activity that drives the calcium changes is masked by the calcium indicator dynamics. Here we develop and evaluate new methods to deconvolve the calcium traces and estimate the underlying neural spiking rate. Our methods take into account both the noise in the recordings and the temporal dynamics of the calcium indicator response. Our first proposal estimates firing rates that are constant over discrete time bins. The size of each time bin depends on the data and is determined dynamically. Our second proposal estimates the rate as a continuous function and is meant for studies that look for slow rate fluctuations rather than abrupt changes. We compare our results with those of two alternative approaches: direct deconvolution using a first differences approach, and the Lucy-Richardson image recovery method, adapted to recover temporal dynamics. We show that our methods outperform competitors on synthetic data as well as on wide-field calcium recordings in which the spikes were recorded in parallel using multi-channel silicon probe.

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