Biology subjects

Mastrovito, D.

Publications and source records attributed to Mastrovito, D..

7 recordsLinked to original sources

bioRxiv2025

Map of spiking activity underlying change detection in the mouse visual system

Visual behavior requires coordinated activity across hierarchically organized brain circuits. Understanding this complexity demands datasets that are both large-scale (sampling many areas) and dense (recording many neurons in each area). Here we present a database of spiking activity across the mouse visual system--including thalamus, cortex, and midbrain--while mice perform an image change detection task. Using Neuropixels probes, we record from >75,000 high-quality units in 54 mice, mapping area-, cortical layer-, and cell type-specific coding of sensory and motor information. Modulation by task-engagement increased across the thalamocortical hierarchy but was strongest in the midbrain. Novel images modulated cortical (but not thalamic) responses through delayed recurrent activity. Population decoding and optogenetics identified a critical decision window for change detection and revealed that mice use an adaptation-based rather than image-comparison strategy. This comprehensive resource provides a valuable substrate for understanding sensorimotor computations in neural networks.

neuroscience↗

bioRxiv2024

Communication subspaces align with training in ANNs

Communication subspaces have recently been identified as a promising mechanism for selectively routing information between brain areas. In this study, we explored whether communication sub-spaces develop with training in artificial neural networks (ANNs) and explored differences across connection types. Specifically, we analyzed the subspace angles between activations and weights in ResNet-50 before and after training. We found that activations were more aligned to the weight layers after training, although this effect decreased in deeper layers. We also analyzed the angles between pairs of weight layers. We found that for all branching, direct, and skip connections, weight layer pairs were more geometrically aligned in trained versus untrained models throughout the entire network. These findings indicate that such alignment is essential for the proper functioning of deep networks and highlights the potential to enhance training efficiency through pre-alignment. In biological data, our results motivate further exploration into whether learning induces similar subspace alignment.

neuroscience↗

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.

neuroscience↗

bioRxiv2023

Modeling the cell-type specific mesoscale murine connectome with anterograde tracing experiments

The Allen Mouse Brain Connectivity Atlas (MCA) consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class specific whole brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method which we previously used to fill in spatial gaps to also fill in a gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type and structure specific connectivities. We also show that the wild type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model. AUTHOR SUMMARYLarge-scale studies have described the connections between areas in multiple mammalian models in ever expanding detail. Standard connectivity studies focus on the connection strength between areas. However, when describing functions at a local circuit level, there is an increasing focus on cell types. We have recently described the importance of connection types in the cortico-thalamic system, which allows an unsupervised discovery of its hierarchical organization. In this study we focus on adding a dimension of connection type for a brain-wide mesoscopic connectivity model. Even with our relatively massive dataset, the data in the cell type direction for connectivity is quite sparse, and we had to develop methods to more reliably extrapolate in such directions, and to estimate when such extrapolations are impossible. This allows us to fill in such a connection type specific inter-areal connectivity matrix to the extent our data allows. While analyzing this complex connectivity, we observed that it can be described via a small set of factors. While not complete, this connectivity matrix represents a a categorical and quantitative improvement in mouse mesoscale connectivity models.

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bioRxiv2023

Temporal Dynamics of Activity in Default Mode Network Suggests a Role in Top-down processing

The default mode network (DMN) is a collection of brain regions including midline frontal and parietal structures, medial and lateral temporal lobes, and lateral parietal cortex. Although there is evidence that the network can be subdivided into at least two subcomponents, the network reliably exhibits highly correlated activity both at rest and during task performance. Current understanding regarding the function of the DMN rests on a large body of research indicating that activity in the network decreases during task epochs of experimental paradigms relative to inter-trial intervals. A seeming contradiction arises when the experimental paradigm includes tasks involving autobiographical memory, thinking about ones self, planning for the future, or social cognition. In such cases, the DMNs activity increases and is correlated with attentional networks. Some have therefore concluded that the DMN supports advanced human cognitive abilities such as interoceptive processing and theory of mind. This conclusion may be called into question by evidence of correlated activity in homologous brain regions in other, even non-primate, species. Thus, there are contradictory findings related to the function of the DMN that have been difficult to integrate into a coherent theory regarding its function. Using data from the Human Connectome Project, we explore the temporal dynamics of activity in different regions of the DMN in relation to stimulus presentation. We show that generally the dorsal portion of the network exhibits only a transient initial decrease in activity at the start of trials that increases over trial duration. The ventral component often has more similarity in its time course to that of task-activated areas. We propose that task-associated ramping dynamics in the network are incompatible with a task-negative view of the DMN and propose the dorsal and ventral sub-components of network may rather work together to support bottom-up salience detection and subsequent top-down voluntary action. In this context, we re-interpret the body of anatomical and neurophysiological experimental evidence, arguing that this interpretation can accommodate the seeming contradictions regarding DMN function in the extant literature.

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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.

neuroscience↗

bioRxiv2021

A Biologically Plausible Model for Continual Learning using Synaptic Weight Attractors

The human brain readily learns tasks in sequence without forgetting previous ones. Artificial neural networks (ANNs), on the other hand, need to be modified to achieve similar performance. While effective, many algorithms that accomplish this are based on weight importance methods that do not correspond to biological mechanisms. Here we introduce a simple, biologically plausible, method for enabling effective continual learning in ANNs. We show that it is possible to learn a weight-dependent plasticity function that prevents catastrophic forgetting over multiple tasks. We highlight the effectiveness of our method by evaluating it on a set of MNIST classification tasks. We further find that the use of our method promotes synaptic multi-modality, similar to that seen in biology.

neuroscience↗