Biological vision has evolved to make efficient use of the limited information processing capability and tight energy budget of the brain by preferentially processing the most salient features of visual scenes. In contrast, modern deep vision models rely on expansive, high-dimensional representations. This may offer potential recognition gains but increases computing costs. As a consequence, the computer vision community has been exploring sparsity-enforcing techniques such as activation dropout and weight pruning. Beyond ameliorating the burden of computation, sparsity techniques such as random dropout have been shown to regularize model training, thus allowing for better generalization. Here, we pursue both dropout and weight pruning in tandem and adopt a biologically plausible saliency-informed dropout technique as an explainable alternative to the unstructured standard random dropout approach. Our approach involves a hierarchical “retinotopic" gating of convolutional feature maps, which promotes efficient deletion of “redundant" weights by iterative magnitude pruning. We explore the effectiveness of saliency-informed dropout based on different approaches to dropping based on the saliency map, dropout through all layers or only early layers, and by additionally applying dropout at inference. We compare throughout with standard random dropout. We present empirical results on regimes where such structured dynamic and static (weight) sparsities interact optimally to prune a ResNet model on the Imagenette dataset.
Shira Goldhaber-Gordon, A. Akwaboah, Aaron L. Sampson et al.· International Conference on...· 1 citation
In 1949, Donald Hebb proposed that neuronal assemblies with temporally specific patterns of activity form the building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging due to a lack of large-scale structure-activity maps. Here, we combine in vivo optical physiology with postmortem electron microscopy (EM) in the same tissue volume. Using higher-order correlations in fluorescence traces, we extract neuronal assemblies. Physiologically, we show that these assemblies respond more reliably to repeated natural movies than size-matched control ensembles and decode such stimuli more accurately. Structurally, we find that over a quarter of the pyramidal neurons do not participate in any assembly and are significantly less integrated into the connectome than those that do. We do not observe a marked increase in the strength of monosynaptic excitatory connections between neurons sharing assembly assignment, but instead find significantly stronger indirect inhibitory connections targeting cells in other assemblies. These results show that assemblies can serve as functional units of perception and suggest they may be structurally delineated by mutual inhibition.
J. Wagner-Carena, Sai Kate, Trevor Riordan et al.· Cell Reports· 0 citations
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 the cortex, thalamus, 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 recruited an expanded cortical population and modulated late cortical (but not thalamic) responses. Population decoding and optogenetics identified a critical time window for change detection and were consistent with mice using an adaptation-based rather than image-comparison strategy. This comprehensive resource provides a valuable substrate for understanding sensorimotor computations in neural networks.
Corbett Bennett, Samuel D. Gale, Greggory Heller et al.· Cell· 0 citations
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