Biology subjects

Bashivan, P.

Publications and source records attributed to Bashivan, P..

11 recordsLinked to original sources

bioRxiv2025

Spatially and non-spatially tuned hippocampal neurons are linear perceptual and nonlinear memory encoders

The hippocampus has long been regarded as a neural map of physical space, with its neurons categorized as spatially or non-spatially tuned according to their response selectivity. However, growing evidence suggests that this dichotomy oversimplifies the complex roles hippocampal neurons play in integrating spatial and non-spatial information. Through computational modeling and in-vivo electrophysiology in macaques, we show that neurons classified as spatially tuned primarily encode linear combinations of immediate behaviorally relevant factors, while those labeled as non-spatially tuned rely on nonlinear mechanisms to integrate temporally distant experiences. Furthermore, our findings reveal a temporal gradient in the primate CA3 region, where spatial selectivity diminishes as neurons encode increasingly distant past events. Finally, using artificial neural networks, we demonstrate that nonlinear recurrent connections are crucial for capturing the response dynamics of non-spatially tuned neurons, particularly those encoding memory-related information. These findings challenge the traditional dichotomy of spatial versus non-spatial representations and instead suggest a continuum of linear and nonlinear computations that underpin hippocampal function. This framework provides new insights into how the hippocampus bridges perception and memory, informing on its role in episodic memory, spatial navigation, and associative learning.

neuroscience↗

bioRxiv2025

Emergent brain-like representations in a goal-directed neural network model of visual search

Animals actively engage with their environment to gather information, continuously shaping both their sensory input and behavior. Understanding this closed loop between perception and action remains a central challenge in neuroscience. A key example is active vision, where observers decide where to look next, selectively sampling from their visual space to guide ongoing perception and action. However, despite major advances in linking neural activity with behavior and computational modeling of vision under passive viewing conditions, the interactive aspects of natural vision remain underexplored. Visual search, the act of locating a target among distractors, exemplifies this dynamic sampling process and has long served as a core paradigm for studying visual attention. While its behavioral and neural signatures have been characterized in humans and non-human primates, a unifying model that links these neural phenomena to behavior during visual search has been lacking. Here, we present a biologically aligned neural network model trained to perform visual search directly from natural scenes by generating sequences of saccades to locate a target. The model generalizes to novel objects and scenes, produces human-like scanpaths, and recapitulates classic behavioral biases in human visual search. Strikingly, units in the model exhibit neural response properties characteristic of the fronto-parietal network, including a stable cue template in working memory, a retinocentric cue-similarity map, and prospective fixation signals. Beyond reproducing known behavioral and neural phenomena, the model reveals a representational geometry that supports cue-driven prioritization, spatial memory, and planning of future fixations. These results establish a computational framework for studying visual search as an emergent property of goal-directed perception, offering concrete predictions for neurophysiological and behavioral testing, and paving the way toward a unified account of active vision.

neuroscience↗

bioRxiv2024

Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Accurate perception of objects within the environment independent of context is essential for an organisms survival. While neurons that respond in an invariant manner to identity-preserving transformations of objects are thought to provide a neural correlate of context-independent perception, how these emerge in the brain remains poorly understood. Here we demonstrate that burst firing in neural populations can give rise to an invariant representation of highly heterogeneous natural communication stimuli. Multi-unit recordings from central sensory neural populations showed that considering burst spike trains led to invariant representations at the population but not the single neuron level. Computational modeling further revealed that optimal invariance is achieved for levels of burst firing seen experimentally. Taken together, our results demonstrate a novel function for burst firing towards establishing invariant representations of sensory input in neural populations.

neuroscience↗

bioRxiv2024

Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks

Across the primate cortex, neurons that perform similar functions tend to be spatially grouped together. This biological principle extends to many other species as well, reflecting a common way of organizing sensory processing across diverse forms of life. In the visual cortex, this biological principle manifests itself as a modular organization of neuronal clusters, each tuned to a specific visual property. The tendency toward short connections is widely believed to explain the existence of such an organization in the brains of many animals. However, the neural mechanisms underlying this phenomenon remain unclear. Here, we use artificial deep neural network models to demonstrate that a topographical organization akin to that in the primary, intermediate, and high-level human visual cortex emerges when units in these models are locally laterally connected and their weight parameters are tuned by top-down credit assignment. The emergence of modular organization without explicit topography-inducing learning rules or objective functions challenges their necessity and suggests that local lateral connectivity alone may suffice for the formation of topographic organization across the cortex. Furthermore, the incorporation of lateral connections in deep convolutional networks enhances their robustness to subtle alterations in visual inputs, such as those designed to deceive the model (i.e. adversarial examples), indicating an additional role for these connections in learning robust representations.

neuroscience↗

bioRxiv2023

The feature landscape of visual cortex

Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visual cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that areas role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neurons visual feature preference. First, we discovered that the mouses visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given areas collection of preferred stimuli ( own-stimuli) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas ( other-stimuli). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.

neuroscience↗

bioRxiv2023

How well do models of visual cortex generalize to out of distribution samples?

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Lin-ear combinations of DNN unit activities are widely used to build predictive models of neu-ronal activity in the visual cortex. Nevertheless, prediction performance in these models is often investigated on stimulus sets consisting of everyday objects under naturalistic set-tings. Recent work has revealed a generalization gap in how predicting neuronal responses to synthetically generated out-of-distribution (OOD) stimuli. Here, we investigated how the recent progress in improving DNNs object recognition generalization, as well as various DNN design choices such as architecture, learning algorithm, and datasets have impacted the generalization gap in neural predictivity. We came to a surprising conclusion that the performance on none of the common computer vision OOD object recognition benchmarks is predictive of OOD neural predictivity performance. Furthermore, we found that adver-sarially robust models often yield substantially higher generalization in neural predictivity, although the degree of robustness itself was not predictive of neural predictivity score. These results suggest that improving object recognition behavior on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex.

neuroscience↗

bioRxiv2020

Auditory cortex supports verbal working memory capacity

Working memory (WM) is a fundamental construct of human cognition. The neural basis of auditory WM is thought to reflect a distributed brain network consisting of canonical memory and central executive brain regions including frontal lobe, prefrontal areas, and hippocampus. Yet, the role of auditory (sensory) cortex in supporting active memory representations remains controversial. Here, we recorded neuroelectric activity via EEG as listeners actively performed an auditory version of the Sternberg memory task. Memory load was taxed by parametrically manipulating the number of auditory tokens (letter sounds) held in memory. Source analysis of scalp potentials showed that sustained neural activity maintained in auditory cortex (AC) prior to memory retrieval closely scaled with behavioral performance. Brain-behavior correlations revealed lateralized modulations in left (but not right) AC predicted individual differences in auditory WM capacity. Our findings confirm a prominent role of auditory cortex, traditionally viewed as a sensory-perceptual processor, in actively maintaining memory traces and dictating individual differences in behavioral WM limits.

neuroscience↗

bioRxiv2019

Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge

Image features computed by specific convolutional artificial neural networks (ANNs) can be used to make state-of-the-art predictions of primate ventral stream responses to visual stimuli.\n\nHowever, in addition to selecting the specific ANN and layer that is used, the modeler makes other choices in preprocessing the stimulus image and generating brain predictions from ANN features. The effect of these choices on brain predictivity is currently underexplored.\n\nHere, we directly evaluated many of these choices by performing a grid search over network architectures, layers, image preprocessing strategies, feature pooling mechanisms, and the use of dimensionality reduction. Our goal was to identify model configurations that produce responses to visual stimuli that are most similar to the human neural representations, as measured by human fMRI and MEG responses. In total, we evaluated more than 140,338 model configurations. We found that specific configurations of CORnet-S best predicted fMRI responses in early visual cortex, and CORnet-R and SqueezeNet models best predicted fMRI responses in inferior temporal cortex. We found specific configurations of VGG-16 and CORnet-S models that best predicted the MEG responses.\n\nWe also observed that downsizing input images to ~50-75% of the input tensor size lead to better performing models compared to no downsizing (the default choice in most brain models for vision). Taken together, we present evidence that brain predictivity is sensitive not only to which ANN architecture and layer is used, but choices in image preprocessing and feature postprocessing, and these choices should be further explored.

neuroscience↗

bioRxiv2018

Neural Population Control via Deep Image Synthesis

Particular deep artificial neural networks (ANNs) are todays most accurate models of the primate brains ventral visual stream. Here we report that, using a targeted ANN-driven image synthesis method, new luminous power patterns (i.e. images) can be applied to the primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. More importantly, this method, while not yet perfect, already achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in todays ANN models might be used to non-invasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

neuroscience↗

bioRxiv2018

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brains mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at [≥] 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brains network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

neuroscience↗

bioRxiv2018

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

Primates--including humans--can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks--such as those obtained here--could serve as direct guides for discovering such models.\n\nSIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

neuroscience↗