Biology subjects

Adeli, H.

Publications and source records attributed to Adeli, H..

7 recordsLinked to original sources

bioRxiv2026

Lateral recurrence dynamically refines visual representations for task performance

Recurrent connections are pervasive in visual cortex, and visual representations evolve as recurrent processing unfolds. Yet the mechanism underlying this temporal transformation remains poorly understood. To address this question, we trained recurrent convolutional networks on face and object recognition and tested them on a parametric stimulus set in which category, identity, and pose were systematically varied. This design isolates how lateral recurrence reshapes neural-population representational geometry and single-unit tuning across recurrent timesteps. Within the trained domain, recurrence increased the separability between identities in deeper layers, while leaving the non-trained domain representations largely unchanged. Gating lateral weights abolished the across-timestep gain in trained-domain identity decoding, demonstrating that lateral recurrence drives this refinement. At the single-unit level, recurrence strengthened the correlation between each unit's category preference and its within-domain identity sensitivity, providing a unit-level mechanism for the population-level changes. These results identify lateral recurrence as a dynamic mechanism for refining learned representational structure within a fixed neural population, yielding testable predictions for time-resolved recordings in domain-selective cortex.

neuroscience↗

bioRxiv2025

Multiple patterns of selectivity in superior colliculus control visual search

Visual search is a ubiquitous behavior for many animal species, but efficient search performance requires a relatively complex integration of target guidance and behavioral history signals. In this study, we investigate how the superior colliculus (SC), a midbrain area implicated in the control of overt spatial attention, integrates these two control signals in a multi-target search task. Monkeys made sequences of saccades to stimuli presented in a grid while searching for a "true" (reward eliciting) target amid color-coded targets and distractors. The efficient performance of this task requires integrating a target guidance signal with a history of previously fixated locations in order to guide search to new potential targets that might offer reward. Theories of systems neuroscience suggest that this multi-factor search behavior might be controlled using either mixed selectivity, dynamic selectivity, or pure selectivity. We find evidence for SC neurons exhibiting each of these patterns and conclude that this brain structure participates in circuits representing target similarity and previously fixated locations, in addition to a circuit responsible for integrating these signals using the mechanisms of dynamic and mixed selectivity. We also introduce a novel time-series partial information decomposition analysis that provides a rich and direct view of how a neuron allocates its representational capacity. These findings support an emerging view of the superior colliculus as much more than a bottom-up priority map, but instead an important site for the flexible cognitive control of visually-guided behavior.

neuroscience↗

bioRxiv2024

Evaluating theories of neural information integration during visual search

The brain routes and integrates information from many sources during behavior. A number of models explain this phenomenon within the framework of mixed selectivity theory, yet it is difficult to compare their predictions to understand how neurons and circuits integrate information. In this work, we apply time-series partial information decomposition [PID] to compare models of integration on a dataset of superior colliculus [SC] recordings collected during a multi-target visual search task. On this task, SC must integrate target guidance, bottom-up salience, and previous fixation signals to drive attention. We find evidence that SC neurons integrate these factors in diverse ways, including decision-variable selectivity to expected value, functional specialization to previous fixation, and code-switching (to incorporate new visual input).

neuroscience↗

bioRxiv2023

Predicting brain activity using Transformers

The Algonauts challenge [Gifford et al., 2023] called on the community to provide novel solutions for predicting brain activity of humans viewing natural scenes. This report provides an overview and technical details of our submitted solution. We use a general transformer encoder-decoder model to map images to fMRI responses. The encoder model is a vision transformer trained using self-supervised methods (DINOv2). The decoder uses queries corresponding to different brain regions of interests (ROI) in different hemispheres to gather relevant information from the encoder output for predicting neural activity in each ROI. The output tokens from the decoder are then linearly mapped to the fMRI activity. The predictive success (challenge score: 63.5229, rank 2) suggests that features from self-supervised transformers may deserve consideration as models of human visual brain representations and shows the effectiveness of transformer mechanisms (self and cross-attention) to learn the mapping from features to brain responses. Code is available in this github repository.

neuroscience↗

bioRxiv2023

Generating object-based attention through reconstruction

Humans are extremely robust in our ability to perceive and recognize objects--we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward pass of processing through the ventral stream and less on the top-down feedback that likely underlies robust object perception and recognition. Aligned with the generative approach, we propose that the visual system actively facilitates recognition by reconstructing the object hypothesized to be in the image. Top-down attention then uses this reconstruction as a template to bias feedforward processing to align with the most plausible object hypothesis. Building on auto-encoder neural networks, our model makes detailed hypotheses about the appearance and location of the candidate objects in the image by reconstructing a complete object representation from potentially incomplete visual input due to noise and occlusion. The model then leverages the best object reconstruction, measured by reconstruction error, to direct the bottom-up processing of selectively routing low-level features, a top-down biasing that captures a core function of attention. We evaluated our model using the MNIST-C (handwritten digits under corruptions) and ImageNet-C (real-world objects under corruptions) datasets. Not only did our model achieve superior performance on these challenging tasks designed to approximate real-world noise and occlusion viewing conditions, but also better accounted for human behavioral reaction times and error patterns than a standard feedforward Convolutional Neural Network. Our model suggests that a complete understanding of object perception and recognition requires integrating top-down and attention feedback, which we propose is an object reconstruction. Author SummaryHumans can dream and imagine things, and this means that the human brain can generate perceptions of things that are not there. We propose that humans evolved this generation capability, not solely to have more vivid dreams, but to help us better understand the world, especially when what we see is unclear or missing some details (due to occlusion, changing perspective, etc.). Through a combination of computational modeling and behavioral experiments, we demonstrate how the process of generating objects--actively reconstructing the most plausible object representation from noisy visual input--guides attention towards specific features or locations within an image (known as functions of top-down attention), thereby enhancing the systems robustness to various types of noise and corruption. We found that this generative attention mechanism could explain, not only the time that it took people to recognize challenging objects, but also the types of recognition errors made by people (seeing an object as one thing when it was really another). These findings contribute to a deeper understanding of the computational mechanisms of attention in the brain and their potential connection to the generative processes that facilitate robust object recognition.

neuroscience↗

bioRxiv2022

A brain-inspired object-based attention network for multi-object recognition and visual reasoning

The visual system uses sequences of selective glimpses to objects to support goal-directed behavior, but how is this attention control learned? Here we present an encoder-decoder model inspired by the interacting bottom-up and top-down visual pathways making up the recognitionattention system in the brain. At every iteration, a new glimpse is taken from the image and is processed through the "what" encoder, a hierarchy of feedforward, recurrent, and capsule layers, to obtain an object-centric (object-file) representation. This representation feeds to the "where" decoder, where the evolving recurrent representation provides top-down attentional modulation to plan subsequent glimpses and impact routing in the encoder. We demonstrate how the attention mechanism significantly improves the accuracy of classifying highly overlapping digits. In a visual reasoning task requiring comparison of two objects, our model achieves near-perfect accuracy and significantly outperforms larger models in generalizing to unseen stimuli. Our work demonstrates the benefits of object-based attention mechanisms taking sequential glimpses of objects.

animal behavior and cognition↗

bioRxiv2021

Readers move their eyes mindlessly using midbrain visuo-motor principles

Saccadic eye movements rapidly shift our gaze over 100,000 times daily, enabling countless tasks ranging from driving to reading. Long regarded as a window to the mind1 and human information processing2, they are thought to be cortically/cognitively controlled movements aimed at objects/words of interest3-10. Saccades however involve a complex cerebral network11-13 wherein the contribution of phylogenetically older sensory-motor pathways14-15 remains unclear. Here we show using a neuro-computational approach16 that mindless visuo-motor computations, akin to reflexive orienting responses17 in neonates18-19 and vertebrates with little neocortex15,20, guide humans eye movements in a quintessentially cognitive task, reading. These computations occur in the superior colliculus, an ancestral midbrain structure15, that integrates retinal and (sub)cortical afferent signals13 over retinotopically organized, and size-invariant, neuronal populations21. Simply considering retinal and primary-visual-cortex afferents, which convey the distribution of luminance contrast over sentences (visual-saliency map22), we find that collicular population-averaging principles capture readers prototypical word-based oculomotor behavior2, leaving essentially rereading behavior unexplained. These principles reveal that inter-word spacing is unnecessary23-24, explaining metadata across languages and writing systems using only print size as a predictor25-26. Our findings demonstrate that saccades, rather than being a window into cognitive/linguistic processes, primarily reflect rudimentary visuo-motor mechanisms in the midbrain that survived brain-evolution pressure27.

neuroscience↗