Biology subjects

Ponce, C. R.

Publications and source records attributed to Ponce, C. R..

3 recordsLinked to original sources

bioRxiv2024

Brain Feature Maps Reveal Progressive Animal-Feature Representations in the Ventral Stream.

What are the fundamental units of representation in the primate visual brain? While objects have become an intuitive framework for studying neurons in many parts of cortex, it is possible that neurons follow a more expressive organizational principle, such as encoding generic features present across textures, places, and objects. In this study, we used multi-electrode arrays to record from neurons in early (V1/V2), middle (V4), and late (posterior inferotemporal cortex (PIT)) areas across the visual hierarchy, estimating the local operation of each neuron over the entire visual scene. These estimates, called "heatmaps," approximate the weight sharing operation of convolutional neural networks. We found that while populations of neurons across V1, V4, and PIT responded over the full scene, they focused on salient sub-regions within object outlines. The best captured object feature class belonged to animals, not general objects, as a trend that increased over the visual hierarchy. These results show that the monkey ventral stream is partially organized to encode local animal features over objects, even as early as primary visual cortex.

neuroscience↗

bioRxiv2024

Neural Dynamics of Object Manifold Alignment in the Ventral Stream

Visual neurons respond across a vast landscape of images, comprising objects, textures, and places. Natural images can be parameterized using deep generative networks, raising the question of whether latent factors learned by some networks control images in ways that better align with visual neurons. We studied neurons in areas V1, V4 and posterior IT, optimizing images using a closed-loop evolutionary algorithm. We used two generative image models: (1) DeePSim, which parameterizes local image patterns, and (2) BigGAN which parameterizes object identity and nuisance variables. We found that neurons could guide image optimization on both pattern- and object-based image manifolds across areas; V1 aligned best with the DeePSim image space, whereas PIT aligned well with both DeePSim and BigGAN spaces. While initially PIT neurons responded well to the textural manifold, their responses to objects also emerged over time, suggesting that object-like responses required further processing. We identified similar local features common to both textural and object images, but not optimal global configuration. We conclude that visual cortex neurons are aligned to a representational space not yet captured by current artificial model of the visual system.

neuroscience↗

bioRxiv2019

Evolving super stimuli for real neurons using deep generative networks

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.\n\nHighlightsO_LIA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.\nC_LIO_LIThe evolved images activated neurons more strongly than did thousands of natural images.\nC_LIO_LIDistance in image space from the evolved images predicted responses of neurons to novel images.\nC_LI

neuroscience↗