Object recognition

…is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from

The primate ventral visual system is critical for object recognition, but how its neural representations change during adult categorization learning remains debated. Some studies suggest that neural responses of visual areas change with training, e.g., during perceptual learning. Others propose that behavioral changes are readily explained by adjustments in the readout of information by downstream frontoparietal regions, with sensory regions remaining largely unchanged. Here, we investigated changes in neural representations in macaque visual areas V4 and IT during the learning of novel catego

Primates visually identify objects primarily by shape and also by texture. Models of primate vision based on convolutional neural networks (CNNs) achieve high object recognition performance but, unlike primates, emphasize texture over shape. Overcoming this texture bias and making models more brain-like requires data sets that distinguish how shape and texture cues interact in mid-level visual processing. To this end, we created sets of images that parametrically varied between objects and textures, and recorded responses to these images from populations of single units in macaque mid-level vi

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information

In visual scenes, the presence of corner elements greatly simplifies object recognition [1]. It has been suggested that cortical area V4 is responsible for the processing of these elements, since neurons exhibit selectivity for different curvatures [2]. However, V4 response properties and their contribution to contour integration or shape completion are still far from being understood. We investigated visually-evoked responses of macaque V4 neurons to sparse random stimuli, composed of angled segments pointing in two directions like clock hands (Fig 1a). Recordings were obtained using a 96-cha

The effects of spaceflight are induced by three concomitant factors: poor social interactions in a reduced living space, microgravity, and the radiation bath. To understand how these parameters can influence behaviors, animal studies are being carried out, modelling each of these parameters independently or not of the others. These studies allow to challenge different strategies of countermeasures to prevent spaceflight multisystemic deconditioning. In the NEBULA project, we developed an original suspension system that allows interaction between 2 conspecifics, to tackle the effects of microgr

Hierarchical processing has been shown to underlie complex functions such as invariant object coding. The perirhinal cortex (PER) is a higher-order region that sits at the apex of such processing streams and is involved in object recognition. Although it receives tactile information from barrel cortex (wS1) and secondary somatosensory cortex (S2), it is unclear how PER neurons respond to such stimuli. To address this, we performed population recordings of the barrel and perirhinal cortices of head-fixed mice while an object deflected their whiskers. Both ipsi- and contralateral stimulations we

Ethanol (EtOH) exposure during pregnancy can induce a range of physical, learning, and behavioral disorders in the newborn clinically referred as Fetal Alcohol Spectrum Disorder (FASD). Unfortunately, FASD is poorly understood, and the underlying mechanisms are not fully known. Here, we studied the role of AMPA receptors (AMPARs) in in vitro and in vivo models of prenatal alcohol exposure (PAE). For the in vitro study, immature rat organotypic hippocampal slices were exposed to EtOH (150 mM) for 7 days followed by 24 hours of withdrawal. For the in vivo study, female C57/Bl6 mice were exposed

We previously reported that pregestational chronic unpredictable stress (CUS) and prenatal treatment with the antidepressant mirtazapine alter offspring anxiety and spatial memory (Viñas-Noguera et al. PLoS One 17: e0255546, 2022). The present study was designed to investigate the potential involvement of dopamine transmission in these behavioral changes. Maternal CUS and mirtazapine were administered as previously described. Adult offspring were anesthetized with chloral hydrate and electrodes were placed in the ventral tegmental area. Dopamine neurons were detected and recorded. We found no

Aging produces in brain a progressive oxidative process and neuroinflammation that deteriorates cognition. These brain dysfunctions provoke high economic and social costs to developed societies. For this reason, the search for treatments to prevent cognitive deficits associated with aging is a priority. Previous results suggest that STX64, a steroid sulfatase (STS) inhibitor, improves cognition in neurodegenerative diseases. In this work, we have designed cellular (immunohistochemistry), molecular (qPCRs with primers of inflammatory markers and cellular populations) and behaviour (mainly, obje

…into putative processes such as object recognition, working memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data do not support many of the predictions of these classic subdivisions. Instead, there is divergence and broad distribution of functions that should be unifie

…interacting with behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids hierarchical computations of object recognition is unclear. Two key mechanisms have been proposed: First is an improvement in the efficacy of information transfer, supported by an attentional increase in inter-areal correlations along the hierarchy. A second proposal is an improvement in information capacit

…is critical in supporting an array of object recognition behavior. Previous research, however, has been agnostic to the relevance of specific cell types, inhibitory vs. excitatory, in the formation of “behaviorally sufficient” IT population codes that can accurately predict primate object confusion patterns. Therefore, here, we first compared the strength of behavioral predictions of neural decoding (“readout”) models constructed from specific (put

…assess the spatial-frequency tuning of object recognition by measuring its noise tolerance at various frequencies. 16 observers and 4 neural networks performed 16-way categorization of 1050 ImageNet images perturbed by band-limited Gaussian noise of five strengths centered at seven spatial frequencies. We find that the noise sensitivity of object recognition is an inverted, U-shaped function of spatial frequency. Human observer perfor

Biological visual systems learn complex representations of the world that support a wide range of cognitive behaviors without using a large number of labelled examples. The efficient coding hypothesis suggests that this is accomplished by adapting the sensory representation to the statistics of the input signal, i.e. in a way that facilitates redundancy reduction. Visual signals have several clear sources of redundancy. They evolve slowly in time, since temporally adjacent inputs typically correspond to different views of the same scene, which in turn are usually more similar than views of dis

…associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide r

…create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an object reconstruction and used it as top-down attentional feedback to route the most relevant spatial and feature information to feed-forward object recognition processes. We tested this model using the challenging out-of-distribution digit recognition dataset, MNIST-C, where 15 di

…stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a gap in generalization ability of these models in predicting neuronal responses to out-of-distribution (OOD) samples (i.e. samples that are not regarded as natural photos) . Here, we investigated how the recent progress in improving DNNs’ object re

The responses of neurons in primary visual cortex depend strongly on the spatial context in which stimuli are embedded. This context-dependence has been theorized to reflect efficient and/or predictive coding of natural scenes. Critical tests of these theories in a generic form for natural images are currently lacking , because it is unclear how predictions, and measures of predictability, should be operationalized for natural scenes. Further, it is unclear whether there is just one type or level of predictability given the hierarchical processing in the primate visual system. Here, we traine

Object recognition relies on the complex visual representations in cortical areas at the top of the ventral stream hierarchy. While these are thought to be derived from low-level stages of visual processing, this has not been shown, yet. Here, I describe the results of two projects exploring the contributions of primary visual cortex (V1) processing to object recognition using artificial neural networks (ANNs). First, we developed hundreds of ANN-based V1 models and evaluated how their single neurons approximate those in the macaque V1. We found that, for some models, single neurons in interme

…our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.

A recent approach to understanding the mammalian visual system is to show correspondence between the sequential stages of processing in the ventral stream with layers in a deep convolutional neural network (DCNN), providing evidence that visual information is processed hierarchically, with successive stages containing ever higher-level information. However, correspondence is usually defined as shared variance between brain region and model layer. We propose that task-relevant variance is a stricter test: If a DCNN layer corresponds to a brain region, then substituting the model’s activity with

…the brain circuits that support core object recognition behavior (primates’ ability to categorize objects within hundreds of milliseconds) in non-human primates. On the one hand, my work empirically examines how well computational models of the primate ventral visual pathways embed know

…role in encoding and recognizing objects. The rationale for this dogma is compelling: object recognition is excellent in grayscale images (consider black-and-white movies, where faces, places, objects, and story are readily apparent). In my talk I will discuss experiments in which we used color as a tool to uncover an organizational plan in inferior temporal cortex (parallel, multistage processing for places, faces, colors,