Contrastive learning explains the emergence and function of visual category-selective regions

Science Advances, Journal Year: 2024, Volume and Issue: 10(39)

Published: Sept. 25, 2024

Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework—contrastive coding—based on series analyses relating within biological artificial neural networks. We discover that, models trained with contrastive self-supervised objectives over rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, words. Further, lesions these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles information processing. Finally, pre-identified can predict responses all corresponding face-, scene-, body-, word-selective regions cortex, under highly constrained sparse positive encoding procedure. The success this single indicates that brain-like specialization emerge without category-specific learning pressures, as system learns untangle content. Contrastive coding, therefore, provides unifying account object emergence representation brain.

Language: Английский

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Artificial intelligence for life sciences: A comprehensive guide and future trends

The Innovation Life, Journal Year: 2024, Volume and Issue: unknown, P. 100105 - 100105

Published: Jan. 1, 2024

<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>

Language: Английский

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Privileged representational axes in biological and artificial neural networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 20, 2024

Abstract How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings (or ‘axes’) neural responses. But might these representational axes matter, with some privileged systematically over others? To find out, we developed methods test for alignment tuning across brains deep convolutional networks (DCNNs). Across both vision audition, DCNNs consistently favored certain representing natural world. Moreover, trained on inputs were aligned those in perceptual cortices, axis-sensitive model-brain similarity metrics better differentiated competing models biological sensory systems. We further show coding schemes privilege can reduce downstream wiring costs improve generalization. These results motivate a new framework understanding artificial its computational benefits.

Language: Английский

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Animal models of the human brain: Successes, limitations, and alternatives

Current Opinion in Neurobiology, Journal Year: 2025, Volume and Issue: 90, P. 102969 - 102969

Published: Feb. 1, 2025

Language: Английский

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Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

Imaging Neuroscience, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 1, 2025

The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in high-level vision. One intriguing aspect this region its functional lateralization, with neural responses to words being stronger left hemisphere, and faces right hemisphere; such patterns can be summarized a signed laterality index (LI), positive for leftward laterality. Converging evidence has suggested that word emerges couple efficiently left-lateralized frontotemporal language regions, but more mixed regarding sources lateralization face perception. Here, we use individual differences as tool test three theories VTC organization arising from (1) local competition between driven by long-range coupling processes, (2) other categories, (3) areas exhibiting social processing. First, an in-house MRI experiment, did not obtain negative correlation LIs selectivity relative object responses, find when using fixation baseline, challenging ideas driving rightward lateralization. We next examined broader LI interactions large-scale Human Connectome Project (HCP) dataset. Face were significantly anti-correlated, while body positively correlated, consistent idea generic representational cooperation may shape Last, assessed role development Within our substantial was evident text several nodes distributed text-processing circuit. In HCP data, both negatively correlated processing different subregions posterior lobe (PSL STSp, respectively). summary, no face-word VTC; instead, multiple lateralities within VTC, including Moreover, also influenced lobe, where become lateralized due language.

Language: Английский

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Partial information transfer from peripheral visual streams to foveal visual streams may be mediated through local primary visual circuits

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121147 - 121147

Published: March 1, 2025

Visual object recognition is driven through the what pathway, a hierarchy of visual areas processing features increasing complexity and abstractness. The primary cortex (V1), this pathway's origin, exhibits retinotopic organization: neurons respond to stimuli in specific field regions. A neuron responding central stimulus won't peripheral one, vice versa. However, despite organization, task-relevant feedback about can be decoded unstimulated foveal cortex, disrupting impairs discrimination behavior. information encoded by remains unclear, as prior studies used computer-generated objects ill-suited dissociate different representation types. To address knowledge gap, we investigated nature periphery-to-fovea using real-world stimuli. Participants performed same/different task on peripherally displayed images vehicles faces. Using fMRI multivariate decoding, found that both V1 could decode separated low-level perceptual models (vehicles) but not those semantic (faces). This suggests primarily carries information. In contrast, higher resolved semantically distinct images. functional connectivity analysis revealed connections later-stage areas. These findings indicate while early late may contribute transfer from streams, higher-to-lower area involve loss.

Language: Английский

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Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 30, 2024

Biological visual systems have evolved to process natural scenes. A full understanding of cortical functions requires a comprehensive characterization how neuronal populations in each area encode Here, we utilized widefield calcium imaging record V4 response tens thousands images male macaques. Using this large dataset, developed deep-learning digital twin that allowed us map the image preferences neural population at 100-µm scale. This detailed revealed diverse set functional domains V4, encoding distinct features. We validated these model predictions using additional and single-cell resolution two-photon imaging. Feature attribution analysis lie along continuum from preferring spatially localized shape features dispersed surface These results provide insights into organizing principles govern scene V4. How scenes are represented by specific such as remain not fully understood. The authors produced dataset macaque responses images, used deep learning techniques elucidate encoded topologically organized

Language: Английский

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Conclusions about Neural Network to Brain Alignment are Profoundly Impacted by the Similarity Measure

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 9, 2024

Abstract Deep neural networks are popular models of brain activity, and many studies ask which provide the best fit. To make such comparisons, papers use similarity measures as Linear Predictivity or Representational Similarity Analysis (RSA). It is often assumed that these yield comparable results, making their choice inconsequential, but it? Here we if how measure affects conclusions. We find influences layer-area correspondence well ranking models. explore choices impact prior conclusions about most “brain-like”. Our results suggest widely held regarding relative alignment different network with activity have fragile foundations.

Language: Английский

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Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Abstract The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in computations related to high-level vision. One intriguing aspect this region its asymmetric organization and functional lateralization. Notably, VTC, neural responses words are stronger left hemisphere, whereas faces right hemisphere. Converging evidence has suggested that left-lateralized word emerge couple efficiently with frontotemporal language regions, but more mixed regarding sources right-lateralization for face perception. Here, we use individual differences as a tool adjudicate between three theories VTC arising from: 1) local competition faces, 2) other categories, 3) long-range coupling areas subject their own competition. First, an in-house MRI experiment, demonstrated laterality both substantial reliable within right-handed population young adults. We found no (anti-)correlation selectivity relative object responses, positive correlation when using fixation baseline, challenging ideas faces. next examined broader large-scale Human Connectome Project (HCP) dataset. Face were significantly anti-correlated, while body positively correlated, consistent idea generic representational cooperation may shape Last, assessed role development laterality. Within our was evident text several nodes distributed text-processing circuit. In HCP data, negatively correlated laterality, social perception same areas, effect processing representations, driven by processing. conclude interactions heterogeneous hemispheric specializations visual cortex.

Language: Английский

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Modeling Sensorimotor Processing with Physics-Informed Neural Networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 15, 2024

Proprioception is essential for planning and executing precise movements. Muscle spindles, the key mechanoreceptors proprioception, are principle sensory neurons enabling this process. Emerging evidence suggests spindles act as adaptable processors, modulated by gamma motor to meet task demands. Yet, specifics of modulation remain unknown. Here, we present a novel, physics-informed neural network model that integrates biomechanics dynamics capture spindle function with high fidelity efficiency, while maintaining computational tractability. Through validation across multiple experimental datasets species, our not only outperforms existing approaches but also reveals drivers variability in responses, offering new insights into proprioceptive mechanisms.

Language: Английский

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