A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

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

Published: Oct. 30, 2024

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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Mind Unveiled: Cutting-Edge Neuroscience and Precision Brain Mapping

Asian Journal of Current Research, Journal Year: 2024, Volume and Issue: 9(3), P. 181 - 195

Published: Aug. 10, 2024

Neuroscience, a dynamic field at the forefront of scientific exploration, is unravelling complexities human brain. By merging biology, psychology, physics, and computer science, researchers are gaining profound insights into cognition, behaviour, neurological underpinnings diseases. Brain mapping key component recent advancements. Techniques like fMRI, PET, DTI offer unprecedented views brain structure function. The Human Connectome Project similar initiatives have produced detailed maps connections, revealing how different regions interact to support cognition behaviour. These crucial for identifying disease biomarkers, predicting treatment responses, developing targeted therapies. Molecular biology genetics also driving progress. Researchers uncovering genetic basis disorders, providing clues about susceptibility progression. imaging techniques visualise neurotransmitter systems cellular processes, shedding light on mechanisms. integration neuroscience with modelling AI revolutionising research. algorithms analyse vast datasets, simulate neural networks, even decode signals brain-machine interfaces. This has potential personalised medicine ground-breaking treatments. future holds immense promise. optogenetics single-cell will greater precision in studying circuits. However, we must address ethical considerations around data privacy, cognitive enhancement, brain-altering interventions. Neuroscience not just understanding brain; it's improving lives. striving conquer disorders maximize by pushing boundaries knowledge technology while upholding principles.

Language: Английский

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The perceptual primacy of feeling: Affectless visual machines explain a majority of variance in human visually evoked affect

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(4)

Published: Jan. 23, 2025

Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive in absence of active physiology, deliberative thought, or any form feedback resembles human affective experience offer tools demystify relationship between and feeling, assess how much visually evoked experiences may be a straightforward function representation learning over natural image statistics. In this work, we deploy diverse sample 180 state-of-the-art deep neural network models trained only on canonical computer tasks predict ratings arousal, valence, beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets. Importantly, use features these without additional learning, linearly decoding responses activity same way neuroscientists decode information recordings. Aggregate analysis our survey, demonstrates predictions purely perceptual explain majority explainable variance average alike. Finer-grained within survey (e.g. comparisons shallower deeper layers, randomly initialized, category-supervised, self-supervised models) point rich, preconceptual abstraction (learned diversity visual experience) as key driver predictions. Taken together, results provide further computational evidence an information-processing account affect linked directly efficient statistics, hint locus aesthetic valuation immediately proximate perception.

Language: Английский

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Parallel development of social behavior in biological and artificial fish

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

Published: Dec. 5, 2024

Abstract Our algorithmic understanding of vision has been revolutionized by a reverse engineering paradigm that involves building artificial systems perform the same tasks as biological systems. Here, we extend this to social behavior. We embodied neural networks in fish and raised virtual tanks mimicked rearing conditions fish. When had deep reinforcement learning curiosity-derived rewards, they spontaneously developed fish-like behaviors, including collective behavior preferences (favoring in-group over out-group members). The also naturalistic ocean worlds, showing these models generalize real-world contexts. Thus, animal-like behaviors can develop from generic algorithms (reinforcement intrinsic motivation). study provides foundation for reverse-engineering development using image-computable intelligence, bridging divide between high-dimensional sensory inputs action.

Language: Английский

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Wzorce poznania rozproszonego

Studia Philosophiae Christianae, Journal Year: 2024, Volume and Issue: 60(1), P. 79 - 99

Published: July 31, 2024

Nawet jeżeli integrację poznania rozproszonego z mechanistycznymi koncepcjami wyjaśniania można uznać za ruch interesujący, a w przypadku powodzenia prowadzący do niebanalnego rozszerzenia kognitywistycznych badań nad poznaniem, to perspektywy teoretyka należy ten ryzykowny. W poniższej pracy, dyskusji propozycją Witolda Wachowskiego (2022), postaram się przedstawić ryzyko, jakim wiąże wspomniana integracja i zaproponuję rozwiązanie alternatywne, polegające na połączeniu rozproszenia teorią sieci. Teoria ta, mojej opinii, pozwala bardziej owocne badanie wzorców poznania. ----------------------------------------- Zgłoszono: 26/09/2023. Zrecenzowano: 26/03/2024. Zaakceptowano publikacji: 10/06/2024.

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