Biological Cybernetics, Journal Year: 2024, Volume and Issue: 119(1)
Published: Dec. 30, 2024
Language: Английский
Cell, Journal Year: 2024, Volume and Issue: 187(7), P. 1745 - 1761.e19
Published: March 1, 2024
Proprioception tells the brain state of body based on distributed sensory neurons. Yet, principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate neural code neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal generated large-scale movement repertoire train networks 16 hypotheses, each representing different computational goals. found emerging, task-optimized internal representations generalize from synthetic data predict dynamics CN S1 primates. Computational tasks aim limb position velocity were best at predicting activity both areas. Since task optimization develops better during active than passive movements, postulate is top-down modulated goal-directed movements.
Language: Английский
Citations
15
Cognitive Systems Research, Journal Year: 2024, Volume and Issue: 87, P. 101244 - 101244
Published: April 24, 2024
Language: Английский
Citations
6
Cell, Journal Year: 2024, Volume and Issue: 187(21), P. 5814 - 5832
Published: Oct. 1, 2024
Language: Английский
Citations
5
Progress in Neuro-Psychopharmacology and Biological Psychiatry, Journal Year: 2025, Volume and Issue: unknown, P. 111332 - 111332
Published: March 1, 2025
Language: Английский
Citations
0
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
Citations
3
Journal of Neuroscience Methods, Journal Year: 2024, Volume and Issue: 413, P. 110318 - 110318
Published: Nov. 9, 2024
Language: Английский
Citations
2
PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(11), P. e1012578 - e1012578
Published: Nov. 14, 2024
Prior research has shown that manipulating stimulus energy by changing both contrast and variability results in confidence-accuracy dissociations humans. Specifically, even when performance is matched, higher leads to confidence. The most common explanation for this effect, derived from cognitive modeling, the positive evidence heuristic where confidence neglects disconfirms choice. However, an alternative signal-and-variance-increase hypothesis, according which these arise changes separation variance of perceptual representations. Because artificial neural networks lack built-in heuristics, they can serve as a test necessity heuristics explaining dissociations. Therefore, we tested whether induced manipulations emerge naturally convolutional (CNNs). We found that, across three different manipulations, CNNs produced similar those This effect was present range CNN architectures shallow 4-layer very deep ones, such VGG-19 ResNet-50 pretrained on ImageNet. Further, traced back reason all same signal-and-variance increase been proposed humans: increased distributions CNNs’ output layer leading matched accuracy. These findings cast doubt explain human establish promising models testing theories behavior.
Language: Английский
Citations
2
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
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 26, 2024
Abstract Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align behavior and representations in biological systems. Here, we show that this alignment is a consequence of convergence onto the same by high-performing ANNs brains. We developed method to identify stimuli systematically vary degree inter-model representation agreement. Across language vision, then showed from high-and low-agreement sets predictably modulated model-to-brain alignment. also examined which stimulus features distinguish high-from sentences images. Our results establish universality as core component provide new approach for using uncover structure computations.
Language: Английский
Citations
1
Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 11 - 21
Published: Jan. 1, 2024
Language: Английский
Citations
0