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Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on univer...

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Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. We argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis, which proposes that alignment arises not from convergence toward a single global optimum but from overlap in the ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in the ecological constraint space rather than as a search for a single optimal world model.

Index and overview of neuroimaging datasets for visual perception reconstruction. - seelikat/neuro-visual-reconstruction-dataset-index

Abstract. Reward processing is critical for motivation, learning, and decision making. It involves a network centered on the fronto-striatal circuit, with the ventral striatum (VS) playing a pivotal r...

How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests that learning reduces redundancy in neural representations to improve efficiency, whereas anot...

Index and overview of neuroimaging datasets for visual perception reconstruction. - seelikat/neuro-visual-reconstruction-dataset-index

Representational similarity analysis (RSA) is widely used to analyze the alignment between humans and neural networks; however, conclusions based on this approach can be misleading without...

New research forms a bridge between human and machine representation

Subjective experience is key to understanding affective states, characterized by valence and arousal. Traditional experiments using post-stimulus summary rat...

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To the Editor Chowdhury et al1 evaluated a biomarker for pain sensitivity, combining peak alpha frequency and corticomotor excitability. The authors report outstanding performance (validation set area...

Der Neurowissenschaftler Simon Hofmann untersucht, wie künstliche Intelligenz in der Medizin zum Einsatz kommen kann.

An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...

Two EEG studies in healthy human adults suggest that choice history and stimulus probability-induced biases in somatosensory perception are reflected in distinct prestimulus beta power modulations acr...

YouTube video by Moritz Gerster