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Neural encoding is the study of how neurons represent information with electrical activity (action potentials) at the level of individual cells or in networks of neurons. Studies of neural encoding aim to characterize the relationship between sensory stimuli or behavioural output and neural signals.
How sensory systems rapidly adapt to changing stimulus statistics remains unclear. Here the authors show that gain adaptation in recurrent networks can implement fast efficient coding, unifying prior attraction and adapter repulsion, and supporting adaptive behavior.
Representational similarity analysis of human brain fMRI during natural scene viewing reveals two cortical routes: a ventromedial pathway for scene context and layout, and a lateral occipitotemporal pathway tuned to animate content.
Whether large language models (LLMs) process language in a human-like manner remains unclear. Here, the authors re-examine prior LLM-to-brain mapping results, demonstrating that LLM-brain alignment can result from non-robust methods.
Perseveration – repeating one choice when others would generate larger rewards – is a common behavior, but neither its purpose nor neuronal mechanisms are understood. Here the authors demonstrate a neural correlate and causal role of dorsal prefrontal cortex, specifically anterior supplementary motor cortex, in perseveration in mice performing a dynamic reward learning task.
Decision-making is influenced by subjective reference points. Here, choices are framed as potential gains or losses relative to a cumulative token count, revealing a gain-related substrate of reference-dependency in dorsal anterior cingulate cortex.
Dorsal medial prefrontal cortex simultaneously yet independently encodes interconnected attributes of environmental cues to motivate appetitive and aversive behaviour in mice via multifaceted representations.
Research now suggests that large language models (LLMs) are viable in silico models of human language processing. By examining multi-participant high-quality brain responses, researchers were able to break new ground in the validation of this proposal, which could dramatically reduce the barrier to studying how language is processed in the human brain.
