neuroscience

The article presents Joscha Bach's argument that replicating the physical wiring of the brain cannot produce human-like consciousness, emphasizing that mental states arise from information processing rather than mere anatomical mapping.

Researchers at Baylor College of Medicine discovered that the unconscious human hippocampus can process language and predict words, challenging current views on consciousness. The study, published in Nature, suggests biological parallels to AI predictive coding.

This article profiles MIT senior Olivia Honeycutt, highlighting her interdisciplinary research at the intersection of linguistics, computation, and cognition, with a focus on comparing human language processing with large language models.

NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.

This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.

TRIBE v2 is a new predictive foundation model designed to understand how the human brain processes complex stimuli.

This blog post explores the intersection of machine learning and neuroscience, specifically focusing on using multivariate classification techniques on neuroimaging data to understand brain function and behavior.