Jérémy Rapin
5 papers · Latest:
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench is a unified open-source framework for systematically benchmarking AI models of brain activity, including a large EEG benchmark.
A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a tri-modal AI foundation model that accurately predicts human brain activity across vision, audition, and language, enabling in-silico neuroscience.
NeuralSet: A High-Performing Python Package for Neuro-AI
NeuralSet is a Python package that unifies diverse neural data and stimuli processing for neuro-AI research, scaling from local to cluster.
Temporal structure of the language hierarchy within small cortical patches
Small cortical patches dynamically multiplex phonetic, syllabic, and lexical representations for speech production, challenging macroscopic organization.
Code Llama: Open Foundation Models for Code
Code Llama is a new family of open-source large language models specialized for coding tasks, achieving state-of-the-art results on multiple benchmarks with support for long contexts and code infilling.
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