arXiv subjects

Linnea Evanson

Publications and source records attributed to Linnea Evanson.

6 recordsLinked to original sources

arXiv2026

NeuralSet: A High-Performing Python Package for Neuro-AI

Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by a fragmented software ecosystem. Current tools are siloed by recording modality and optimized for small-scale, in-memory workflows, limiting the use of massive, naturalistic datasets. Here, we introduce NeuralSet, a Python framework that efficiently unifies the processing of diverse neural recordings (including fMRI, M/EEG, and spikes) and complex experimental stimuli (such as text, audio, and video). By decoupling experimental metadata from lazy, memory-efficient data extraction, NeuralSet harmonizes standard neuroscientific preprocessing pipelines with pretrained deep learning embeddings. This approach provides a single PyTorch-ready interface that scales seamlessly from local prototyping to high-performance cluster execution. By eliminating manual data wrangling and ensuring full computational provenance, NeuralSet establishes a scalable, unified infrastructure for the next generation of neuro-AI research.

q-bio.NC↗

arXiv2026

Temporal structure of the language hierarchy within small cortical patches

Speech production requires the rapid coordination of a complex hierarchy of linguistic units, transforming a semantic representation into a precise sequence of articulatory movements. To unravel the neural mechanisms underlying this feat, we leverage recordings from eight 3.2 x 3.2 mm 64-microelectrode arrays implanted in the motor cortex and inferior frontal gyrus of two patients tasked to produce twenty thousand sentences. We show that a hierarchy of linguistic features are robustly encoded in most of these small cortical patches. Contrary to our expectations, instead of a clear macroscopic organization between patches, we observe a multiplexing of phonetic, syllabic and lexical representations within each cortical patch. Critically, this coding scheme dynamically changes over time to allow successive phonemes, syllables and words to be simultaneously represented without interference. Overall, these results, reminiscent of position encoding in transformers, show how small cortical patches organize the unfolding of the speech hierarchy during language production.

q-bio.NC↗

arXiv2025

From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining

Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to leverage week-long intracranial and audio recordings from patients undergoing clinical monitoring, effectively increasing the training dataset size by over two orders of magnitude. With this pretraining, our contrastive learning model substantially outperforms models trained solely on classic experimental data, with gains that scale log-linearly with dataset size. Analysis of the learned representations reveals that, while brain activity represents speech features, its global structure largely drifts across days, highlighting the need for models that explicitly account for cross-day variability. Overall, our approach opens a scalable path toward decoding and modeling brain representations in both real-life and controlled task settings.

cs.SD↗

arXiv2025

Emergence of Language in the Developing Brain

A few million words suffice for children to acquire language. Yet, the brain mechanisms underlying this unique ability remain poorly understood. To address this issue, we investigate neural activity recorded from over 7,400 electrodes implanted in the brains of 46 children, teenagers, and adults for epilepsy monitoring, as they listened to an audiobook version of "The Little Prince". We then train neural encoding and decoding models using representations, derived either from linguistic theory or from large language models, to map the location, dynamics and development of the language hierarchy in the brain. We find that a broad range of linguistic features is robustly represented across the cortex, even in 2-5-year-olds. Crucially, these representations evolve with age: while fast phonetic features are already present in the superior temporal gyrus of the youngest individuals, slower word-level representations only emerge in the associative cortices of older individuals. Remarkably, this neuro-developmental trajectory is spontaneously captured by large language models: with training, these AI models learned representations that can only be identified in the adult human brain. Together, these findings reveal the maturation of language representations in the developing brain and show that modern AI systems provide a promising tool to model the neural bases of language acquisition.

q-bio.NC↗

arXiv2024

New modalities of cortical electrophysiology, perspectives in medical research and human physiology

Recent advances in material technology and in micro- and nano-electronics have profoundly changed the design of intracranial electrophysiology electrodes. It is now possible to manufacture electrodes that record cortical activity at a spatial resolution that was previously unthinkable. This high spatial resolution enables recording of the functional structures of the brain, and differentiation of the activity of the different types of neurons composing them. In this paper, we present a review of the different types of electrodes now available, and then suggest one of the first applications for such high resolution electrodes, namely a means to better characterise the mechanisms that generate focal seizures in epileptics. Finally, we reflect more broadly on prospects for their future use.

q-bio.NC↗

arXiv2023

Language acquisition: do children and language models follow similar learning stages?

During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master increasingly complex syntactic structures. However, the computational principles that lead to this learning trajectory remain largely unknown. To investigate this, we here compare the learning trajectories of deep language models to those of children. Specifically, we test whether, during its training, GPT-2 exhibits stages of language acquisition comparable to those observed in children aged between 18 months and 6 years. For this, we train 48 GPT-2 models from scratch and evaluate their syntactic and semantic abilities at each training step, using 96 probes curated from the BLiMP, Zorro and BIG-Bench benchmarks. We then compare these evaluations with the behavior of 54 children during language production. Our analyses reveal three main findings. First, similarly to children, the language models tend to learn linguistic skills in a systematic order. Second, this learning scheme is parallel: the language tasks that are learned last improve from the very first training steps. Third, some - but not all - learning stages are shared between children and these language models. Overall, these results shed new light on the principles of language acquisition, and highlight important divergences in how humans and modern algorithms learn to process natural language.

cs.CL↗