SAN DIEGO — Artificial intelligence (AI) is transforming the field of neuroscience, enabling scientific breakthroughs previously out of reach. New applications of AI and machine learning techniques will be presented at Neuroscience 2025, the annual meeting of the Society for Neuroscience and the world’s largest source of emerging news about brain science and health.
The promise of AI is immense. In the realm of neuroscience, AI models can analyze enormous datasets quickly and effectively, from subtle body movements to internal word processing. AI could eventually even simulate some brain functions, allowing researchers to test hypotheses without the need for a human or animal subject. This analysis can accelerate experiments and lead to faster insights, which in turn will drive diagnosis and treatment options.
New findings show that:
“AI is no longer just a tool borrowed from computer science,” said Christopher Rozell, PhD , executive director of the Institute for Neuroscience, Neurotechnology, and Society at the Georgia Institute of Technology and moderator of the press conference. “It’s deeply integrated into neuroscience, enabling new discoveries and therapies by allowing us to identify patterns and mechanisms that were invisible before. At the same time, AI was inspired by biological intelligence, so the more we learn about the brain, the more we can improve AI.”
For complete access to Neuroscience 2025 in-person and online, request media credentials . This research was supported by national funding agencies including the National Institutes of Health and private funding organizations.
Monday, November 17, 2025
11 a.m.–noon PST
San Diego Convention Center, Room 15A, and online for registered media
AI Press Conference Summary
Decoding and characterizing the intracranial representation of semantic information
Matthew Nelson, matthewnelson@uabmc.edu , Abstract PSTR249.12
Integrating clinician insights into markerless gait analysis: Toward AI-driven, interpretable gait assessment
Trisha Kesar, trisha.m.kesar@emory.edu , Abstract PSTR253.20
Early Detection of Freezing of Gait episodes in Parkinson’s disease using a Deep Learning Approach
Paul Cantlay, cantlap@ccf.org, Abstract PSTR478.10
Accurate inference of single-neuron biophysics from voltage responses with deep learning
Roy Ben-Shalom, rbenshalom@health.ucdavis.edu , Abstract PSTR145.09
Predicting Structure-Function Relationships in Cortex via Artificial Neural Networks
Marcel Oberlaender, m.oberlaender@vu.nl , Abstract PSTR478.11
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The Society for Neuroscience (SfN) is an organization of nearly 30,000 basic scientists and clinicians who study the brain and the nervous system.