University of Manchester at SfN 2026

Nov 14–18, 2026 Washington, DC ~21k attendees Website

University of Manchester brings 2 presentations to SfN 2026 in Washington. Its presentations centre on visual responses during behaviour and machine learning for brain imaging. One thread examines how mice respond to coarse visual patterns through a pathway reliant on melanopsin.

Organization	SfN 2026 Attendance
University of Manchester Manchester, UK
manchester.ac.uk Universities and research institutes Public research university in Manchester, England.	Poster Sun Nov 15 1–5pm Part of Session PSTR097: Visual System: Responses During Behavior and Response Modulation An innate behavioural response to coarse visual patterns in mice reliant on melanopsin WCC Halls A-C · Board X26 Poster Wed Nov 18 8am–noon Part of Session PSTR338: Statistical and Machine Learning Methods for Neural Data Uncertainty-Aware Deep Learning for Reliable Neurodegenerative Disease Diagnosis from Brain MRI: Calibration, Uncertainty Quantification, and Clinically Interpretable Confidence Reporting WCC Halls A-C · Board Web Only

Organization

SfN 2026 Attendance

University of Manchester

Manchester, UK

manchester.ac.uk

Universities and research institutes

Public research university in Manchester, England.

Poster

Sun Nov 15
1–5pm

Part of Session PSTR097: Visual System: Responses During Behavior and Response Modulation

An innate behavioural response to coarse visual patterns in mice reliant on melanopsin

WCC Halls A-C · Board X26

Poster

Wed Nov 18
8am–noon

Part of Session PSTR338: Statistical and Machine Learning Methods for Neural Data

Uncertainty-Aware Deep Learning for Reliable Neurodegenerative Disease Diagnosis from Brain MRI: Calibration, Uncertainty Quantification, and Clinically Interpretable Confidence Reporting

WCC Halls A-C · Board Web Only

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Not affiliated with the Society for Neuroscience. Organization details are derived from SfN’s published exhibitor list, its Neuroscience Meeting Planner and each organization’s own website. Submit a correction