Keynote & Tutorial – Quantifying Similarity between Neural Population Codes

Alex Williams1,2, Sarah Harvey2, Tahereh Toos3, Heiko Schütt4 and Niko Kriegeskorte3, 1New York University, 2Flatiron Institute, 3Columbia University, 4Université du Luxembourg. Cognitive neuroscience is now flush with measurements of neural population activity in humans, animal subjects, and large-scale artificial network models. This keynote and tutorial will address an extensively studied, yet unresolved, question: What statistical methods should we use to quantify whether two or more neural systems have “similar” neural population activity? Indeed, there has been a proliferation of approaches ranging from linear predictivity scores, canonical correlations analysis (CCA), representational similarity analysis (RSA), Procrustes distance, and many more. To help practitioners navigate this complex menu of choices, we will describe a unifying conceptual framework that enables several simplifications. For example, we show that a variant of RSA called Normalized Bures Similarity is essenti

Keynote & Tutorial – Quantifying Similarity between Neural Population Codes Skip to main content Keynote & Tutorial Wednesday, August 7, 2:00 - 2:40 pm, Kresge Hall (Keynote) Wednesday, August 7, 4:30 - 6:15 pm, Sala de Puerto Rico (Tutorial) Quantifying Similarity between Neural Population Codes Alex Williams 1,2 , Sarah Harvey 2 , Tahereh Toos 3 , Heiko Schütt 4 and Niko Kriegeskorte 3 , 1 New York University, 2 Flatiron Institute, 3 Columbia University, 4 Université du Luxembourg. Cognitive neuroscience is now flush with measurements of neural population activity in humans, animal sub

related reading

• Zoom In: An Introduction to Circuitsdistill.pub
• How should we compare neural network representations? – The Berkeley Artificial Intelligence Research Blogbair.berkeley.edu
• All things neural distribution alignment | neuralignnerdslab.github.io
• POYO-1poyo-brain.github.io
• Representational geometry: integrating cognition, computation, and the brain - PMCncbi.nlm.nih.gov
• The Topology and Geometry of Neural Representationsarxiv.org
• Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience | bioRxivbiorxiv.org
• Towards a “universal translator” for neural dynamics at single-cell, single-spike resolutionarxiv.org
• Measures of spike train synchrony - Scholarpediascholarpedia.org
• Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity - PMCpmc.ncbi.nlm.nih.gov
• Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models | PLOS Computational Biologyjournals.plos.org
• Introduction — Neuromatch Academy: Computational Neurosciencecompneuro.neuromatch.io