This remains my personal fave:

www.youtube.com/watch?v=3ADu...

Topics include but are not limited to:
•Optimal and adaptive stimulus selection for fitting, developing, testing or validating models
•Stimulus ensembles for model comparison
•Methods to generate stimuli with “naturalistic” properties
•Experimental paradigms and results using model-optimized stimuli

The symposium also serves to kick off a special issue of JOV!

"Choose your stimuli wisely: Advances in stimulus synthesis and selection"
jov.arvojournals.org/ss/synthetic...
Paper Deadline: Dec 12th

For those not able to attend tomorrow, I will strive to post some of the highlights here 👀 👀 👀

This is joint work with fantastic co-authors from @flatironinstitute.org Center for Computational Neuroscience: @lipshutz.bsky.social (co-first) @sarah-harvey.bsky.social @itsneuronal.bsky.social @eerosim.bsky.social

These examples demonstrate how our framework can be used to probe for informative differences in local sensitivities between complex models, and suggest how it could be used to compare model representations with human perception.

In a second example, we apply our method to a set of deep neural network models and reveal differences in the local geometry that arise due to architecture and training types, illustrating the method's potential for revealing interpretable differences between computational models.

As an example, we use this framework to compare a set of simple models of the early visual system, identifying a novel set of image distortions that allow immediate comparison of the models by visual inspection.

This provides an efficient method to generate stimulus distortions that discriminate image representations. These distortions can be used to test which model is closest to human perception.

We then extend this work to show that the metric may be used to optimally differentiate a set of *many* models, by finding a pair of “principal distortions” that maximize the variance of the models under this metric.

We use the FIM to define a metric on the local geometry of an image representation near a base image. This metric can be related to previous work investigating the sensitivities of one or two models.

We propose a framework for comparing a set of image representations in terms of their local geometries. We quantify the local geometry of a representation using the Fisher information matrix (FIM), a standard statistical tool for characterizing the sensitivity to local stimulus distortions.