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First, we will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes the shattering capacity for linearly classifying object categories based on the underlying neural manifolds’ structural properties.
Next, we will describe how such methods can, in fact, open the ‘black box’ of distributed neural networks, by showing how we can understand a) the role of network motifs in task implementation in neural networks and b) the role of neural noise in adversarial robustness in vision and audition.
Finally, we will discuss our recent efforts to develop biologically plausible learning rules for deep neural networks, inspired by recent experimental findings in synaptic plasticity. By extending our mathematical toolkit for analyzing representations and learning rules underlying complex neuronal networks, we hope to contribute towards the long-term challenge of understanding the neuronal basis of tasks and behaviors.
Where: Building 420 050 or Zoom (hybrid format). Note we are back to hybrid!