Assistant Professor, University of California, Berkeley
1 paper at NeurIPS 2025
We derive brain-like inference as natural gradient descent on free energy (FOND). The resulting spiking network (iP-VAE) outperforms amortized VAEs in reconstruction-sparsity trade-offs and out-of-distribution generalization.
