Continual learning in RNNs
started in Methods for Computational Neuroscience 2024 Summer School - with collaborators who will be disclosed in the future 
Projects
Multiple adaptation mechanisms account for the emergence of prediction error responses in V1
We identify possible plasticity mechanisms that explain the adaptation of responses to repeated stimulus presentations and the emergence of prediction error responses in a recurrent circuit model with multiple interneuron sub-types. 
Shaping low-rank recurrent neural networks with biological learning rules
Biologically-plausible training of low-rank RNNs - with Pablo Crespo, Dimitra Maoutsa, Matt Getz 
Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
How different forms of spike-timing dependent plasticity affect the functional properties of spiking SSNs - with Raul Adell Segarra, Dylan Festa, Dimitra Maoutsa 
Revealing latent stochastic dynamics from neuronal spiking patterns
Inference of latent dynamics through stochastic control - with myself 