P4 25 “Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy” by @shuqiw.bsky.social
P4 35 “Biologically plausible contrastive learning rules with top-down feedback for deep networks” by @zihan-wu.bsky.social
P4 25 “Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy” by @shuqiw.bsky.social
P4 35 “Biologically plausible contrastive learning rules with top-down feedback for deep networks” by @zihan-wu.bsky.social
P3 4 “Toy Models of Identifiability for Neuroscience” by @flavioh.bsky.social
P3 55 “How many neurons is “infinitely many”? A dynamical systems perspective on the mean-field limit of structured recurrent neural networks” by Louis Pezon
P3 4 “Toy Models of Identifiability for Neuroscience” by @flavioh.bsky.social
P3 55 “How many neurons is “infinitely many”? A dynamical systems perspective on the mean-field limit of structured recurrent neural networks” by Louis Pezon
P2 2 “Biologically informed cortical models predict optogenetic perturbations” by @bellecguill.bsky.social
P2 12 “High-precision detection of monosynaptic connections from extra-cellular recordings” by @shuqiw.bsky.social
P2 2 “Biologically informed cortical models predict optogenetic perturbations” by @bellecguill.bsky.social
P2 12 “High-precision detection of monosynaptic connections from extra-cellular recordings” by @shuqiw.bsky.social
We also show the capacity of our model of both forward and backward transfer! All of this thanks to the shared neuronal activity across tasks.
We also show the capacity of our model of both forward and backward transfer! All of this thanks to the shared neuronal activity across tasks.