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Identifying structural design principles shaping the computational abilities of recurrent neural networks
Tom Talpir, Elad Schneidman
Understanding how the architecture of neural networks shapes the computations they carry is a central challenge in neuroscience and machine learning. While specific circuit archite…
Semantic representations emerge in biologically inspired ensembles of cross-supervising neural networks
Roy Urbach, Elad Schneidman
Brains learn to represent information from a large set of stimuli, typically by weak supervision. Unsupervised learning is therefore a natural approach for exploring the design of…
The computational and learning benefits of Daleian neural networks
Adam Haber, Elad Schneidman
Dale's principle implies that biological neural networks are composed of neurons that are either excitatory or inhibitory. While the number of possible architectures of such Daleia…
Discrete modes of social information processing predict individual behavior of fish in a group
Roy Harpaz, Gašper Tkačik, Elad Schneidman
Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex in…
Searching for collective behavior in a network of real neurons
Gašper Tkačik, Olivier Marre, Dario Amodei +3
Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this pri…
Stimulus-dependent maximum entropy models of neural population codes
Einat Granot-Atedgi, Gašper Tkačik, Ronen Segev +1
Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus…