93 citations · 329 across the 12 of their papers we have counts for
13 papers
Aligned and oblique dynamics in recurrent neural networks
Friedrich Schuessler, Francesca Mastrogiuseppe, Srdjan Ostojic +1
The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. The…
Charting and navigating the space of solutions for recurrent neural networks
Elia Turner, Kabir Dabholkar, Omri Barak
Recurrent Neural Networks (RNNs) were recently successfully used to model the way neural activity drives task-related behavior in animals, operating under the implicit assumption t…
Cancer Progression as a Learning Process
Aseel Shomar, Omri Barak, Naama Brenner
Drug resistance and metastasis - the major complications in cancer - both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, the…
Quality of internal representation shapes learning performance in feedback neural networks
Lee Susman, Francesca Mastrogiuseppe, Naama Brenner +1
A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservo…
The interplay between randomness and structure during learning in RNNs
Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil +2
Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks. However, the solutions found by learning and the ef…
Mapping low-dimensional dynamics to high-dimensional neural activity: A derivation of the ring model from the neural engineering framework
Omri Barak, Sandro Romani
Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed n…
Implementing Inductive bias for different navigation tasks through diverse RNN attractors
Tie Xu, Omri Barak
Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map. The precise form of this represe…
Dynamics of random recurrent networks with correlated low-rank structure
Friedrich Schuessler, Alexis Dubreuil, Francesca Mastrogiuseppe +2
A given neural network in the brain is involved in many different tasks. This implies that, when considering a specific task, the network's connectivity contains a component which…
Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network
Tomoki Kurikawa, Omri Barak, Kunihiko Kaneko
Memories in neural system are shaped through the interplay of neural and learning dynamics under external inputs. By introducing a simple local learning rule to a neural network, w…
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv, Alexander Rivkind, Omri Barak
To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories…
Stable memory with unstable synapses
Lee Susman, Naama Brenner, Omri Barak
What is the physiological basis of long-term memory? The prevailing view in neuroscience attributes changes in synaptic efficacy to memory acquisition. This view implies that stabl…
One step back, two steps forward: interference and learning in recurrent neural networks
Chen Beer, Omri Barak
Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has…
Local Dynamics in Trained Recurrent Neural Networks
Alexander Rivkind, Omri Barak
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task related neural dynamics we study trained Recurrent Neural Networ…