310 citations · 1.3k across the 14 of their papers we have counts for
15 papers
Theory of coupled neuronal-synaptic dynamics
David G. Clark, L. F. Abbott
In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plast…
Dimension of activity in random neural networks
David G. Clark, L. F. Abbott, Ashok Litwin-Kumar
Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and…
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks
Samuel Lippl, L. F. Abbott, SueYeon Chung
Understanding the asymptotic behavior of gradient-descent training of deep neural networks is essential for revealing inductive biases and improving network performance. We derive…
Credit Assignment Through Broadcasting a Global Error Vector
David G. Clark, L. F. Abbott, SueYeon Chung
Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks (DNNs) with remarkable success. That biological neural circuits appear to perform credit as…
Neural population geometry: An approach for understanding biological and artificial neural networks
SueYeon Chung, L. F. Abbott
Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unl…
Lyapunov spectra of chaotic recurrent neural networks
Rainer Engelken, Fred Wolf, L. F. Abbott
Brains process information through the collective dynamics of large neural networks. Collective chaos was suggested to underlie the complex ongoing dynamics observed in cerebral co…