7 citations · 16 across the 4 of their papers we have counts for
4 papers
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
James Hazelden, Yuhan Helena Liu, Eli Shlizerman +1
Training networks consisting of biophysically accurate neuron models could allow for new insights into how brain circuits can organize and solve tasks. We begin by analyzing the ex…
How connectivity structure shapes rich and lazy learning in neural circuits
Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford +3
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight di…
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Yuhan Helena Liu, Stephen Smith, Stefan Mihalas +2
The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biolo…
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Yuhan Helena Liu, Arna Ghosh, Blake A. Richards +2
To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond t…