Search Results for author:
Found 4 papers, 2 papers with code
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
Training networks consisting of biophysically accurate neuron models could allow for new insights into how brain circuits can organize and solve tasks.
How connectivity structure shapes rich and lazy learning in neural circuits
Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks.
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely.
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.
