8 citations · 11 across the 4 of their papers we have counts for
4 papers
Storing overlapping associative memories on latent manifolds in low-rank spiking networks
William F. Podlaski, Christian K. Machens
Associative memory architectures such as the Hopfield network have long been important conceptual and theoretical models for neuroscience and artificial intelligence. However, tran…
Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks
William F. Podlaski, Christian K. Machens
Deep feedforward and recurrent rate-based neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale…
Nonlinear computations in spiking neural networks through multiplicative synapses
Michele Nardin, James W Phillips, William F Podlaski +1
The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be i…
Biological credit assignment through dynamic inversion of feedforward networks
William F. Podlaski, Christian K. Machens
Learning depends on changes in synaptic connections deep inside the brain. In multilayer networks, these changes are triggered by error signals fed back from the output, generally…