1 citations · 2 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules
Alireza Alemi, Christian Machens, Sophie Denève +1
Understanding how recurrent neural circuits can learn to implement dynamical systems is a fundamental challenge in neuroscience. The credit assignment problem, i.e. determining the…
Learning to represent signals spike by spike
Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi +2
A key question in neuroscience is at which level functional meaning emerges from biophysical phenomena. In most vertebrate systems, precise functions are assigned at the level of n…
Percept formation from neural populations in sensory decision-making tasks
Adrien Wohrer, Christian K. Machens
We study a standard linear readout model of perceptual integration from a population of sensory neurons. We show that the readout can be associated to a set of characteristic equat…