Deperrois, N., Petrovici, M.A., Senn, W., & Jordan, J. (2021). Learning cortical representations through perturbed and adversarial dreaming. arXiv preprint arXiv:2109.04261.
Jordan, J., Sacramento, J., Wybo, W. A., Petrovici, M. A., & Senn, W. (2021). Learning
Bayes-optimal dendritic opinion pooling. arXiv preprint arXiv:2104.13238.
Jakob Jordan
Department of Physiology, University of Bern, Switzerland
03.03.2022, SCN Retreat, Crans-Montana, Switzerland
[Illing et al., 2021]
[Goetschalckx et al., 2021]
GenerativeAdversarial
Networks
Nicolas Deperrois
Objectives:
(remark: FID uses an Inception-v3 network, hence likely
focuses on local image statistics, e.g., Brendel & Bethge, 2019)
visual
auditory
olfactory
Bayes-optimal inference
Bidirectional voltage dynamics
Average membrane potentials
= reliability-weighted opinions
Membrane potential variance
= 1/total reliability
Synaptic plasticity modifies excitatory/inhibitory synapses
\(u_\text{s}^*\): sample from target distribution \(p^*(u_\text{s})\)
target
actual
The trained model approximates ideal observers
and reproduces psychophysical signatures of experimental data
[Nikbakht et al., 2018]
The trained model exhibits cross-modal suppression:
[Ohshiro et al., 2017]
Adversarial learning during wakefulness and sleep allows the emergence of organized cortical representations.
Single neurons with conductance-based synapses learn to be optimal cue integrators.
Deperrois, N., Petrovici, M.A., Senn, W., & Jordan, J. (2021). Learning cortical representations through perturbed and adversarial dreaming. arXiv preprint arXiv:2109.04261.
Jordan, J., Sacramento, J., Wybo, W. A., Petrovici, M. A., & Senn, W. (2021). Learning
Bayes-optimal dendritic opinion pooling. arXiv preprint arXiv:2104.13238.