Cookies
We use essential cookies to run the site. Analytics cookies are optional and help us improve World Wide. Learn more.
Dr
Max Planck Institute for Dynamics and Self-Organization
Showing your local timezone
Schedule
Tuesday, November 2, 2021
5:50 PM Europe/Berlin
Recording provided by the organiser.
Domain
NeuroscienceHost
SNUFA
Duration
70 minutes
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that input weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under specific requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, inhibition learns to locally balance excitatory input in individual dendritic compartments, and thereby can modulate excitatory synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex, high-dimensional and correlated inputs. It also works in the presence of inhibitory transmission delays, where Hebbian-like plasticity typically fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo, and suggest that both are crucial for representation learning.
Viola Priesemann
Dr
Max Planck Institute for Dynamics and Self-Organization
neuro
Nervous system function relies on the polarized architecture of neurons, established by directional transport of pre- and postsynaptic cargoes. While delivery of postsynaptic components depends on the
neuro
A combinatorial neural code for long-term motor memory
neuro
The postdoctoral researcher will work on an interdisciplinary project at the intersection of deep learning and comparative politics. The candidate will work in the Human-Centered Machine Learning (HuM
We are looking for highly motivated researchers to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is repres
The CSNG Lab at the Faculty of Mathematics and Physics at the Charles University is seeking a highly motivated Postdoctoral Researcher to join our team to work on a digital twin model of the visual sy
PROJECT SUMMARY / ABSTRACT Cluster headache (CH), which affects about 1 in 1,000 people, is a severe and debilitating primary headache disorder characterized by repeated attacks occurring in clusters