6 citations · 15 across the 4 of their papers we have counts for
5 papers
Dendritic structure enables powerful plasticity
Ben von Hünerbein, Federico Benitez, Kevin Max +10
Over the past decades, it has become increasingly clear that the complex morphology of cortical neurons is more than just a quirk of evolution, and that dendritic compartments serv…
Synthetic Biology meets Neuromorphic Computing: Towards a bio-inspired Olfactory Perception System
Kevin Max, Larissa Sames, Shimeng Ye +2
In this study, we explore how the combination of synthetic biology, neuroscience modeling, and neuromorphic electronic systems offers a new approach to creating an artificial syste…
Spike-based alignment learning solves the weight transport problem
Timo Gierlich, Andreas Baumbach, Akos F. Kungl +2
In both machine learning and in computational neuroscience, plasticity in functional neural networks is frequently expressed as gradient descent on a cost. Often, this imposes symm…
Backpropagation through space, time, and the brain
Benjamin Ellenberger, Paul Haider, Jakob Jordan +5
How physical networks of neurons, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains, to a large extent, an open question. In machine l…
Learning efficient backprojections across cortical hierarchies in real time
Kevin Max, Laura Kriener, Garibaldi Pineda García +4
Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, whi…