Robert A. Legenstein

Non-synaptic plasticity enables memory-dependent local learning

The paper proposes that both synaptic and non-synaptic plasticity are essential for memory-dependent processing in neuronal networks, showing their roles in different time scales and tasks.

Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks

This paper shows that biologically realistic spiking neural networks can learn from few examples by storing priors in synaptic weights and using network state for rapid information absorption.

Spike frequency adaptation supports network computations on temporally dispersed information

The paper shows spike frequency adaptation improves spiking neural network performance on temporally dispersed tasks, reaching human brain levels.

Spike-based symbolic computations on bit strings and numbers

This paper reviews spike-based symbolic computations in neural networks and shows small spiking networks can perform symbolic computations on bit sequences and numbers using biologically plausible learning rules.

Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

The paper proposes a normative model where top-down inputs in apical dendrites provide probabilistic priors for sensory information, consistent with synaptic plasticity and NMDA-spikes.

A solution to the learning dilemma for recurrent networks of spiking neurons

This paper introduces a new learning method for spiking neural networks that enables biologically plausible online learning through gradient descent, achieving performance comparable to BPTT.

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

This paper introduces H-Mem, a neural network model that uses Hebbian plasticity to memorize associations and perform computations, demonstrating its ability to solve question-answering tasks on synthetic stories.

STDP forms associations between memory traces in networks of spiking neurons

The paper investigates how spike-timing-dependent plasticity (STDP) enables the formation of associations between memory traces in spiking neuron networks, showing it can reproduce both fast overlap-based and new-assembly-based memory association patterns.