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Joscha Tapani Schmiedt

dblp:62/10453 · also Joscha Schmiedt · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms
neuromorphic computing
0.112010
Spike timing-dependent plasticity as dynamic filter · NIPS 2010
Emerging computing paradigms › neuromorphic computing › synaptic plasticity
spike-timing-dependent plasticity
0.112010
Spike timing-dependent plasticity as dynamic filter · NIPS 2010
Emerging computing paradigms › neuromorphic computing
spiking neural network
0.112010
Spike timing-dependent plasticity as dynamic filter · NIPS 2010
Emerging computing paradigms › neuromorphic computing
synaptic plasticity
0.112010
Spike timing-dependent plasticity as dynamic filter · NIPS 2010

Methods — techniques the papers use, named apart from their topics

dynamical systems analysis · 0.1differential equation model · 0.1
YearPublicationVenuePosition
2010 Spike timing-dependent plasticity as dynamic filter
abstract
When stimulated with complex action potential sequences synapses exhibit spike timing-dependent plasticity (STDP) with attenuated and enhanced pre- and postsynaptic contributions to long-term synaptic modifications. In order to investigate the functional consequences of these contribution dynamics (CD) we propose a minimal model formulated in terms of differential equations. We find that our model reproduces a wide range of experimental results with a small number of biophysically interpretable parameters. The model allows to investigate the susceptibility of STDP to arbitrary time courses of pre- and postsynaptic activities, i.e. its nonlinear filter properties. We demonstrate this for the simple example of small periodic modulations of pre- and postsynaptic firing rates for which our model can be solved. It predicts synaptic strengthening for synchronous rate modulations. For low baseline rates modifications are dominant in the theta frequency range, a result which underlines the well known relevance of theta activities in hippocampus and cortex for learning. We also find emphasis of low baseline spike rates and suppression for high baseline rates. The latter suggests a mechanism of network activity regulation inherent in STDP. Furthermore, our novel formulation provides a general framework for investigating the joint dynamics of neuronal activity and the CD of STDP in both spike-based as well as rate-based neuronal network models.
Joscha Tapani Schmiedt, Christian Albers, Klaus Pawelzik
NIPS1