From Hebb Rules to Spike-Timing-Dependent Plasticity: A Personal Account

By Wulfram Gerstner. 2010

8 citations recorded by the source corpus.

to translate Hopfield models into spiking networks where the intrinsic neuronal time scale would be clearly defined (Gerstner, 1991; Gerstner and van Hemmen, 1992). After my return to Munich, I wondered whether my spiking networks would be able to learn spatio-temporal spike patterns. In analogy to earlier work on sequence learning (Herz et al., 1991), I realized that this would only be possible if I used a Hebbian learn-ing rule which reflects the causality principle implicit in Hebb’s formulation: the timing must be such that synapses that contrib-ute to firing the postsynaptic neuron are maximally strengthened. Hence long-term potentiation (LTP) must be maximal if the spike arrives at the synapse 1 or 2 ms

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Andrea Soltoggio
2. Kenneth O. Stanley
3. Jeff Clune
4. Jean-Baptiste Mouret
5. Paul Tonelli
6. Sebastian Risi
7. J. Gustafsson
8. Roby Velez

In Electrical and Electronic Engineering

1. Andrea Soltoggio
2. Jeff Clune
3. Kenneth O. Stanley
4. Roby Velez

Citations in the corpus

• Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, positive): “This type of computation attempts to capture the effect of a presynaptic neuron on the postsynaptic neuron, i.e. the causal pre-before-post situation (Gerstner, 2010), considered to be the link between the Hebb’s postulate and STDP (Kempter et al, 1999).”
• Spike-Timing-Dependent Plasticity: A Comprehensive Overview (2012, unknown): “Sjöström and Gerstner (2010) next define and briefly outline the STDP concept.”