Hebbian plasticity requires compensatory processes on multiple timescales

By Friedemann Zenke, Wulfram Gerstner. 2017

114 citations recorded by the source corpus.

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Friedemann Zenke
2. Surya Ganguli
3. Wulfram Gerstner
4. Rahaf Aljundi
5. Yoshua Bengio
6. Greg Yang
7. Sam Schoenholz
8. Tinne Tuytelaars

In Cognitive Neuroscience

1. Friedemann Zenke
2. Surya Ganguli
3. Wulfram Gerstner
4. Pierre Yger
5. Andrea Soltoggio
6. Claudia Clopath
7. Matthieu Gilson
8. Nicolas Frémaux

In Electrical and Electronic Engineering

1. Friedemann Zenke
2. Surya Ganguli
3. Wulfram Gerstner
4. Rahaf Aljundi
5. Pierre Yger
6. Andrea Soltoggio
7. Claudia Clopath
8. Matthieu Gilson

Citations in the corpus

• The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Indeed modeling studies that have attempted to use homeostatic plasticity mechanisms to stabilize Hebbian learning [11,30,31 ,32 ,33,34] were typically required to speed up homeostatic plasticity to timescales that are orders of magnitude faster than those observed in experiments (Figure 1).”