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The temporal paradox of Hebbian learning and

homeostatic plasticity

1 2 1

Friedemann Zenke , Wulfram Gerstner and Surya Ganguli

Hebbian plasticity, a synaptic mechanism which detects and fundamental ingredients required to solve these pro-

amplifies co-activity between neurons, is considered a key blems are stabilization [3], which prevents runaway

ingredient underlying learning and memory in the brain. neural activity, and competition [4 ,5,6 ], in which

However, Hebbian plasticity alone is unstable, leading to the strengthening of a may come at the ex-

runaway neuronal activity, and therefore requires stabilization pense of the weakening of others.

by additional compensatory processes. Traditionally, a

diversity of homeostatic plasticity phenomena found in neural

In theoretical models, competition and stability are often

circuits is thought to play this role. However, recent modelling

achieved by augmenting Hebbian plasticity with addi-

work suggests that the slow evolution of homeostatic plasticity,

tional constraints [3,5,7 ]. Such constraints are typically

as observed in experiments, is insufficient to prevent

implemented by imposing upper limits on individual

instabilities originating from Hebbian plasticity. To remedy this

synaptic strengths, and by enforcing some constraint

situation, we suggest that homeostatic plasticity is

on biophysical variables, for example, the total synaptic

complemented by additional rapid compensatory processes,

strength or average neuronal activity [6 ,7 ,8–12]. In

which rapidly stabilize neuronal activity on short timescales.

neurobiology, forms of plasticity exist which seemingly

Addresses enforce such limits or constraints through synaptic scal-

1

Department of Applied Physics, Stanford University, Stanford, CA ing in response to firing rate perturbations [13,14], or

94305, USA

2 through stabilizing adjustments of the properties of plas-

Brain Mind Institute, School of Life Sciences and School of Computer

ticity in response to the recent synaptic history, a phe-

and Communication Sciences, Ecole Polytechnique Fe´ de´ rale de

Lausanne, EPFL, CH-1015 Lausanne, Switzerland nomenon known as homeostatic

[6 ,11,15,16]. Overall, synaptic scaling and metaplasti-

Corresponding author: Zenke, Friedemann ([email protected])

city, as special cases of homeostatic mechanisms that

operate over diverse spatiotemporal scales across neuro-

biology [17–22], are considered key ingredients that

Current Opinion in Neurobiology 2017, 43:166-176

contribute both stability and competition to Hebbian

This review comes from a themed issue on Neurobiology of learning

plasticity by directly affecting the fate of synaptic and plasticity

strength.

Edited by Leslie Griffith and Tim Vogels

The defining characteristic of homeostatic plasticity is

that it drives synaptic strengths so as to ensure a homeo-

http://dx.doi.org/10.1016/j.conb.2017.03.015 static set point [23,24 ], such as a specific neuronal firing

0959-4388/# 2017 Elsevier Ltd. All rights reserved rate or membrane potential. However, it is important that

this constraint is implemented only on average, over long

timescales, thereby allowing neuronal activity to fluctuate

on shorter timescales, so that these neuronal activity

fluctuations, which drive learning through Hebbian plas-

ticity, can indeed reflect the structure of ongoing experi-

Introduction ence. This requisite separation of timescales is indeed

More than half a century ago, Donald Hebb [1] laid observed exp