scispace - formally typeset
Search or ask a question
Topic

Hebbian theory

About: Hebbian theory is a research topic. Over the lifetime, 3139 publications have been published within this topic receiving 96216 citations. The topic is also known as: Hebbian theory & Hebb's rule.


Papers
More filters
Journal ArticleDOI
TL;DR: In modeling studies, it is found that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing.
Abstract: Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.

2,605 citations

Journal ArticleDOI
TL;DR: Evidence is discussed from a number of systems that homeostatic synaptic plasticity is crucial for processes ranging from memory storage to activity-dependent development, and how these processes maintain stable activity states in the face of destabilizing forces is discussed.
Abstract: Activity has an important role in refining synaptic connectivity during development, in part through 'Hebbian' mechanisms such as long-term potentiation and long-term depression. However, Hebbian plasticity is probably insufficient to explain activity-dependent development because it tends to destabilize the activity of neural circuits. How can complex circuits maintain stable activity states in the face of such destabilizing forces? An idea that is emerging from recent work is that average neuronal activity levels are maintained by a set of homeostatic plasticity mechanisms that dynamically adjust synaptic strengths in the correct direction to promote stability. Here we discuss evidence from a number of systems that homeostatic synaptic plasticity is crucial for processes ranging from memory storage to activity-dependent development.

2,315 citations

Journal ArticleDOI
TL;DR: This work reviews three Hebbian forms of plasticity—synaptic scaling, spike-timing dependent plasticity and synaptic redistribution—and discusses their functional implications.
Abstract: Synaptic plasticity provides the basis for most models of learning, memory and development in neural circuits. To generate realistic results, synapse-specific Hebbian forms of plasticity, such as long-term potentiation and depression, must be augmented by global processes that regulate overall levels of neuronal and network activity. Regulatory processes are often as important as the more intensively studied Hebbian processes in determining the consequences of synaptic plasticity for network function. Recent experimental results suggest several novel mechanisms for regulating levels of activity in conjunction with Hebbian synaptic modification. We review three of them-synaptic scaling, spike-timing dependent plasticity and synaptic redistribution-and discuss their functional implications.

2,118 citations

Journal ArticleDOI
TL;DR: The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an emphasis on the work that attempts to unite both fields, to highlight the gaps in the understanding of synaptic and cellular mechanisms underlying cortical representational plasticity.
Abstract: It has been clear for almost two decades that cortical representations in adult animals are not fixed entities, but rather, are dynamic and are continuously modified by experience. The cortex can preferentially allocate area to represent the particular peripheral input sources that are proportionally most used. Alterations in cortical representations appear to underlie learning tasks dependent on the use of the behaviorally important peripheral inputs that they represent. The rules governing this cortical representational plasticity following manipulations of inputs, including learning, are increasingly well understood. In parallel with developments in the field of cortical map plasticity, studies of synaptic plasticity have characterized specific elementary forms of plasticity, including associative long-term potentiation and long-term depression of excitatory postsynaptic potentials. Investigators have made many important strides toward understanding the molecular underpinnings of these fundamental plasticity processes and toward defining the learning rules that govern their induction. The fields of cortical synaptic plasticity and cortical map plasticity have been implicitly linked by the hypothesis that synaptic plasticity underlies cortical map reorganization. Recent experimental and theoretical work has provided increasingly stronger support for this hypothesis. The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an emphasis on the work that attempts to unite both fields. A second objective is to highlight the gaps in our understanding of synaptic and cellular mechanisms underlying cortical representational plasticity.

2,051 citations

Journal ArticleDOI
TL;DR: A theoretical framework is developed that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations and shows that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
Abstract: We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.

1,920 citations


Network Information
Related Topics (5)
Artificial neural network
207K papers, 4.5M citations
82% related
Synaptic plasticity
19.3K papers, 1.3M citations
81% related
Robustness (computer science)
94.7K papers, 1.6M citations
77% related
Inference
36.8K papers, 1.3M citations
77% related
Hippocampal formation
30.6K papers, 1.7M citations
76% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202393
2022236
2021116
2020142
2019121
2018130