Excitability changes that complement Hebbian learning

Janowitz, Maia K.; Rossum, Mark C. W. van

doi:10.1080/09548980500286797

Cited by 13 publications

(11 citation statements)

References 24 publications

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“…Our analysis of the interaction of Hebbian learning with the proposed IP model is a small step in this direction. In a similar vein, Janowitz and van Rossum (2006) have explored the role of a fast nonhomeostatic IP mechanism for trace conditioning. So far, however, we have seen only a small glimpse of this terra incognita.…”

Section: Future Workmentioning

confidence: 98%

Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

2007

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We propose a model of intrinsic plasticity for a continuous activation model neuron based on information theory. We then show how intrinsic and synaptic plasticity mechanisms interact and allow the neuron to discover heavy-tailed directions in the input. We also demonstrate that intrinsic plasticity may be an alternative explanation for the sliding threshold postulated in the BCM theory of synaptic plasticity. We present a theoretical analysis of the interaction of intrinsic plasticity with different Hebbian learning rules for the case of clustered inputs. Finally, we perform experiments on the "bars" problem, a popular nonlinear independent component analysis problem.

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Section: Future Workmentioning

confidence: 98%

Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

2007

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“…In an artificial neural network containing excitability changes and Hebbian learning rules, the excitability of a neuron may serve as a “label” to identify it as recently active [53]. This mechanism could bridge the gaps between temporally separated stimuli and help explain aspects of trace conditioning (see above) [54].…”

Section: Changes In Excitability Produced By Learningmentioning

confidence: 99%

More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory

Mozzachiodi

Byrne

2010

Trends in Neurosciences

231

189

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Decades of research on the cellular mechanisms of memory have led to the widely-held view that memories are stored as modifications of synaptic strength. These changes involve presynaptic processes, such as direct modulation of the release machinery, or postsynaptic processes, such as modulation of receptor properties. Parallel studies have revealed that memories may also be stored by nonsynaptic processes, such as modulation of voltage-dependent membrane conductances, which are expressed as changes in neuronal excitability. Although in some cases nonsynaptic changes may function as part of the engram itself, they may also serve as mechanisms through which a neural circuit is set to a permissive state to facilitate synaptic modifications that are necessary for memory storage.

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“…Including multiple time scales in an attempt to more accurately capture the wide variety of molecular processes involved in memory has also been argued for in previous models (Fusi et al, 2005; Clopath et al, 2008). Another model hypothesized a memory scheme whereby LTP and LTP-IE could interact (Janowitz and van Rossum, 2006), but updates were asynchronous, which is difficult to reconcile with the coordinated interdependence known from biology (Daoudal and Debanne, 2003) and shown here for spike-based BCPNN.…”

Section: Discussionmentioning

confidence: 73%

Synaptic and nonsynaptic plasticity approximating probabilistic inference

Tully

Hennig

Lansner

2014

Front. Synaptic Neurosci.

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Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

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Excitability changes that complement Hebbian learning

Cited by 13 publications

References 24 publications

Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory

Synaptic and nonsynaptic plasticity approximating probabilistic inference

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