Observed Stent's anti-Hebbian postulate on dynamic stochastic computational synapses

@article{Fernando2011ObservedSA,
  title={Observed Stent's anti-Hebbian postulate on dynamic stochastic computational synapses},
  author={Subha Danushika Fernando and Koichi Yamada and Ashu Marasinghe},
  journal={The 2011 International Joint Conference on Neural Networks},
  year={2011},
  pages={1336-1343}
}
Unconstrained growth of synaptic connectivity and the lack of references to synaptic depression in Hebb's postulate has diminished its value as a learning algorithm. While spike timing dependent plasticity and other synaptic scaling mechanisms have been studying the possibility of regulating synaptic activity on neuronal level, we studied the possibility of regulating the synaptic activity of Hebb's neurons on dynamic stochastic computational synapses. The study was conducted on fully connected… 

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References

SHOWING 1-10 OF 19 REFERENCES

A physiological mechanism for Hebb's postulate of learning.

  • G. Stent
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1973
TLDR
The physiological mechanism proposed here for this process posits that at synapses acting according to Hebb's postulate, the receptors for the neurotransmitter are eliminated from the post Synaptic membrane by the transient reversals of the sign of membrane polarization that occur during action potential impulses in the postsynaptic cell.

Dynamic Stochastic Synapses as Computational Units

TLDR
This work considers a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking neurons) and investigates the consequences of the model for computing with individual spikes.

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity

TLDR
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.

The Role of Constraints in Hebbian Learning

TLDR
These results may be used to understand constraints both over output cells and over input cells, and a variety of rules that can implement constrained dynamics are discussed.

A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory.

  • J. Lisman
  • Biology, Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 1989
TLDR
Modeling of the group of Ca2+/calmodulin-dependent protein kinase II molecules contained within a postsynaptic density shows that it can function as an analog computer that can store a synaptic weight and modify it in accord with the Hebb and anti-Hebb learning rules.

Homeostatic plasticity in the developing nervous system

TLDR
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.

The Applicability of Spike Time Dependent Plasticity to Development

TLDR
It is suggested that STDP is not a universal rule, but rather might be masked or phased in, depending on the information available to instruct refinement in different developing circuits, to suggest a more general framework for understanding where it could be playing a role in development.

Synaptic computation

TLDR
The diverse types of synaptic plasticity and the range of timescales over which they operate suggest that synapses have a more active role in information processing.

The probability of neurotransmitter release: variability and feedback control at single synapses

TLDR
This work has demonstrated that synaptic terminals can individually set their neurotransmitter release probability dynamically through local feedback regulation, and this local tuning of transmission has important implications for current models of single-neuron computation.