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

  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},
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… 

Synaptic redistribution and variability of signal release probability of Hebbian neurons at low-firing frequencies in a dynamic stochastic neural network

Synaptic redistribution has improved the signal transmission for the first few signals in the signal train by continuously increasing and decreasing the number of postsynaptic ‘active-receptors’ and presynaptic ’active-transmitters’ within a short time period.

Spike-timing dependent plasticity with release probability supported to eliminate weight boundaries and to balance the excitation of Hebbian neurons

  • Subha Danushika FernandoKoichi Yamada
  • Computer Science, Biology
    The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems
  • 2012
The results have proven the possibility of balancing the excitation of the neural networks as the modeled network stabilizes its weights distribution for Poisson inputs with frequency less than 40 Hz and excited synapses have resembled the median of the weight distribution into unimodal Gaussian distribution.

Spike-Timing-Dependent Plasticity and Short-Term Plasticity Jointly Control the Excitation of Hebbian Plasticity without Weight Constraints in Neural Networks

According to the results both plasticity mechanisms work together to balance the excitation of the neural network as the neurons stabilized its weights for Poisson inputs with mean firing rates from 10 Hz to 40‬Hz.

Modeling honeybee communication using network of spiking neural networks to simulate nectar reporting behavior

The findings of the research that attempted to mathematically model the cognitive behavior that could arise due to the interaction between honeybees in a colony during forager recruitment process have supported that the proposed mathematical model can sufficiently simulate the unemployed forager’s behavior during recruitment process.



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
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

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

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

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
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

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

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

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

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.