It has been shown that brain-like self-repair can arise from the interactions between neurons and astrocytes where endocannabinoids are synthesized and released from active neurons. This retrograde messenger feeds back to local synapses directly and indirectly to distant synapses via astrocytes. This direct/indirect feedback of the endocannabinoid retrograde messenger results in the modulation of the probability of release (PR) at synaptic sites. When synapses fail, there is a corresponding falloff in the firing activity of the associated neurons, and hence the strength of the direct feedback messenger diminishes. This triggers an increase in PR of healthy synapses, due to the indirect messenger from other active neurons, which is the catalyst for the repair process. In this paper, the repair process is implemented by developing a new learning rule that captures the spike-timing-dependent plasticity and Bienenstock, Cooper, and Munro learning rules. The rule is activated by the increase in PR and results in a potentiation of the weight values, which reestablishes the firing activity of neurons. In addition, this self-repairing mechanism is extended to network-level repair where astrocyte to astrocyte communications are implemented using a linear gap junction model. This facilitates the implementation of an astroglial syncytium involving multiple astrocytes, which relays the indirect feedback messenger to distant neurons: each astrocyte is bidirectionally coupled to neurons. A detailed and comprehensive set of results with analysis is presented demonstrating repair at both cellular and network levels.
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - 6 Jan 2015|
- and Munro (BCM)
- fault tolerance
- neuron models
- probability of release (PR)
- spike-timing-dependent plasticity (STDP)