The neural mechanisms involved in regression are currently unknown. One important clue is the high incidence of epilepsy or epileptiform activity found in children with ASD, although the evidence for a link between epilepsies and regression is inconclusive.
Objectives: to develop a simple neural-network model of autistic regression at the synaptic and local network level.
Methods: We rely upon an existing recurrent network model with short term synaptic depression, which implements a memory completion network. The model produces a sharply tuned response from a partial or noisy input and switches rapidly into a bursting regime depending, for instance, on the strength of the external input. We explored the regressive effect of the bursting regime as a model for autistic regression.
Results: The model showed degraded memory completion during the bursting regime. On a short term, this resulted in a partial loss of the "weaker" memories which were fully recovered when shifting out of the bursting regime. However, long term bursting is expected to produce long-term damage due to Hebbian synaptic changes that can only be recovered by retraining. The model thus demonstrates that a reversible short-term regression and a long-term regression could have the same origin.
Conclusions: Autistic regression could be linked to the frequently observed spiking or epileptic activity via a recurrent network model that enters a bursting regime. The model demonstrates several features that can inspire further investigation.
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