Memory

Helfer, P., & Shultz, T. R. (2019). A computational model of systems memory reconsolidation and extinction. Hippocampus, 30, 659-677. In the mammalian brain, newly acquired memories depend on the hippocampus for maintenance and recall, but over time these functions are taken over by the neocortex through a process called systems memory consolidation. However, reactivation of a well-consolidated memory can return it to a hippocampus-dependent state. This is normally followed by a restoration of hippocampus independence, a phenomenon known as systems memory reconsolidation. The neural mechanisms underlying systems memory consolidation and reconsolidation are poorly understood. Here, we propose a neural model based on well-documented mechanisms of synaptic plasticity and stability and describe a computational implementation that demonstrates the model’s ability to account for a number of findings from the systems consolidation and reconsolidation literature. pdf
Helfer, P., & Shultz, T. R. (2018). Coupled feedback loops maintain synaptic long-term potentiation: A computational model of PKMzeta synthesis and AMPA receptor trafficking. PLOS Computational Biology, 14(5):e1006147. In long-term potentiation (LTP), one of the most studied types of neural plasticity, synaptic strength is persistently increased in response to stimulation. Although a number of different proteins have been implicated in the sub-cellular molecular processes underlying induction and maintenance of LTP, the precise mechanisms remain unknown. A particular challenge is to demonstrate that a proposed molecular mechanism can provide the level of stability needed to maintain memories for months or longer, in spite of the fact that many of the participating molecules have much shorter life spans. Here we present a computational model that combines simulations of several biochemical reactions that have been suggested in the LTP literature and show that the resulting system does exhibit the required stability. At the core of the model are two interlinked feedback loops of molecular reactions, one involving the atypical protein kinase PKMζ and its messenger RNA, the other involving PKMζ and GluA2-containing AMPA receptors. We demonstrate that robust bistability - stable equilibria both in the synapse's potentiated and unpotentiated states - can arise from a set of simple molecular reactions. The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions. pdf
Helfer, P. & Shultz, T. R. (3 March 2017). A computational model of systems memory reconsolidation and extinction. - In the mammalian brain, newly acquired memories are dependent on the hippocampus for maintenance and recall, but over time these functions are taken over by the neocortex through a process called memory consolidation. Whereas recent memories are likely to be disrupted in the event of hippocampal damage, older memories are less vulnerable. However, if a consolidated memory is reactivated by a reminder, it can temporarily return to a hippocampus-dependent state. This is known as memory reconsolidation. We present an artificial neural-network model that captures memory consolidation and reconsolidation, as well as the related phenomena of memory extinction, spontaneous recovery and trace dominance. The model provides a novel explanation of trace dominance, the competition between reconsolidation and extinction, from which we derive two predictions that could be tested in future experiments. https://arxiv.org/abs/1703.01357
Helfer, P., Shultz, T. R., Hardt, O., & Nader, K. (2013). A computational model of systems memory reconsolidation. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 2512-2517). Austin, TX: Cognitive Science Society. - The first computational model of systems-level memory reconsolidation. pdf