Publication Date
1-1-2024
Journal
Biological Psychiatry Global Open Science
Abstract
Background
Learning requires the activation of protein kinases with distinct temporal dynamics. In Aplysia, nonassociative learning can be enhanced by a computationally designed learning protocol with intertrial intervals (ITIs) that maximize the interaction between fast-activated PKA (protein kinase A) and slow-activated ERK (extracellular signal-regulated kinase). Whether a similar strategy can enhance associative learning in mammals is unknown.
Methods
We simulated 1000 training protocols with varying ITIs to predict an optimal protocol based on empirical data for PKA and ERK dynamics in rat hippocampus. Adult male rats received the optimal protocol or control protocols in auditory fear conditioning and fear extinction experiments. Immunohistochemistry was performed to evaluate pCREB (phosphorylated cAMP response element binding)\protein levels in brain regions that have been implicated in fear acquisition.
Results
Rats exposed to the optimal conditioning protocol with irregular ITIs exhibited impaired extinction memory acquisition within the session using a standard footshock intensity, and stronger fear memory retrieval and spontaneous recovery with a weaker footshock intensity, compared with rats that received massed or spaced conditioning protocols with fixed ITIs. Rats exposed to the optimal extinction protocol displayed improved extinction of contextual fear memory and reduced spontaneous recovery compared with rats that received standard extinction protocols. Moreover, the optimal conditioning protocol increased pCREB levels in the dentate gyrus of the dorsal hippocampus, suggesting enhanced induction of long-term potentiation.
Conclusions
These findings demonstrate that a computational model–driven behavioral intervention can enhance associative learning in mammals and may provide insight into strategies to improve cognition in humans.
Keywords
Associative memory, Computational model, Fear conditioning, Fear extinction, Long-term potentiation, Spaced learning