UPDATING THE BRAIN’S MEMORY BASE: COMPUTATIONAL PERSPECTIVE
Partners: IN, IFISC-UIB
Fecha de inicio: 1 de Septiembre de 2022
Fecha de final: 31 de Agosto de 2025
In this project we bring together systems neuroscience (Subproject 1, IP: Santiago Canals) and computational perspectives (Subproject 2, IP: Claudio Mirasso), to investigate how hippocampal (HC) dependent episodic memories are updated to keep track of the ongoing experience. Our aim is to formalize and support experimentally a novel theoretical framework in which we propose that memory updating is under the control of neuronal synchronization in brain-wide networks orchestrated by the dentate gyrus (DG). Our theoretical framework states that (1) the DG is a critical node coordinating communication in memory brain networks, (2) the key mechanism for coordination is the balance between synaptic excitation and inhibition in the DG, and (3) the level of inhibition in the DG thus defines whether new information is assimilated in the memory base. We first review the evidence provided in recent years by our research groups and others, in support of the proposed theoretical framework. In doing so, we will see how many pieces of the puzzle of how the memory base is updated come together to suggest that synaptic plasticity and a fine-tuning of the E-I balance in the DG are responsible for coordinating neuronal ensembles distributed across brain regions. The function is twofold, to synchronize the neuronal populations that convey the new information when the memory has to be updated, or to desynchronize them to prevent continuous overwriting when environmental contingencies remain stable. As we will explain in the proposal, our ideas translate into concrete and testable propositions to validate (or deny) the above theoretical framework.
Apart from academic merit, if our theory proves true, the gained mechanistic understanding could have a transformative impact on how we handle memory problems in the population, as well as how we devise neuro-inspired Artificial Intelligence (AI) systems. In our last (third) objective, we will explore two applications that can be derived from the basic results of this project. On the one hand, a biomedical application in the context of addiction to substances of abuse, such as alcohol, and on the other, a technological application aimed at improving the efficiency of artificial neural networks by involving neurobiological mechanisms.
The coordinated project is organized with two tightly integrated subprojects that will be explained together in the grant application, indicating which tasks will be developed more independently (but still coordinated), and which ones as an integrated team.