Topic: engram formation

Synaptic plasticity is key to the brain’s life-long learning capabilities, but the link between changes at individual synapses and emerging network properties remains elusive. Recent meta-learning approaches have started automating the search through the space of possible plasticity rules for plausible candidates. However, these approaches were either limited to narrow sets of rules, or did not investigate any network function beyond stability. Here, we identify thousands of co-active excitatory(E)-to-E, E-to-inhibitory(I), I-to-E and I-to-I Hebbian rules that elicit learning in large recurren

Synaptic plasticity is the cornerstone of learning and memory. However, the mechanistic link between individual synaptic changes and emerging network functions remains elusive. Here, we apply a numerical method for meta-learning plasticity rules---filter Simulation-Based Inference (fSBI)---to in vivo data. We identify thousands of co-active excitatory(E)-to-E, E-to-inhibitory(I), I-to-E and I-to-I rules that can drive learning and memory functions in large recurrent spiking networks. We begin with large search spaces of flexibly-parameterized co-active plasticity rules. We then use fSBI to fil

Understanding the neural mechanisms underlying memory formation and retrieval is fundamental to unraveling the pathophysiology of Alzheimer's disease (AD). Inhibitory interneurons have fundamental roles in shaping neural circuits and synaptic plasticity within the hippocampus, a brain region critical for memory encoding and consolidation. However, the specific contribution of inhibitory interneurons to the formation and maintenance of engrams, the neural representations of memories, remains poorly understood, particularly in the context of AD.To determine neurons participating in the engram fo