Cell Assembly Formation and Retrieval in a Biologically Constrained CA3 Network

Abstract

When we reminisce about our past, the evoked memory typically consists of specifics including where and when an event occurred, what happened, and how we felt about it. Our brain remembers these multi-faceted experiences by co-activating groups of neurons, so-called cell assemblies. A leading neuroscience theory posits that hippocampal area Cornu Ammonis 3 (CA3) binds together details to form an episodic memory via auto-association (cell assembly formation), and the memory is later recalled through pattern completion (cell assembly retrieval). However, the exact mechanisms of how circuits of diverse neurons communicating via spikes and complex synaptic signals implement these processes are still unknown. To address this open problem, I created a full-scale spiking neural network (SNN) simulation of the mouse CA3 that integrated data-driven properties from the Hippocampome.org open-access knowledge base. I first simulated the SNN without prolonged stimulation to investigate the network dynamics before memory storage. The resultant network activity was stable and rhythmic in the beta band (12-30 Hz), consistent with empirical evidence when awake mice are not performing a memory task. Building on this work, I demonstrated the SNN was capable of robust auto-association and pattern completion via cell assemblies. The assemblies could successfully and systematically retrieve patterns from heavily incomplete or corrupted cue presentations. A broad range of assembly sizes, consistent with theory and experiments in rodents and humans, supported strong auto-association and pattern completion. The CA3 SNN performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These results provided computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain. Furthermore, previous studies highlighted the importance of acetylcholine as a neuromodulator in memory formation and retrieval. Therefore, I assessed the dynamics of acetylcholine in the medial septum, the major source of cholinergic modulation for the hippocampal formation, in mice foraging for food in an open environment. Machine learning classification of the movement of mice revealed four distinct behaviors of exploratory running and walking, grooming, and rearing. Linear regression further demonstrated an increase of cholinergic activity in the MS during rearing, when a mouse scans its surroundings on its hindlimbs from an elevated perspective, suggesting a role in encoding information for spatial memories. Taken together, the results of this thesis demonstrate the capability of a biologically realistic SNN of the mouse CA3 to encode and retrieve memories, as well as the utility of deep learning in uncovering correlations between neuromodulation and behavior. These advances afford the community complementary and synergistic opportunities to better understand episodic memory in future studies