Pattern separation is the neuronal computation posited to support our ability to store similar experiences as distinct memory traces. The dentate gyrus (DG) of the hippocampus is generally thought to perform this process, transforming similar cortical input patterns into dissimilar output patterns before they are transferred and stored in downstream hippocampal networks such as CA3. Despite the centrality of this 30-year-old hypothesis to most theories of episodic memory, whether the DG network per se reduces the overlap between similar inputs, and how it performs this computation, has remained a mystery. My doctoral work aims at rigorously assessing different temporal forms of pattern separation by the DG circuitry. Additionally, because the ability of the DG to gate cortical excitation is thought to fail in temporal lobe epilepsy (TLE), leading to seizures, and because TLE patients also suffer from memory impairments that are poorly understood, I investigated the relationship between TLE and pattern separation in the DG.
Using the ability to directly control inputs and measure outputs afforded by slice electrophysiology (in mice), I first showed that the isolated DG circuitry decorrelates non-simultaneous input spiketrains at the level of single granule cells (GCs), the output neurons of the DG. Pattern decorrelation is larger in GCs than in DG interneurons (fast-spiking interneurons and hilar mossy cells) and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics.
Second, I investigated other forms of pattern separation by considering diverse neural codes that imply different definitions of the similarity between spiketrains. Results demonstrate that the DG can perform pattern separation using multiplexed coding strategies, and that different celltypes can favor pattern separation through different codes. Pharmacological decrease of inhibitory synaptic transmission showed the importance of fast inhibition in regulating these various computations of the DG network.
I finally examined how TLE affects DG pattern separation. Behavioral experiments in humans and mice first determined that TLE is characterized by deficits in mnemonic discrimination, the cognitive function purportedly supported by pattern separation. Electrophysiological experiments in brain slices showed that temporal pattern separation is also decreased in epileptic mice, notably for a subpopulation of GCs exhibiting pathological spiking patterns.
Overall, my dissertation shows that pattern separation can be performed in the DG through different neural codes, and that the impairment of these neuronal computations is correlated with mnemonic confusion, implicating the DG in TLE as a critical nexus for both seizures and cognitive deficits.