Recurrent Inhibitory Dynamics in the Entorhinal Cortex Support Pattern Separation

Abstract

The entorhinal cortex (EC) provides the major input to the hippocampus (HPC). Numerous computational models on the EC propose that its grid cells serve as a spatial metric, supporting path integration and efficient generalization. However, little is known about how these cells could contribute to episodic memory, which emphasizes episode-specific representations that align with pattern separation. Taking into consideration anatomical specifications of EC inputs to the HPC and computational principles underlying the EC-HPC memory system, we argue that EC layer IIa (EC2a) supports pattern separation and EC layer IIb/III (EC2b/3) supports generalization. Utilizing recurrent inhibition and the nature of single EC2a neurons binding converging inputs from the neocortex (i.e., conjunctive coding), we built a biologically-based neural network model of the EC-HPC system for episodic memory. By examining how EC2a transformed its cortical inputs and output them to the trisynaptic pathway (EC2a - Dentate Gyrus - CA3 - CA1), we found that instead of systematically generalizing across similar inputs, recurrent inhibition and conjunctive coding in EC2a neurons support strong pattern separation and increase mnemonic discrimination. Furthermore, lesioning EC2a neurons in the model resembled memory impairments found in people with Alzheimers Disease, suggesting an intertwined relationship between memory and the majority of pure grid cells in the EC. On the other hand, the topographically organized monosynaptic pathway (EC2b/3 - CA1) is computationally more suitable for efficient factorization and generalization. This model provides novel anatomically-based predictions regarding the computational roles of EC cells in pattern separation and generalization, which together form a critical computational framework for both episodic memory and spatial navigation.