Artificial neural network performance based on correlation analysis qualitatively comparable with human performance in behavioral signal detection experiments.

Standard Gaussian signal detection theory (SDT) is a widely used approach to assess the detection performance of living organisms or technical systems without looking at the inner workings of these systems like neural or electronic mechanisms. Nevertheless, a consideration of the inner mechanisms of a system and how they produce observed behaviors should help to better understand the functioning. It might even offer the possibility to demonstrate isolated pattern separation processes directly in the model. To do so, modeling the interaction between the entorhinal cortex (EC) and the hippocampal subnetwork dentate gyrus (DG) via the perforant path reveals the decorrelation network's mode of operation. We show that the ability to do pattern separation is crucial for high-performance pattern recognition, but also for lure discrimination, and depends on the proportionality between input and output network. We elucidate the interplay of the entorhinal cortex and the hippocampal dentate gyrus during pattern separation tasks by providing a new simulation model. Functional memory formation and processing of similar memory content is illuminated from within the system. For the first time orthogonalized spiking patterns are evaluated with signal detection theory methods, and the results are applied to clinically established and novel tests.