Hippocampus as an Echo State Network

Sammanfattning: The Hippocampus is a brain region responsible for learning, memoryand spatial navigation. In that, the interactions between the CA3 andCA1 subregions have been the most studied due to the interesting dynamicsbetween the two regions. The excitatory auto-associative connectionsin the CA3 and the lack thereof in CA1 can be modelled asan Echo State Network (ESN) with the reservoir and readout approximatingCA3 and CA1 respectively. However, CA1 possesses somedegree of recurrent connections between the excitatory and the inhibitoryneurons, thereby posing an important problem from the computationaland Machine Learning perspective. The aim of this thesisis to introduce the recurrent connections in the readout and exploringits implications. By doing so, we observed that the recurrent connectionsperform a dynamic mapping of the readout output that makesthe system susceptible to noise, thereby affecting the performance.However, we also observed that by controlling certain parameters, themodel with the recurrent readout connections could perform comparablywith the basic ESN.