A spiking-network cognitive architecture inspired by the hippocampus

We present a new building block for cognitive machines: a model called NeuroNavigator, inspired by theories of the hippocampus. The model is designed to fit both biological plausibility and constraints of new-generation neuromorphic hardware. Its functions may range from spatial navigation to episodic memory retrieval. The goal of the present study is to implement and test previously designed cognitive functionality within a highly scalable spiking framework, and to compare the resulting model dynamics with available rodent data beyond the similarity explicitly introduced by the definition of NeuroNavigator. The core of the architecture includes three layers (DG, CA3 and CA1) of spiking neurons with noisy synapses and spike-time-dependent synaptic plasticity (STDP). The model is applied to an object retrieval paradigm (involving spatial navigation) in a virtual environment. The topology and metrics of the environment are not available a priori to the agent and need to be learned by exploration. The goal in each trial is set arbitrarily as any one of the previously seen objects or features. In order to navigate toward the goal, the agent needs to “imagine” available moves at the current location and select one of them, using the acquired spatial knowledge. This process controlled by NeuroNavigator is repeated until the goal is reached. Overall, the simulation results show extended similarity with rodent data, as well as robustness and scalability of the solution based on a biologically-inspired network of spiking neurons and STDP synapses.