Towards a neocortically-inspired ab initio cellular model of associative memory

We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio (simulated) spiking neural networks (SNNs) with cellular resolution and complex network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level measurements are information-preserving and could be used as the basis of an associative memory. The dissertation details the relevant background and results of a series of experiments designed to accomplish this objective. The results provide encouraging empirical evidence that such a model can be used for encoding attractors with multi-sensory inputs and across sensory modalities, which both emphasize the potential of such a model for use as a multi-modal associative memory