UC San Diego

Pattern recognition is a core facet of learning any concept, whether it be a literal pattern in an image or a predictable stimulus-response pattern as in operant conditioning. From the molecular mechanisms up to the behavioral consequences, patterns can be embedded to allow humans to learn from past experiences to better predict future outcomes. A key player in this embedding process is the spike-timing-dependent plasticity (STDP) of the connections between neurons within the brain. STDP is theorized to leverage the spiking activity of neurons resulting from various stimuli according to the initial rule proposed by Donald Hebb: neurons that fire together wire together. However, the actual process by which STDP learning takes place remains disputed, especially in cases such as inhibition, where neurons do not actually support subsequent firing. This dissertation tackles a few of these issues from multiple angles: (1) An evaluation of many different proposed STDP rules and how they have been justified experimentally and employed computationally. (2) The creation of a computational model that highlights the value of the STDP-mediated embedding of a stimulus into the architecture of a spiking neural network in a manner that could resemble brain embeddings. (3) A proposed approach to teaching students by taking advantage of thenatural tendency of the brain to identify patterns and use them to create connections in new contexts. Here, we employ a biologically-inspired spiking neural network model with edge latencies, refractory periods, and modern STDP rules to demonstrate the potential of the brain to embed information within its architecture via synaptic weights. These embeddings may be quite relevant in the brain where the astrocyte syncytium may be able to access them to communicate local learning information on a more global scale.