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Long‐term potentiation (LTP) and Long‐term depression (LTD) are the best understood mechanisms by which biological systems achieve the sculpting of neural pathways necessary for learning and memory. Foundational to the understanding of LTP and LTD is the model of Hebbian learning. Although research has demonstrated that neural networks can be developed and trained in silico to mimic the computational function of neural ensembles, the development of a truly realistic model remains elusive. We construct a simulation of Hebbian learning implementing the Hopfield network model. We then propose an experimental setup that will use the light‐directed electrical stimulation of neurons cultured on silicon wafers in order to gain information on the parameters of synaptic strength and firing frequencies for all neurons in the system simultaneously (determinants of LTP and LTD). This will allow the parallel running of neural networks in the in vivo and in silico setting, allowing statistical comparison between the biological model and computer simulation and additional refinement to create a more biologically relevant computational model.
Keywords: short and long-term potentiation (LTP), short and long-term depression (LTD), neural plasticity, Hebbian learning, Hopfield Network, parallel neural network training, light-directed electrical stimulation (LDES).