The goal of computational modeling in behavioral and psychological science is using mathematical models to characterize behavioral (or neural) data. Over the past decade, this practice has revolutionized social psychological science (and neuroscience) by allowing researchers to formalize theories as constrained mathematical models and test specific hypotheses to explain unobservable aspects of complex social cognitive processes and behaviors. This course is composed of 4 modules in the format of Jupyter Notebooks. This course comprises lecture-based, discussion-based, and lab-based instruction. At least one-third of class sessions will be hands-on. We will discuss relevant book chapters and journal articles, and work with simulated and real data using the Python programming language (no prior programming experience necessary) as we survey some selected areas of research at the intersection of computational modeling and social behavior. These selected topics will span a broad set of social psychological abilities including (1) learning from and for others, (2) learning about others, and (3) social influence on decision-making and mental states. Rhoads, S. A. & Gan, L. (2022). Computational models of human social behavior and neuroscience - An open educational course and Jupyter Book to advance computational training.  ​​​Journal of Open Source Education, 5(47), 146. https://doi.org/10.21105/jose.00146

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models. 

Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation. 

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time. 

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks.