This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

This lesson reviews theoretical and mathematical descriptions of correlated spike trains.

This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).

This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.

In this lesson, you will learn about the intrinsic plasticity of single neurons.

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

This lesson briefly wraps up the course on Computational Modeling of Neuronal Plasticity.

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 talks discusses data sharing in the context of dementia. It explains why data sharing in dementia is important, how data is usually shared in the field and illustrates two examples: the Netherlands Consortium Dementia cohorts and the European Platform for Neurodegenerative Diseases.

The Medical Informatics Platform (MIP) Dementia had been installed in several memory clinics across Europe allowing them to federate their real-world databases. Research open access databases had also been integrated such as ADNI (Alzheimer’s Dementia Neuroimaging Initiative), reaching a cumulative case load of more than 5,000 patients (major cognitive disorder due to Alzheimer’s disease, other major cognitive disorder, minor cognitive disorder, controls). The statistic and machine learning tools implemented in the MIP allowed researchers to conduct easily federated analyses among Italian memory clinics (Redolfi et al. 2020) and also across borders between the French (Lille), the Swiss (Lausanne) and the Italian (Brescia) datasets.

The number of patients with dementia is estimated to increase given the aging population. This will lead to a number of challenges in the future in terms of diagnosis and care for patients with dementia. To meet these needs such as early diagnsosis and development of prognostic biomarkers, large datasets, such as the federated datasets on dementia. The EAN Dementia and cognitive disorders scientific panel can play an important role as coordinator and connecting panel members who wish to participate in e.g. consortia.