MOTIVATION LETTER The Conjunctive Consolidation Threshold model, which I developed as an independent researcher in Lagos, uses ordinary differential equations and Bayesian population dynamics to predict a reduction in reward-memory encoding probability from 0.855 to 0.122. This represents an 85.8 percent reduction in the core mechanism of addiction. The model has been validated through pre-registered hypotheses H1 through H5, and a provisional patent on its core architecture is filed for Q3 2026. My formal training, however, is in pharmacy, not computational neuroscience. The Computational Neuroscience course on Coursera, offered by the University of Washington, directly addresses this gap. The course covers three technical pillars I already use: ODE solvers for dynamical systems, Bayesian inference for parameter estimation, and neural network architectures for modeling learning and memory. My independent work on the CCT model already applies ODE/RK45 integration and Bayesian MCMC sampling via PyMC. The course will formalize my understanding of the theoretical foundations behind these methods, particularly in the context of neural circuits and synaptic plasticity. This matters because my CCT model proposes a tripartite pharmacological intervention at the level of reward-memory consolidation, and a deeper grasp of the underlying neural dynamics will strengthen both the model and my ability to communicate it to neuroscientists. I am applying to this specific training programme because it is structured, self-paced, and directly relevant to my research trajectory. It offers no funding or credential, but it provides a systematic curriculum that I can complete while continuing my independent research and my role as National Product Manager at Synthcare. Completion of the course will also strengthen my MSc applications for an October 2026 start at the Medical University of Graz in Austria, by demonstrating structured learning in computational neuroscience from a recognized university programme. My collaborators include Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. These researchers work at the intersection of computational and experimental neuroscience. This course will help me speak their language with greater precision and depth. SHORT ESSAY: LEARNING GOALS Three specific learning goals drive my application to this course. First, I want to deepen my understanding of neural network models of reinforcement learning. My CCT model currently uses a Bayesian framework to predict encoding probability reduction, but I have not yet modeled the underlying neural population dynamics with spiking networks. The course covers rate-based and spiking neural network models, which I will apply to simulate the effect of CCT-targeted pharmacological agents on dopaminergic and glutamatergic circuits. This will allow me to move from a purely statistical model to a biophysically grounded one. Second, I need formal training in information theory and neural coding. My TOPOLOGIX platform uses topological data analysis and persistent homology to analyze drug-protein interactions, but I have not applied these methods to neural spike train data. The course includes lectures on neural coding and decoding, which will enable me to extend TOPOLOGIX to analyze neural recordings from addiction models. This is directly relevant to my GATE platform for BCI neural-stimulation safety evaluation. Third, I want to strengthen my ability to design computational experiments that test specific hypotheses about memory consolidation. My pre-registered hypotheses H1 through H5 for the CCT model were confirmed, but the experimental architecture was designed from a pharmacological perspective. The course will teach me how to design neural-level experiments that can be simulated computationally, which will improve the next iteration of my clinical trial architecture described in my Zenodo preprint. SHORT ESSAY: RELEVANCE TO INDEPENDENT RESEARCH My independent research program centers on the Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for preventing reward-memory encoding in addiction. The model has been specified mathematically in a preprint on OSF, validated with Bayesian population dynamics in a Zenodo preprint, and is currently under review at Neuroscience and Biobehavioral Reviews. A co-authored paper on alcohol addiction mechanisms is under review at Alcohol, an Elsevier journal. The Computational Neuroscience course is directly relevant because it covers the three methodological pillars of my model: dynamical systems theory for the ODE-based encoding probability equations, Bayesian inference for the MCMC validation of population-level effects, and neural network models for the circuit-level mechanisms of memory consolidation. The course will allow me to formalize my self-taught expertise in these areas, which currently relies on practical implementation in Python, scipy, numpy, and PyMC without formal theoretical grounding. Nigeria has no dedicated computational neuroscience training programme. As an independent researcher in Lagos, I have built my skills through online resources, collaboration with international researchers, and hands-on implementation. This course from the University of Washington provides a structured curriculum that I cannot access locally. It will also serve as evidence of formal training when I apply for MSc programmes in Austria starting October 2026. The course is free to audit and self-paced, which fits my current situation as a full-time National Product Manager at Synthcare and an independent researcher. I will complete all quizzes and assignments to earn the certificate, which I will include in my MSc applications and in my ORCID profile. CHECKLIST - [ ] Enroll in the Computational Neuroscience course on Coursera - [ ] Complete all video lectures and reading materials - [ ] Pass all quizzes and programming assignments - [ ] Earn the course certificate - [ ] Add certificate to ORCID profile (0009-0001-9272-6735) - [ ] Add certificate to LinkedIn profile - [ ] Reference course completion in MSc applications for MUG/Graz, Austria - [ ] Apply course concepts to next iteration of CCT model simulations - [ ] Document course completion on zyco.org independent research page EDITOR NOTES - Eligibility: The course is open enrollment with no restrictions, so no eligibility risk exists. Confirm that the University of Washington Coursera course is the correct provider, as the URL in the prompt points to a generic Coursera page without specifying the university. The prompt says "Provider: " with a blank field. Verify the actual provider and update the letter accordingly. - Fact verification: Confirm that the course covers ODE solvers, Bayesian inference, and neural network models. The standard University of Washington Computational Neuroscience course on Coursera taught by Rajesh Rao and Adrienne Fairhall does cover these topics, but verify the current syllabus before submitting. - Personal detail gap: The profile does not specify why Eniola chose the Medical University of Graz specifically. If this detail is needed for any application component, Eniola should insert a brief explanation of the fit with his research interests or potential supervisors. - Timeline: The course is self-paced, but Eniola should set a target completion date before his MSc applications are due (likely late 2025 or early 2026 for an October 2026 start). Include this timeline in any planning documents. - Certificate cost: The course is free to audit, but a verified certificate costs money. Eniola should confirm whether he needs the paid certificate for his MSc applications or whether the audit completion record is sufficient.