MOTIVATION LETTER The Shanahan Undergraduate and Postbaccalaureate Fellowships at the Computational Neuroscience Center, University of Washington, target precisely the transition I occupy: an independent researcher with a completed degree and a validated computational model, seeking formal integration into a doctoral trajectory. My Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, has been specified mathematically, validated with ODE/RK45 and Bayesian MCMC methods, and published as three sole-authored preprints on OSF and Zenodo. The model reduces encoding probability from 0.855 to 0.122, an 85.8% reduction with super-additivity of 12.8 percentage points, and all five pre-registered hypotheses H1 through H5 were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier). I built this work in Lagos, Nigeria, without a host lab, using open-source tools and HPC access through ZYCO. My computational stack includes Python (scipy, numpy, PyMC/MCMC, ODE/RK45), topological data analysis (Ripser, Gudhi), and molecular simulation (GROMACS, AutoDock, AlphaFold). I have also built three platforms: IMPRINT for addiction-liability screening, TOPOLOGIX for TDA-based drug-protein interaction with persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation, released under Apache 2.0. These platforms demonstrate my ability to translate theoretical models into deployable tools. The fellowship would allow me to embed in a lab at the Computational Neuroscience Center, working directly with faculty whose research on reinforcement learning, neural computation, and decision-making aligns with the CCT model's core mechanisms. I have received endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. Gershman provided my arXiv endorsement. A provisional patent on the CCT core architecture is scheduled for Q3 2026. My goal is to enter a PhD program in computational neuroscience or neuropharmacology starting fall 2027. This fellowship would provide the structured mentorship, computational resources, and collaborative environment needed to strengthen my application and extend the CCT model into experimental testbeds. I am a Nigerian national, 29 years old, with a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and licensure as a pharmacist with the Pharmacists Council of Nigeria. I am not yet enrolled in an MSc but am applying for October 2026 start at MUG/Graz, Austria. This fellowship would bridge that gap and position me for a competitive PhD application. RESEARCH STATEMENT My independent research program centers on the Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for preventing reward-memory encoding in addiction. The model posits that reward-memory consolidation requires simultaneous activation above a conjunctive threshold across three neural subsystems: dopaminergic salience, glutamatergic plasticity, and noradrenergic arousal. Pharmacological blockade of any two subsystems reduces encoding probability below the threshold, preventing the formation of addiction-related memories. The CCT model was specified mathematically in my second preprint (OSF 10.17605/OSF.IO/EMY4U), which formalizes the threshold as a nonlinear function of subsystem activation levels. I validated the model using ODE/RK45 numerical integration and Bayesian MCMC parameter estimation with PyMC. The validation dataset comprised 10,000 simulated trials across five parameter regimes. Results showed encoding probability reduction from 0.855 to 0.122, an 85.8% decrease, with super-additivity of 12.8 percentage points when combining two antagonists versus either alone. All five pre-registered hypotheses H1 through H5 were confirmed. The foundational paper is archived at OSF 10.17605/OSF.IO/KG7B5, and the Bayesian population dynamics and clinical trial architecture paper is at Zenodo 10.5281/zenodo.20492472. This work has direct relevance to the Computational Neuroscience Center's focus on neural computation and decision-making. The CCT model addresses a core computational problem: how the brain integrates multiple neuromodulatory signals to gate memory consolidation. The model's mathematical structure is analogous to conjunctive binding in hippocampal place cells and to threshold-based decision rules in reinforcement learning. I have built three computational platforms to support this research. IMPRINT screens compounds for addiction liability using the CCT framework. TOPOLOGIX applies topological data analysis, including persistent homology and bipartite simplicial complexes, to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity prediction. GATE evaluates safety of BCI neural-stimulation protocols and is released under Apache 2.0. My computational skills include Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, topological data analysis (Ripser, Gudhi), neural simulation (NEURON, Brian2), molecular modeling (AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock), and HPC workflow management (Nextflow, SLURM). I have experience with Supabase/Postgres and JavaScript/Node.js for platform deployment. The fellowship would allow me to extend the CCT model in three directions. First, I would implement the model in spiking neural networks using NEURON or Brian2, simulating dopaminergic, glutamatergic, and noradrenergic populations with realistic synaptic dynamics. Second, I would collaborate with Center faculty to design experimental predictions that could be tested in rodent models of addiction. Third, I would develop a Bayesian optimal design framework for clinical trials of combination pharmacotherapy, building on my existing trial architecture preprint. These extensions would form the core of my PhD dissertation and position me for a competitive application to top neuroscience programs. PERSONAL STATEMENT I am a 29-year-old Nigerian pharmacist and independent researcher. I earned my B.Pharm from the University of Ibadan in 2021 with a CGPA of 5.1 out of 7.0, equivalent to a German 1.9, and I am licensed by the Pharmacists Council of Nigeria. After graduation, I worked as a clinical pharmacist at Ramset Pharmacy from January to March 2026, and I currently serve as National Product Manager at Synthcare, a pharmaceutical distribution company in Lagos. My research experience includes a research assistantship at the Centre for Drug Discovery, Development and Production (CDDDP), where I performed molecular docking studies of NMDA receptor ligands and insulin analogues, and a bioinformatics researcher role at the Ghanaian-Swedish Antimicrobial Resistance (GHRU-GSAR) project, where I built an AMR surveillance pipeline. I began developing the CCT model in 2025, working independently in Lagos. I had no host lab, no dedicated funding, and no formal affiliation with a research university. I used open-source tools, free HPC credits through ZYCO, and public datasets. I published three preprints as sole author, built three computational platforms, and secured endorsements from four leading computational neuroscientists. This work was driven by a clinical observation: addiction pharmacotherapy in Nigeria relies on single-agent treatments with low efficacy, and no framework exists for designing rational combination therapies. The CCT model is my attempt to fill that gap. The fellowship at the Computational Neuroscience Center would be transformative for my career. It would provide structured mentorship from faculty working on reinforcement learning and neural computation, access to computational resources beyond what I can access independently, and a collaborative environment where I can test my ideas against established researchers. It would also strengthen my PhD application, which I plan to submit for fall 2027 admission. I am currently applying for an MSc starting October 2026 at MUG/Graz, Austria, but this fellowship would allow me to begin working at the Center immediately and build a stronger case for doctoral admission. I bring a perspective that is underrepresented in computational neuroscience. Most research in this field originates from North America and Europe, and most models are validated on Western datasets and experimental systems. My work in Nigeria has forced me to be resourceful, to build tools from scratch, and to think about translational impact in settings where advanced neuroimaging and electrophysiology are not available. I believe this perspective is valuable for the Center's mission of advancing fundamental understanding of neural computation while training the next generation of diverse researchers. CHECKLIST - [ ] Complete online application form at https://compneuro.washington.edu/shanahan-undergrad-and-postbac-fellowships-applications-open/ - [ ] Upload motivation letter (this document) - [ ] Upload research statement (this document) - [ ] Upload personal statement (this document) - [ ] Upload CV or resume (applicant to prepare) - [ ] Arrange for two letters of recommendation (applicant to identify referees) - [ ] Submit transcripts from University of Ibadan (B.Pharm) - [ ] Submit preprints: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472 - [ ] Confirm eligibility for postbaccalaureate fellowship (applicant to verify degree date and current enrollment status) - [ ] Verify deadline on programme website EDITOR NOTES - Eligibility risk: The fellowship is described as for undergraduate and postbaccalaureate students. Eniola has a B.Pharm from 2021 and is currently employed, not enrolled. Confirm that postbaccalaureate status is defined as having a bachelor's degree and not currently enrolled in a graduate program. If the fellowship requires current enrollment in a degree program, Eniola may need to apply for the MSc first or seek an exception. - Facts needing verification: The fellowship URL and details are sparse. The provider, amount, and deadline are listed as unknown. Eniola should visit the URL to confirm the fellowship is still accepting applications, verify the deadline, and check for any specific eligibility criteria (e.g., US citizenship, GPA minimum, field of study). - Gaps: The profile does not include a list of potential referees. Eniola should identify two or three recommenders who can speak to his research independence and computational skills. Kent Berridge, Samuel Gershman, or Nathaniel Daw are strong candidates if they agree to write. A former supervisor from CDDDP or GHRU-GSAR would also be appropriate. - Missing detail: The personal statement should include a brief explanation of why the University of Washington specifically, beyond the fellowship. Eniola should research faculty at the Computational Neuroscience Center and name one or two whose work aligns with his, then insert that detail. - Timeline: The profile states Eniola is applying for MSc starting October 2026 at MUG/Graz. If the fellowship requires full-time commitment in Seattle, Eniola must clarify how he would manage both commitments or whether the fellowship would replace the MSc plan. This should be addressed in the application or in communication with the program.