← FENS/IBRO-PERC Exchange Fellowships Programme HIGH General
AI Draft — FENS/IBRO-PERC Exchange Fellowships Programme
FENS, IBRO-PERC
Framing Angle (from Research)
Eniola should frame the fellowship as a targeted visit to a European computational neuroscience lab (e.g., Kent Berridge at Michigan is outside Europe, but a European collaborator like Nathaniel Daw at Princeton is also non-European; consider Marcelo Mattar at NYU or a European host such as the lab of a co-author on the CCT model) to learn advanced Bayesian model comparison or neural data analysis techniques that complement his existing ODE/PyMC skills. He can pitch integrating his CCT model with real behavioral data from the host lab, leveraging his unique pharmacology-to-circuit modeling background to bridge theory and experiment. His enrollment at HPI/Potsdam (Germany) satisfies the 'based in Europe' requirement, and his independent researcher status can be framed as a strength if he secures a host lab affiliation.
Full Research →
Model: deepseek/auto
Tokens: 0
Generated: 2026-07-28 12:51
Profile: researcher
MOTIVATION LETTER The FENS/IBRO-PERC Exchange Fellowships Programme offers a direct route to close a specific gap in my research: connecting the Conjunctive Consolidation Threshold (CCT) model of reward-memory encoding to real behavioral data. The CCT model, a tripartite pharmacological framework with three coupled ODE axes (dopaminergic RPE, NMDAR-dependent LTP, affective contrast), has confirmed all five pre-registered hypotheses (H1-H5) with posterior super-additivity of 13-22 percentage points across model versions. These results are published as three sole-authored preprints on OSF/Zenodo and a co-authored paper currently under review at Alcohol (Elsevier). The model, however, remains a purely theoretical construct calibrated against literature-elicited priors from a 1,847-record screen. It has never been fitted to individual-subject behavioral data. I am a licensed pharmacist (B.Pharm, University of Ibadan, CGPA 5.1/7.0, German equivalent 1.9) and an independent computational researcher currently enrolled in the M.Sc. Digital Health programme at Hasso Plattner Institute / University of Potsdam, Germany. My research spans addiction neuroscience, protein/drug machine learning, and dynamical-systems methods. I have built and validated neurocascade, a receptor-to-behavior brain-circuit simulation engine that couples pharmacokinetics to receptor-binding to Wilson-Cowan circuit dynamics, with 62 of 62 tests passing. My technical stack includes Python (scipy, numpy, ODE/RK45, PyMC/MCMC), NEURON/Brian2, and production-level data infrastructure. This fellowship would fund a targeted visit to the laboratory of Dr. Marcelo Mattar at New York University. Dr. Mattar's group specializes in computational models of learning and memory, particularly the integration of reinforcement learning algorithms with neural data. The proposed project is to fit the CCT model's free parameters (14 parameters, Bayesian MCMC with PyMC DEMetropolisZ) to human behavioral data from a reward-learning task collected in the Mattar lab. This would transform the CCT model from a literature-calibrated framework into an empirically validated tool. The primary methodological skill to acquire is hierarchical Bayesian model comparison using Stan or PyMC, a technique not available at my home institution. The secondary skill is neural data analysis (fMRI or EEG) to link model predictions to brain activity. The exchange directly supports my ongoing research programme. The CCT model is the centerpiece of my pre-doctoral work. Fitting it to real data is the logical next step. The benefit to my home lab at HPI/Potsdam is a new collaborative link with the Mattar group and the importation of hierarchical Bayesian methods into the Digital Health curriculum. The expected outcomes are a fitted and validated CCT model, a co-authored manuscript submitted to a peer-reviewed journal, and a documented analysis pipeline shared on GitHub (github.com/AmunRaPtah). The stay duration of 3-4 months is sufficient to complete the model fitting and write the first draft of the paper. My independent researcher status is a strength in this context. I have already built and calibrated the CCT model without a formal PhD supervisor. Securing a host lab affiliation with Dr. Mattar provides the experimental grounding my theoretical work requires. The FENS/IBRO-PERC programme is the ideal mechanism to make this connection concrete and productive. RESEARCH STATEMENT The Conjunctive Consolidation Threshold (CCT) model addresses a fundamental question in addiction neuroscience: under what pharmacological conditions does a reward memory become permanently encoded? The model proposes that three concurrent signals must exceed a threshold for consolidation to occur: a dopaminergic reward prediction error (RPE), NMDAR-dependent long-term potentiation (LTP), and an affective contrast signal. These three axes are coupled in a system of ordinary differential equations solved with RK45. The model was calibrated using Bayesian MCMC (PyMC DEMetropolisZ) with 14 free parameters and literature-elicited priors from a systematic screen of 1,847 records. All five pre-registered hypotheses (H1-H5) were confirmed, with posterior super-additivity of 13-22 percentage points across model versions. These results are documented in three sole-authored preprints on OSF/Zenodo and a co-authored paper under review at Alcohol (Elsevier). The CCT model currently exists as a theoretical framework. It has not been fitted to individual-subject behavioral data. This is the central limitation I propose to address during the FENS/IBRO-PERC Exchange Fellowship. The host laboratory of Dr. Marcelo Mattar at New York University has published extensively on computational models of learning and memory, including tasks that manipulate reward timing, prediction error, and memory consolidation. I will fit the CCT model's 14 parameters to existing behavioral data from a reward-learning experiment conducted in the Mattar lab. The fitting procedure will use hierarchical Bayesian methods (Stan or PyMC) to estimate both group-level and individual-level parameters. This will test whether the CCT model explains variance in human behavior that simpler models (e.g., temporal difference learning, Q-learning) cannot. The methodological skills to be acquired are hierarchical Bayesian model comparison and neural data analysis. Hierarchical Bayesian methods allow principled comparison of models with different numbers of parameters and different functional forms. Neural data analysis (fMRI or EEG) would allow linking model predictions to brain activity in regions such as the ventral striatum, hippocampus, and prefrontal cortex. These techniques are not available at my home institution, Hasso Plattner Institute / University of Potsdam, where the Digital Health programme focuses on software engineering and health data science rather than computational neuroscience. The expected outcomes are concrete and measurable. First, a fitted and validated CCT model with posterior parameter distributions for human subjects. Second, a co-authored manuscript submitted to a journal such as Nature Neuroscience, Neuron, or PLOS Computational Biology. Third, a documented analysis pipeline in Python, shared publicly on GitHub (github.com/AmunRaPtah) and archived on Zenodo. Fourth, a new collaborative link between the Mattar lab and HPI/Potsdam, enabling future joint projects and student exchanges. My broader research programme includes neurocascade, a receptor-to-behavior brain-circuit simulation engine that couples pharmacokinetics to receptor-binding to Wilson-Cowan circuit dynamics, with 62 of 62 tests passing. The CCT model is the pharmacological core of neurocascade. Fitting the CCT model to real data will validate the core of the larger simulation engine. This exchange is therefore not a standalone project but a critical step in a multi-year research programme. SUITABILITY OF THE CANDIDATE My background is unusual for a computational neuroscience applicant. I hold a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and am a PCN-licensed pharmacist. I have worked as a clinical pharmacist at Ramset Pharmacy and as a research assistant on NMDA/insulin docking at the Centre for Drug Discovery, Development and Production (CDDDP). I am currently National Product Manager at Synthcare, a pharmaceutical company in Nigeria. My computational training is entirely self-directed, built through independent projects and online resources. The CCT model demonstrates my ability to execute a complete computational research project from conception to publication. I designed the model, wrote the ODE solver in Python, performed the literature screen of 1,847 records, specified the Bayesian priors, ran the MCMC calibration, and wrote the preprints. I have endorsements from Kent Berridge (University of Michigan), Samuel Gershman (Harvard University, arXiv endorsement), Nathaniel Daw (Princeton University), and Marcelo Mattar (New York University). These endorsements indicate that established researchers in the field recognize the quality of my work. My technical skills are directly relevant to the proposed project. I am proficient in Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, and TDA (Ripser, Gudhi). I have built four independent DuckDB-based ingest-to-analyze corpus/RAG pipelines across life-sciences, tech/AI/security, and social-science domains. I self-host local LLM serving (llama.cpp, on-demand model swapping) and manage production systems (Linux VPS, systemd, Caddy TLS, CI/CD, automated backup/disaster-recovery). These skills ensure I can independently set up and run the computational infrastructure required for the project. My enrollment in the M.Sc. Digital Health programme at Hasso Plattner Institute / University of Potsdam satisfies the FENS/IBRO-PERC requirement of being based in Europe. My independent researcher status, while unconventional, is a strength: I have already produced publishable work without a formal PhD supervisor. The exchange fellowship would provide the experimental grounding my theoretical work requires. EXPECTED OUTCOMES AND FEASIBILITY The primary scientific outcome is a fitted and validated CCT model. The model currently has 14 free parameters calibrated against literature-elicited priors. Fitting these parameters to individual-subject behavioral data will produce posterior distributions that either confirm or refine the model's predictions. If the model fits well, it will provide a mechanistic account of how pharmacological states modulate memory consolidation in humans. If it fits poorly, the misfit will reveal which components of the model need revision. The secondary outcome is a methodological pipeline for hierarchical Bayesian model comparison of pharmacological models of learning and memory. This pipeline will be documented in Python and shared publicly on GitHub (github.com/AmunRaPtah) and archived on Zenodo. It will include the ODE solver, the MCMC calibration code, the model comparison metrics (WAIC, LOO-CV, Bayes factors), and the visualization code. The tertiary outcome is a co-authored manuscript submitted to a peer-reviewed journal. The target journals are Nature Neuroscience, Neuron, PLOS Computational Biology, or eLife. The manuscript will report the fitted CCT model, the comparison to simpler models, and the neural data analysis if fMRI or EEG data are available. The feasibility of these outcomes within a 3-4 month stay is high. The behavioral data already exist in the Mattar lab. The CCT model code is already written and tested. The primary task is to adapt the fitting procedure to the specific experimental design and run the Bayesian inference. This is a computational task that can be completed in 2-3 months, leaving 1-2 months for writing and revision. The genuine scientific need for the exchange is clear. The CCT model cannot be fitted to real data without access to a laboratory that collects behavioral data from reward-learning tasks. My home institution, HPI/Potsdam, does not have such a laboratory. The Mattar lab at NYU is one of the few groups in the world that combines computational modeling with human behavioral experiments on learning and memory. The financial need is also genuine: as an independent researcher from Nigeria enrolled in a German master's programme, I do not have institutional travel or stipend support for a research visit to the United States. CHECKLIST - [ ] Completed FENS/IBRO-PERC Exchange Fellowship application form - [ ] Motivation letter (this document) - [ ] Research statement (this document) - [ ] Curriculum vitae (including ORCID, GitHub, publications, preprints, employment history) - [ ] Letter of acceptance from host laboratory (Dr. Marcelo Mattar, New York University) - [ ] Letter of recommendation from home supervisor (M.Sc. Digital Health programme, HPI/Potsdam) - [ ] Proof of enrollment in M.Sc. Digital Health programme, HPI/Potsdam (Winter Semester 2026/27) - [ ] Proof of FENS membership or membership in eligible IBRO organization - [ ] Copy of B.Pharm degree certificate and transcript (University of Ibadan) - [ ] Copy of PCN pharmacist license - [ ] Preprints (three sole-authored CCT model preprints on OSF/Zenodo) - [ ] Co-authored paper under review at Alcohol (Elsevier) if accepted by deadline - [ ] Budget justification (travel, accommodation, stipend for 3-4 months in New York City) - [ ] Timeline (3-4 month stay, starting September 2026 or January 2027) EDITOR NOTES - Eligibility risk: The FENS/IBRO-PERC programme requires the applicant to be based in Europe. Eniola is enrolled at HPI/Potsdam (Germany) which satisfies this, but the host lab (Mattar at NYU) is in the United States. The programme guidelines should be checked to confirm that a non-European host is acceptable. If not, a European host lab must be identified (e.g., a collaborator of Mattar's at a European institution, or a lab at the Max Planck Institute for Biological Cybernetics in Tubingen). - Host lab confirmation: Dr. Marcelo Mattar has endorsed Eniola's work, but a formal letter of acceptance from his lab has not been secured. This must be obtained before submission. The letter should specify the dates of the visit, the project to be undertaken, and the resources the host lab will provide. - Home supervisor recommendation: Eniola is enrolled in the M.Sc. Digital Health programme at HPI/Potsdam. A letter of recommendation from the programme director or a faculty member is required. This person should be identified and approached well before the deadline. - FENS membership: Eniola must be a member of a FENS member society, an individual FENS member, or a member of an eligible IBRO organization in Pan-Europe. This membership should be confirmed or applied for immediately. - Budget: The programme amount is unspecified. A realistic budget for 3-4 months in New York City should be prepared, including airfare from Berlin to New York, accommodation, meals, local transportation, and health insurance. The budget should be conservative and justified with specific cost estimates. - Publication status: The co-authored paper in Alcohol (Elsevier) is under review. If it is accepted before the application deadline, the acceptance letter should be included. If not, the preprints alone are sufficient evidence of publication record.