Draft — IBRO Neuroscience Training Grants 2026

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding can be prevented by pharmacologically disrupting the temporal conjunction of dopamine, glutamate, and norepinephrine signals. I developed this tripartite framework as an independent researcher in Lagos, Nigeria, with no formal supervision, no institutional funding, and no access to a university laboratory. The model has been validated through ODE/RK45 simulations showing an 85.8 percent reduction in encoding probability, confirmed across all five pre-registered hypotheses H1 through H5. Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU have endorsed the work. A provisional patent on the core architecture is filed for Q3 2026.

I am applying for the IBRO Neuroscience Training Grant to attend the Computational Psychiatry course at Cold Spring Harbor Laboratory, scheduled for August 2026. This course directly addresses the single largest gap in my current skill set: Bayesian hierarchical modeling for clinical trial simulation. My self-taught PyMC/MCMC pipeline works for population-level dynamics, but I cannot yet model subject-level variability, dropout mechanisms, or adaptive trial designs. The Cold Spring Harbor curriculum covers exactly these methods, taught by faculty who have published the foundational papers I currently read alone.

The training will enable me to simulate a full Bayesian adaptive trial for the CCT model's lead compound, generating the dose-response and safety data required to attract pharmaceutical partnership or academic collaboration. This simulation will form the computational core of my MSc application to the Medical University of Graz, where I plan to start in October 2026. Without this training, I will submit an MSc application that describes a model I cannot fully validate. With it, I will submit a complete computational package.

Nigeria has no computational neuroscience PhD programme. No university in West Africa offers a course in computational psychiatry. I built my entire pipeline from open-source textbooks, YouTube lectures, and GitHub repositories. The IBRO grant is the only funding mechanism I have found that supports an independent LMIC researcher at my career stage to access world-class training without requiring institutional affiliation. The $5,000 maximum for African applicants covers the course fee of $2,800, round-trip airfare from Lagos to New York at $1,200, local accommodation for two weeks at $800, and incidentals at $200. I have secured $1,000 in co-funding from my current employer, Synthcare, toward the accommodation cost.

Samuel Gershman at Harvard has agreed to provide a reference letter. He endorsed my arXiv submission and has reviewed the CCT mathematical specification. His letter will confirm the novelty of the framework and the necessity of formal Bayesian training for its clinical translation.

SHORT ESSAY: RESEARCH BACKGROUND AND TRAINING NEED

The CCT model addresses a specific failure in current addiction pharmacotherapy: no existing drug prevents the encoding of reward-associated memories during active use. My computational simulations demonstrate that a triple-drug combination targeting D1 dopamine receptors, NMDA glutamate receptors, and alpha-2 adrenergic receptors can reduce encoding probability from 0.855 to 0.122, a reduction of 85.8 percent, with super-additive effects of 12.8 percentage points beyond the sum of individual drug effects. These results are published as three sole-authored preprints on OSF and Zenodo, with a review article under review at Neuroscience and Biobehavioral Reviews.

I built the entire computational pipeline myself: ODE solvers in Python with scipy and numpy, Bayesian MCMC validation in PyMC, topological data analysis for drug-protein interaction screening using Ripser and Gudhi, and molecular docking with AutoDock and GROMACS. I also developed three open-source platforms: IMPRINT for addiction-liability screening, TOPOLOGIX for topological drug-protein interaction analysis with a hERG cardiotoxicity MVP, and GATE for BCI neural-stimulation safety evaluation, released under Apache 2.0.

The gap is clinical trial simulation. My current Bayesian models treat the population as a single homogeneous group. A real clinical trial requires hierarchical models that account for subject-level variability in metabolism, adherence, and baseline craving. The Cold Spring Harbor Computational Psychiatry course teaches hierarchical Bayesian models for clinical data, adaptive trial design, and model comparison using Watanabe-Akaike information criteria. These methods are standard in the field but absent from my self-taught curriculum. The training will directly enable me to simulate the Phase 2a trial architecture I have already designed in the third preprint, replacing the current simplified model with a realistic Bayesian adaptive framework.

SHORT ESSAY: IMPACT AND FUTURE PLANS

The immediate output of this training will be a complete Bayesian adaptive trial simulation for the CCT model's lead triple-drug combination, submitted as a preprint within three months of course completion. This simulation will include subject-level variability, dropout modeling, and dose-escalation logic, making it suitable for inclusion in an Investigational New Drug application or equivalent regulatory filing. I will release the simulation code as an open-source repository under the same Apache 2.0 license as my existing platforms.

Within six months, I will submit this simulation as the computational centerpiece of my MSc application to the Medical University of Graz, where I have identified a supervisor working on computational psychiatry and reinforcement learning. The training certificate from Cold Spring Harbor will strengthen my application by demonstrating formal training in a programme that admits only 25 participants per year.

Within twelve months, I will use the hierarchical Bayesian framework to re-analyze the CCT model's sensitivity to parameter uncertainty, producing a formal uncertainty quantification that the current ODE-based analysis lacks. This analysis will be submitted to a computational neuroscience journal, with the training course instructors acknowledged.

The longer-term goal is to establish a computational neuroscience research group in Nigeria. No such group currently exists. The CCT model, the open-source platforms, and the training from this grant will form the foundation. I have already built the computational infrastructure: HPC pipelines on SLURM, Nextflow workflows for reproducible analysis, and a Supabase backend for the IMPRINT screening platform. What I lack is the formal training to lead a group that publishes in top journals. This grant fills that gap.

BUDGET

Course fee: Cold Spring Harbor Laboratory Computational Psychiatry course, August 2026. $2,800.
Round-trip airfare: Lagos to New York, economy. $1,200.
Local accommodation: 14 nights at shared housing near CSHL. $800.
Meals and local transport: 14 days. $200.
Total: $5,000.
Co-funding: Synthcare has committed $1,000 toward accommodation. Remaining $4,000 requested from IBRO.

REFERENCE LETTER REQUEST

Samuel Gershman, Professor of Psychology and Neuroscience, Harvard University. He endorsed my arXiv submission and has reviewed the CCT mathematical specification. He will confirm the novelty of the framework, the rigor of the computational validation, and the necessity of formal Bayesian training for clinical translation. His letter will also address my independent researcher status and the absence of formal supervision, which is standard for LMIC researchers without access to PhD programmes.

CHECKLIST

- [ ] Motivation letter, 500 words maximum
- [ ] Short essay: research background and training need, 350 words maximum
- [ ] Short essay: impact and future plans, 350 words maximum
- [ ] Budget table with itemized costs and co-funding source
- [ ] Reference letter from Samuel Gershman, Harvard University
- [ ] CV with ORCID, GitHub, and publication links
- [ ] Proof of independent researcher status (statement of no current institutional affiliation)
- [ ] Course acceptance or proof of application to Cold Spring Harbor Computational Psychiatry course
- [ ] Proof of Nigerian citizenship (passport copy)
- [ ] IBRO online application form completed by May 15, 2026

EDITOR NOTES

- Eligibility risk: IBRO specifies PhD student or early-career postdoc within 5 years of first postdoc. Eniola is neither. The application must explicitly argue that his independent research output (three preprints, one review under review, co-authored paper under review, three platforms, endorsements from four senior PIs) constitutes equivalent standing. This is the single biggest risk.
- Reference letter: Confirm that Gershman has agreed in writing and understands the IBRO format. Have a backup letter from Berridge or Daw in case Gershman is unavailable.
- Course acceptance: Cold Spring Harbor Computational Psychiatry course typically requires an application separate from the IBRO grant. Verify the 2026 course dates and application deadline. If the course is not yet accepting applications, state that the application is in progress.
- Co-funding: Confirm the $1,000 commitment from Synthcare in writing. The budget should specify whether this is a grant, salary support, or personal contribution.
- MSc timeline: Eniola plans to apply for October 2026 start at MUG/Graz. Confirm that the MSc application deadline is after the August 2026 course. If not, adjust the timeline or identify a later course.

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding can be prevented by pharmacologically disrupting the temporal conjunction of dopamine, glutamate, and norepinephrine signals. I developed this tripartite framework as an independent researcher in Lagos, Nigeria, with no formal supervision, no institutional funding, and no access to a university laboratory. The model has been validated through ODE/RK45 simulations showing an 85.8 percent reduction in encoding probability, confirmed across all five pre-registered hypotheses H1 through H5. Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU have endorsed the work. A provisional patent on the core architecture is filed for Q3 2026.

I am applying for the IBRO Neuroscience Training Grant to attend the Computational Psychiatry course at Cold Spring Harbor Laboratory, scheduled for August 2026. This course directly addresses the single largest gap in my current skill set: Bayesian hierarchical modeling for clinical trial simulation. My self-taught PyMC/MCMC pipeline works for population-level dynamics, but I cannot yet model subject-level variability, dropout mechanisms, or adaptive trial designs. The Cold Spring Harbor curriculum covers exactly these methods, taught by faculty who have published the foundational papers I currently read alone.

The training will enable me to simulate a full Bayesian adaptive trial for the CCT model's lead compound, generating the dose-response and safety data required to attract pharmaceutical partnership or academic collaboration. This simulation will form the computational core of my MSc application to the Medical University of Graz, where I plan to start in October 2026. Without this training, I will submit an MSc application that describes a model I cannot fully validate. With it, I will submit a complete computational package.

Nigeria has no computational neuroscience PhD programme. No university in West Africa offers a course in computational psychiatry. I built my entire pipeline from open-source textbooks, YouTube lectures, and GitHub repositories. The IBRO grant is the only funding mechanism I have found that supports an independent LMIC researcher at my career stage to access world-class training without requiring institutional affiliation. The $5,000 maximum for African applicants covers the course fee of $2,800, round-trip airfare from Lagos to New York at $1,200, local accommodation for two weeks at $800, and incidentals at $200. I have secured $1,000 in co-funding from my current employer, Synthcare, toward the accommodation cost.

Samuel Gershman at Harvard has agreed to provide a reference letter. He endorsed my arXiv submission and has reviewed the CCT mathematical specification. His letter will confirm the novelty of the framework and the necessity of formal Bayesian training for its clinical translation.

SHORT ESSAY: RESEARCH BACKGROUND AND TRAINING NEED

The CCT model addresses a specific failure in current addiction pharmacotherapy: no existing drug prevents the encoding of reward-associated memories during active use. My computational simulations demonstrate that a triple-drug combination targeting D1 dopamine receptors, NMDA glutamate receptors, and alpha-2 adrenergic receptors can reduce encoding probability from 0.855 to 0.122, a reduction of 85.8 percent, with super-additive effects of 12.8 percentage points beyond the sum of individual drug effects. These results are published as three sole-authored preprints on OSF and Zenodo, with a review article under review at Neuroscience and Biobehavioral Reviews.

I built the entire computational pipeline myself: ODE solvers in Python with scipy and numpy, Bayesian MCMC validation in PyMC, topological data analysis for drug-protein interaction screening using Ripser and Gudhi, and molecular docking with AutoDock and GROMACS. I also developed three open-source platforms: IMPRINT for addiction-liability screening, TOPOLOGIX for topological drug-protein interaction analysis with a hERG cardiotoxicity MVP, and GATE for BCI neural-stimulation safety evaluation, released under Apache 2.0.

The gap is clinical trial simulation. My current Bayesian models treat the population as a single homogeneous group. A real clinical trial requires hierarchical models that account for subject-level variability in metabolism, adherence, and baseline craving. The Cold Spring Harbor Computational Psychiatry course teaches hierarchical Bayesian models for clinical data, adaptive trial design, and model comparison using Watanabe-Akaike information criteria. These methods are standard in the field but absent from my self-taught curriculum. The training will directly enable me to simulate the Phase 2a trial architecture I have already designed in the third preprint, replacing the current simplified model with a realistic Bayesian adaptive framework.

SHORT ESSAY: IMPACT AND FUTURE PLANS

The immediate output of this training will be a complete Bayesian adaptive trial simulation for the CCT model's lead triple-drug combination, submitted as a preprint within three months of course completion. This simulation will include subject-level variability, dropout modeling, and dose-escalation logic, making it suitable for inclusion in an Investigational New Drug application or equivalent regulatory filing. I will release the simulation code as an open-source repository under the same Apache 2.0 license as my existing platforms.

Within six months, I will submit this simulation as the computational centerpiece of my MSc application to the Medical University of Graz, where I have identified a supervisor working on computational psychiatry and reinforcement learning. The training certificate from Cold Spring Harbor will strengthen my application by demonstrating formal training in a programme that admits only 25 participants per year.

Within twelve months, I will use the hierarchical Bayesian framework to re-analyze the CCT model's sensitivity to parameter uncertainty, producing a formal uncertainty quantification that the current ODE-based analysis lacks. This analysis will be submitted to a computational neuroscience journal, with the training course instructors acknowledged.

The longer-term goal is to establish a computational neuroscience research group in Nigeria. No such group currently exists. The CCT model, the open-source platforms, and the training from this grant will form the foundation. I have already built the computational infrastructure: HPC pipelines on SLURM, Nextflow workflows for reproducible analysis, and a Supabase backend for the IMPRINT screening platform. What I lack is the formal training to lead a group that publishes in top journals. This grant fills that gap.

BUDGET

Course fee: Cold Spring Harbor Laboratory Computational Psychiatry course, August 2026. $2,800.
Round-trip airfare: Lagos to New York, economy. $1,200.
Local accommodation: 14 nights at shared housing near CSHL. $800.
Meals and local transport: 14 days. $200.
Total: $5,000.
Co-funding: Synthcare has committed $1,000 toward accommodation. Remaining $4,000 requested from IBRO.

REFERENCE LETTER REQUEST

Samuel Gershman, Professor of Psychology and Neuroscience, Harvard University. He endorsed my arXiv submission and has reviewed the CCT mathematical specification. He will confirm the novelty of the framework, the rigor of the computational validation, and the necessity of formal Bayesian training for clinical translation. His letter will also address my independent researcher status and the absence of formal supervision, which is standard for LMIC researchers without access to PhD programmes.

CHECKLIST

- [ ] Motivation letter, 500 words maximum
- [ ] Short essay: research background and training need, 350 words maximum
- [ ] Short essay: impact and future plans, 350 words maximum
- [ ] Budget table with itemized costs and co-funding source
- [ ] Reference letter from Samuel Gershman, Harvard University
- [ ] CV with ORCID, GitHub, and publication links
- [ ] Proof of independent researcher status (statement of no current institutional affiliation)
- [ ] Course acceptance or proof of application to Cold Spring Harbor Computational Psychiatry course
- [ ] Proof of Nigerian citizenship (passport copy)
- [ ] IBRO online application form completed by May 15, 2026

EDITOR NOTES

- Eligibility risk: IBRO specifies PhD student or early-career postdoc within 5 years of first postdoc. Eniola is neither. The application must explicitly argue that his independent research output (three preprints, one review under review, co-authored paper under review, three platforms, endorsements from four senior PIs) constitutes equivalent standing. This is the single biggest risk.
- Reference letter: Confirm that Gershman has agreed in writing and understands the IBRO format. Have a backup letter from Berridge or Daw in case Gershman is unavailable.
- Course acceptance: Cold Spring Harbor Computational Psychiatry course typically requires an application separate from the IBRO grant. Verify the 2026 course dates and application deadline. If the course is not yet accepting applications, state that the application is in progress.
- Co-funding: Confirm the $1,000 commitment from Synthcare in writing. The budget should specify whether this is a grant, salary support, or personal contribution.
- MSc timeline: Eniola plans to apply for October 2026 start at MUG/Graz. Confirm that the MSc application deadline is after the August 2026 course. If not, adjust the timeline or identify a later course.

AI Draft — IBRO Neuroscience Training Grants 2026