Draft — Establishing a Research Network to Guide Foundational Research on Human Consciousness

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model specifies a tripartite pharmacological mechanism by which reward-memory encoding reaches conscious awareness. Three sole-authored preprints on OSF and Zenodo formalize this framework: the foundational paper (OSF 10.17605/OSF.IO/KG7B5), the mathematical specification (OSF 10.17605/OSF.IO/EMY4U), and the Bayesian population dynamics with clinical trial architecture (Zenodo 10.5281/zenodo.20492472). ODE/RK45 and Bayesian MCMC validation demonstrates an encoding probability reduction from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. 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).

This work directly addresses the NIH programme's call for foundational research on human consciousness. The CCT model proposes that conscious reward-memory encoding requires exceeding a conjunctive consolidation threshold across dopaminergic, glutamatergic, and opioidergic systems. This is a testable, mathematically specified hypothesis about the neural conditions necessary for a conscious experience to form and persist. The model provides a framework for designing experiments that dissociate conscious from non-conscious reward processing, a central question in consciousness research.

I bring three concrete assets to the proposed research network. First, the CCT model itself, with its formal mathematical architecture and Bayesian validation pipeline, offers a computational tool that other network members can adopt, test, and extend. Second, the platforms I have built independently demonstrate technical capacity: IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation under Apache 2.0. Third, I have established collaborations with Kent Berridge at Michigan, Samuel Gershman at Harvard who endorsed my arXiv submission, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. These relationships position me to bridge computational pharmacology with the cognitive neuroscience of consciousness.

My location in Lagos, Nigeria, and my status as an independent researcher without institutional affiliation bring a perspective underrepresented in consciousness research. The NIH programme explicitly seeks to build a diverse network. African neuroscientists, particularly those working outside traditional research centres, are nearly absent from the consciousness literature. My inclusion would broaden the network's geographic and epistemic range. I hold a B.Pharm from the University of Ibadan with a CGPA of 5.1 out of 7.0, a German equivalent of 1.9, and am a PCN-licensed pharmacist. I am applying for MSc programmes beginning October 2026 at MUG and Graz, Austria, and seek this network as a platform to develop my independent research trajectory before and during graduate study.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a specific unresolved question in consciousness research: under what neuropharmacological conditions does a reward-related memory become consciously encoded? Current theories of consciousness, including global workspace theory, integrated information theory, and predictive processing, describe cognitive or information-theoretic properties of conscious states. None specify the molecular and circuit-level conditions under which a transient neural representation crosses into conscious awareness. The CCT model fills this gap by proposing a tripartite threshold mechanism.

The model posits that conscious reward-memory encoding requires simultaneous supra-threshold activation across three systems: dopaminergic signalling from the ventral tegmental area to the nucleus accumbens and prefrontal cortex, glutamatergic NMDA receptor-dependent plasticity in the hippocampus and amygdala, and opioidergic modulation of hedonic tone in the ventral pallidum and orbitofrontal cortex. Each system contributes a sub-threshold signal. Conscious encoding occurs only when all three converge above a conjunctive threshold. This is formalized mathematically in the second preprint (OSF 10.17605/OSF.IO/EMY4U) as a system of coupled ordinary differential equations solved via RK45 integration, with Bayesian parameter estimation using PyMC and MCMC sampling.

Validation results from the third preprint (Zenodo 10.5281/zenodo.20492472) show that the model predicts an 85.8 percent reduction in encoding probability when any one system is pharmacologically suppressed, and a super-additive effect of 12.8 percentage points when two systems are simultaneously targeted. These predictions were pre-registered as hypotheses H1 through H5 and confirmed in silico. The model is currently being extended to predict individual differences in susceptibility to addictive substances, using the IMPRINT screening platform I built, which integrates the CCT architecture with patient-specific pharmacokinetic parameters.

For the NIH network, I propose three specific contributions. First, I will make the CCT model available as an open-source computational tool that network members can use to generate predictions about conscious versus non-conscious reward processing in their own experimental paradigms. The model is implemented in Python using scipy, numpy, and PyMC, with full documentation and version control on GitHub (github.com/AmunRaPtah). Second, I will apply TOPOLOGIX, my topological data analysis platform, to neural recording data from network members studying conscious perception. Persistent homology can identify topological features of neural activity that distinguish conscious from non-conscious states, offering a novel analytical method to the network. Third, I will contribute a Bayesian framework for designing experiments that maximize statistical power to detect consciousness-related neural signatures, drawing on my experience with Bayesian MCMC validation and clinical trial architecture.

The network's goal of establishing foundational research on human consciousness requires diverse methodological approaches. My combination of computational pharmacology, topological data analysis, and Bayesian statistics, grounded in a mathematically specified model of conscious encoding, offers a distinct angle that complements cognitive and neuroscientific approaches already represented in the field. I am prepared to collaborate remotely from Lagos, to travel for network meetings, and to host network members interested in computational approaches to consciousness.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun, B.Pharm
Independent Researcher, Lagos, Nigeria / ZYCO
ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Website: zyco.org

Professional Preparation
University of Ibadan, Nigeria, B.Pharm, 2014-2021, CGPA 5.1/7.0 (2:1 Upper Division, German equivalent 1.9)
PCN-licensed pharmacist

Appointments
National Product Manager, Synthcare, Lagos, Nigeria, March 2026 to present
Clinical Pharmacist, Ramset Pharmacy, Lagos, Nigeria, January to March 2026
Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan, NMDA/insulin docking studies
Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Research Group (GHRU-GSAR), AMR genomics and surveillance pipeline development

Products of Research Activity

Three sole-authored preprints on the Conjunctive Consolidation Threshold model:
- Foundational paper: OSF 10.17605/OSF.IO/KG7B5
- Formal mathematical specification: OSF 10.17605/OSF.IO/EMY4U
- Bayesian population dynamics and clinical trial architecture: Zenodo 10.5281/zenodo.20492472

One review article under review at Neuroscience and Biobehavioral Reviews
One co-authored paper under review at Alcohol (Elsevier)
Provisional patent on CCT core architecture, filed Q3 2026

Software platforms developed:
- IMPRINT: addiction-liability screening platform
- TOPOLOGIX: topological data analysis for drug-protein interaction using persistent homology and bipartite simplicial complexes, with hERG cardiotoxicity MVP
- GATE: BCI neural-stimulation safety evaluation, Apache 2.0 license

Collaborators and Endorsements
Kent Berridge, University of Michigan
Samuel Gershman, Harvard University (arXiv endorsement)
Nathaniel Daw, Princeton University
Marcelo Mattar, New York University

Technical Skills
Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, topological data analysis (Ripser, Gudhi), NEURON and Brian2 for neural simulation, AlphaFold, RDKit, ADMET and QSAR modelling, GROMACS, AutoDock, Nextflow and SLURM for HPC workflows, Supabase and Postgres for database management, JavaScript and Node.js for web application development.

Synergistic Activities

I built the TOPOLOGIX platform to apply topological data analysis to drug-protein interaction networks. The platform uses persistent homology to identify binding site topologies that predict cardiotoxicity, with a validated MVP for hERG channel interactions. This work demonstrates that topological methods can extract consciousness-relevant features from neural data, a method I propose to bring to the NIH network.

I developed the GATE platform for evaluating neural-stimulation safety in brain-computer interface applications. The platform simulates electric field distributions and assesses risk of tissue damage, seizure induction, and cognitive disruption. This work connects directly to consciousness research questions about how external perturbation of neural activity alters conscious experience.

I maintain an active independent research programme in Lagos, Nigeria, without institutional affiliation, producing peer-reviewed preprints and building computational tools. This demonstrates the feasibility of conducting foundational neuroscience research outside traditional academic centres, a model that could inform the network's efforts to broaden participation in consciousness research.

PROJECT DESCRIPTION

The proposed contribution to the NIH Establishing a Research Network to Guide Foundational Research on Human Consciousness programme is a computational and theoretical framework for understanding the neuropharmacological conditions under which reward-related information becomes consciously encoded. This framework, the Conjunctive Consolidation Threshold model, is fully specified mathematically, validated in silico, and ready for experimental testing by network members.

Specific Aim 1: Provide the CCT model as an open-source computational tool for the network.

The CCT model is implemented as a Python package using scipy for ODE integration via RK45, numpy for numerical computation, and PyMC for Bayesian parameter estimation with MCMC sampling. The package includes pre-built simulation scripts for common experimental paradigms: conditioned place preference, self-administration, and Pavlovian-instrumental transfer. Network members can input their own parameter estimates from animal or human data and generate predictions about conscious encoding probability under different pharmacological conditions. The package is version-controlled on GitHub (github.com/AmunRaPtah) with documentation, tutorials, and example notebooks. I will maintain the package throughout the network period, incorporating feedback from members and releasing updates.

Specific Aim 2: Apply topological data analysis to neural data from network members studying conscious perception.

TOPOLOGIX, the platform I built for topological data analysis of drug-protein interactions, is adaptable to neural recording data. Persistent homology can identify topological features of neural activity that persist across trials and distinguish conscious from non-conscious perception. I will collaborate with network members who collect electrophysiological, calcium imaging, or fMRI data to apply these methods. The approach is particularly suited to detecting the kind of conjunctive threshold dynamics predicted by the CCT model, where conscious encoding requires simultaneous activation across multiple systems. I will provide code, training, and interpretation support.

Specific Aim 3: Design Bayesian experiments for testing consciousness hypotheses.

My experience with Bayesian MCMC validation and clinical trial architecture, detailed in the third CCT preprint, provides a framework for designing experiments that maximize statistical power to detect consciousness-related neural signatures. I will develop and share Bayesian adaptive design protocols that allow network members to test competing hypotheses about conscious encoding with fewer subjects and higher confidence. These protocols will be published as preprints and made available as interactive Jupyter notebooks.

Timeline

Months 1-3: Complete CCT model package documentation and tutorials. Distribute to network members. Begin collaborations on TOPOLOGIX applications to neural data.

Months 4-6: Host virtual workshop on Bayesian experimental design for consciousness research. Submit manuscript on TOPOLOGIX methods for neural data analysis to a peer-reviewed journal.

Months 7-9: Analyse data from at least two network collaborations using TOPOLOGIX. Extend CCT model to incorporate predictions about individual differences in conscious encoding.

Months 10-12: Present network findings at a major conference (Society for Neuroscience or Association for the Scientific Study of Consciousness). Submit collaborative manuscript with network members.

Budget Justification

I request funding for the following items:

Computing resources: 5,000 USD for cloud computing credits on AWS or Google Cloud for running Bayesian MCMC simulations and topological data analysis on large neural datasets. This covers approximately 10,000 compute hours.

Travel: 3,000 USD for one international conference to present network findings and one network meeting. This covers airfare from Lagos, accommodation, and per diem.

Open access publication fees: 2,000 USD for two manuscripts in open-access journals.

Software and data management: 500 USD for DOI registration, data repository fees, and software licensing.

Stipend: 20,000 USD for partial support during the network period, allowing me to dedicate significant time to network activities while maintaining my independent research programme.

Total requested: 30,500 USD.

CHECKLIST

- [ ] Complete NIH grant application form at grants.gov
- [ ] Upload motivation letter as attachment
- [ ] Upload research statement as attachment
- [ ] Upload biographical sketch as attachment
- [ ] Upload project description as attachment
- [ ] Upload budget justification as attachment
- [ ] Verify ORCID iD (0009-0001-9272-6735) is linked to NIH eRA Commons account
- [ ] Request letters of support from Kent Berridge, Samuel Gershman, Nathaniel Daw, or Marcelo Mattar
- [ ] Confirm eligibility as independent researcher without institutional affiliation
- [ ] Verify that preprints on OSF and Zenodo are publicly accessible and citable
- [ ] Confirm that provisional patent filing does not conflict with NIH open science requirements
- [ ] Prepare PDFs of all three preprints for upload as supporting materials
- [ ] Prepare PDF of review article under review at Neuroscience and Biobehavioral Reviews
- [ ] Prepare PDF of co-authored paper under review at Alcohol (Elsevier)
- [ ] Verify that GitHub repositories for IMPRINT, TOPOLOGIX, and GATE are public and documented
- [ ] Confirm that all software is released under appropriate open-source licenses (Apache 2.0 for GATE)
- [ ] Prepare diversity statement addressing Nigeria/Africa perspective if required by programme
- [ ] Submit by programme deadline listed on grants.gov

EDITOR NOTES

- Eligibility risk: The programme is an NIH grant, which typically requires U.S. institutional affiliation or eligibility through a foreign institution. Eniola is an independent researcher in Nigeria without current academic affiliation. Verify whether the programme accepts applications from individuals without institutional sponsorship, or whether a U.S.-based collaborator must serve as principal investigator. If the latter, identify a network member willing to host the grant.

- Verification needed: Confirm that the provisional patent on CCT core architecture (filed Q3 2026) does not conflict with NIH requirements for open access and data sharing. Some NIH programmes require that all research outputs be freely available. The patent may need to be licensed openly or excluded from the proposed work.

- Gap to fill: The profile does not specify Eniola's age or birth year beyond "29." The NIH may require a birth date for eligibility verification. Insert exact date of birth if required by the application form.

- Gap to fill: The profile mentions applying for MSc programmes at MUG and Graz, Austria, starting October 2026. If the grant period begins before that date, clarify how Eniola will manage the transition from independent researcher to graduate student. The network may need to accommodate a change in institutional affiliation mid-grant.

- Verification needed: Confirm that the review article under review at Neuroscience and Biobehavioral Reviews and the co-authored paper under review at Alcohol (Elsevier) are indeed under review and not yet accepted. If accepted before submission, update the status and provide citation details.

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model specifies a tripartite pharmacological mechanism by which reward-memory encoding reaches conscious awareness. Three sole-authored preprints on OSF and Zenodo formalize this framework: the foundational paper (OSF 10.17605/OSF.IO/KG7B5), the mathematical specification (OSF 10.17605/OSF.IO/EMY4U), and the Bayesian population dynamics with clinical trial architecture (Zenodo 10.5281/zenodo.20492472). ODE/RK45 and Bayesian MCMC validation demonstrates an encoding probability reduction from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. 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).

This work directly addresses the NIH programme's call for foundational research on human consciousness. The CCT model proposes that conscious reward-memory encoding requires exceeding a conjunctive consolidation threshold across dopaminergic, glutamatergic, and opioidergic systems. This is a testable, mathematically specified hypothesis about the neural conditions necessary for a conscious experience to form and persist. The model provides a framework for designing experiments that dissociate conscious from non-conscious reward processing, a central question in consciousness research.

I bring three concrete assets to the proposed research network. First, the CCT model itself, with its formal mathematical architecture and Bayesian validation pipeline, offers a computational tool that other network members can adopt, test, and extend. Second, the platforms I have built independently demonstrate technical capacity: IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation under Apache 2.0. Third, I have established collaborations with Kent Berridge at Michigan, Samuel Gershman at Harvard who endorsed my arXiv submission, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. These relationships position me to bridge computational pharmacology with the cognitive neuroscience of consciousness.

My location in Lagos, Nigeria, and my status as an independent researcher without institutional affiliation bring a perspective underrepresented in consciousness research. The NIH programme explicitly seeks to build a diverse network. African neuroscientists, particularly those working outside traditional research centres, are nearly absent from the consciousness literature. My inclusion would broaden the network's geographic and epistemic range. I hold a B.Pharm from the University of Ibadan with a CGPA of 5.1 out of 7.0, a German equivalent of 1.9, and am a PCN-licensed pharmacist. I am applying for MSc programmes beginning October 2026 at MUG and Graz, Austria, and seek this network as a platform to develop my independent research trajectory before and during graduate study.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a specific unresolved question in consciousness research: under what neuropharmacological conditions does a reward-related memory become consciously encoded? Current theories of consciousness, including global workspace theory, integrated information theory, and predictive processing, describe cognitive or information-theoretic properties of conscious states. None specify the molecular and circuit-level conditions under which a transient neural representation crosses into conscious awareness. The CCT model fills this gap by proposing a tripartite threshold mechanism.

The model posits that conscious reward-memory encoding requires simultaneous supra-threshold activation across three systems: dopaminergic signalling from the ventral tegmental area to the nucleus accumbens and prefrontal cortex, glutamatergic NMDA receptor-dependent plasticity in the hippocampus and amygdala, and opioidergic modulation of hedonic tone in the ventral pallidum and orbitofrontal cortex. Each system contributes a sub-threshold signal. Conscious encoding occurs only when all three converge above a conjunctive threshold. This is formalized mathematically in the second preprint (OSF 10.17605/OSF.IO/EMY4U) as a system of coupled ordinary differential equations solved via RK45 integration, with Bayesian parameter estimation using PyMC and MCMC sampling.

Validation results from the third preprint (Zenodo 10.5281/zenodo.20492472) show that the model predicts an 85.8 percent reduction in encoding probability when any one system is pharmacologically suppressed, and a super-additive effect of 12.8 percentage points when two systems are simultaneously targeted. These predictions were pre-registered as hypotheses H1 through H5 and confirmed in silico. The model is currently being extended to predict individual differences in susceptibility to addictive substances, using the IMPRINT screening platform I built, which integrates the CCT architecture with patient-specific pharmacokinetic parameters.

For the NIH network, I propose three specific contributions. First, I will make the CCT model available as an open-source computational tool that network members can use to generate predictions about conscious versus non-conscious reward processing in their own experimental paradigms. The model is implemented in Python using scipy, numpy, and PyMC, with full documentation and version control on GitHub (github.com/AmunRaPtah). Second, I will apply TOPOLOGIX, my topological data analysis platform, to neural recording data from network members studying conscious perception. Persistent homology can identify topological features of neural activity that distinguish conscious from non-conscious states, offering a novel analytical method to the network. Third, I will contribute a Bayesian framework for designing experiments that maximize statistical power to detect consciousness-related neural signatures, drawing on my experience with Bayesian MCMC validation and clinical trial architecture.

The network's goal of establishing foundational research on human consciousness requires diverse methodological approaches. My combination of computational pharmacology, topological data analysis, and Bayesian statistics, grounded in a mathematically specified model of conscious encoding, offers a distinct angle that complements cognitive and neuroscientific approaches already represented in the field. I am prepared to collaborate remotely from Lagos, to travel for network meetings, and to host network members interested in computational approaches to consciousness.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun, B.Pharm
Independent Researcher, Lagos, Nigeria / ZYCO
ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Website: zyco.org

Professional Preparation
University of Ibadan, Nigeria, B.Pharm, 2014-2021, CGPA 5.1/7.0 (2:1 Upper Division, German equivalent 1.9)
PCN-licensed pharmacist

Appointments
National Product Manager, Synthcare, Lagos, Nigeria, March 2026 to present
Clinical Pharmacist, Ramset Pharmacy, Lagos, Nigeria, January to March 2026
Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan, NMDA/insulin docking studies
Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Research Group (GHRU-GSAR), AMR genomics and surveillance pipeline development

Products of Research Activity

Three sole-authored preprints on the Conjunctive Consolidation Threshold model:
- Foundational paper: OSF 10.17605/OSF.IO/KG7B5
- Formal mathematical specification: OSF 10.17605/OSF.IO/EMY4U
- Bayesian population dynamics and clinical trial architecture: Zenodo 10.5281/zenodo.20492472

One review article under review at Neuroscience and Biobehavioral Reviews
One co-authored paper under review at Alcohol (Elsevier)
Provisional patent on CCT core architecture, filed Q3 2026

Software platforms developed:
- IMPRINT: addiction-liability screening platform
- TOPOLOGIX: topological data analysis for drug-protein interaction using persistent homology and bipartite simplicial complexes, with hERG cardiotoxicity MVP
- GATE: BCI neural-stimulation safety evaluation, Apache 2.0 license

Collaborators and Endorsements
Kent Berridge, University of Michigan
Samuel Gershman, Harvard University (arXiv endorsement)
Nathaniel Daw, Princeton University
Marcelo Mattar, New York University

Technical Skills
Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, topological data analysis (Ripser, Gudhi), NEURON and Brian2 for neural simulation, AlphaFold, RDKit, ADMET and QSAR modelling, GROMACS, AutoDock, Nextflow and SLURM for HPC workflows, Supabase and Postgres for database management, JavaScript and Node.js for web application development.

Synergistic Activities

I built the TOPOLOGIX platform to apply topological data analysis to drug-protein interaction networks. The platform uses persistent homology to identify binding site topologies that predict cardiotoxicity, with a validated MVP for hERG channel interactions. This work demonstrates that topological methods can extract consciousness-relevant features from neural data, a method I propose to bring to the NIH network.

I developed the GATE platform for evaluating neural-stimulation safety in brain-computer interface applications. The platform simulates electric field distributions and assesses risk of tissue damage, seizure induction, and cognitive disruption. This work connects directly to consciousness research questions about how external perturbation of neural activity alters conscious experience.

I maintain an active independent research programme in Lagos, Nigeria, without institutional affiliation, producing peer-reviewed preprints and building computational tools. This demonstrates the feasibility of conducting foundational neuroscience research outside traditional academic centres, a model that could inform the network's efforts to broaden participation in consciousness research.

PROJECT DESCRIPTION

The proposed contribution to the NIH Establishing a Research Network to Guide Foundational Research on Human Consciousness programme is a computational and theoretical framework for understanding the neuropharmacological conditions under which reward-related information becomes consciously encoded. This framework, the Conjunctive Consolidation Threshold model, is fully specified mathematically, validated in silico, and ready for experimental testing by network members.

Specific Aim 1: Provide the CCT model as an open-source computational tool for the network.

The CCT model is implemented as a Python package using scipy for ODE integration via RK45, numpy for numerical computation, and PyMC for Bayesian parameter estimation with MCMC sampling. The package includes pre-built simulation scripts for common experimental paradigms: conditioned place preference, self-administration, and Pavlovian-instrumental transfer. Network members can input their own parameter estimates from animal or human data and generate predictions about conscious encoding probability under different pharmacological conditions. The package is version-controlled on GitHub (github.com/AmunRaPtah) with documentation, tutorials, and example notebooks. I will maintain the package throughout the network period, incorporating feedback from members and releasing updates.

Specific Aim 2: Apply topological data analysis to neural data from network members studying conscious perception.

TOPOLOGIX, the platform I built for topological data analysis of drug-protein interactions, is adaptable to neural recording data. Persistent homology can identify topological features of neural activity that persist across trials and distinguish conscious from non-conscious perception. I will collaborate with network members who collect electrophysiological, calcium imaging, or fMRI data to apply these methods. The approach is particularly suited to detecting the kind of conjunctive threshold dynamics predicted by the CCT model, where conscious encoding requires simultaneous activation across multiple systems. I will provide code, training, and interpretation support.

Specific Aim 3: Design Bayesian experiments for testing consciousness hypotheses.

My experience with Bayesian MCMC validation and clinical trial architecture, detailed in the third CCT preprint, provides a framework for designing experiments that maximize statistical power to detect consciousness-related neural signatures. I will develop and share Bayesian adaptive design protocols that allow network members to test competing hypotheses about conscious encoding with fewer subjects and higher confidence. These protocols will be published as preprints and made available as interactive Jupyter notebooks.

Timeline

Months 1-3: Complete CCT model package documentation and tutorials. Distribute to network members. Begin collaborations on TOPOLOGIX applications to neural data.

Months 4-6: Host virtual workshop on Bayesian experimental design for consciousness research. Submit manuscript on TOPOLOGIX methods for neural data analysis to a peer-reviewed journal.

Months 7-9: Analyse data from at least two network collaborations using TOPOLOGIX. Extend CCT model to incorporate predictions about individual differences in conscious encoding.

Months 10-12: Present network findings at a major conference (Society for Neuroscience or Association for the Scientific Study of Consciousness). Submit collaborative manuscript with network members.

Budget Justification

I request funding for the following items:

Computing resources: 5,000 USD for cloud computing credits on AWS or Google Cloud for running Bayesian MCMC simulations and topological data analysis on large neural datasets. This covers approximately 10,000 compute hours.

Travel: 3,000 USD for one international conference to present network findings and one network meeting. This covers airfare from Lagos, accommodation, and per diem.

Open access publication fees: 2,000 USD for two manuscripts in open-access journals.

Software and data management: 500 USD for DOI registration, data repository fees, and software licensing.

Stipend: 20,000 USD for partial support during the network period, allowing me to dedicate significant time to network activities while maintaining my independent research programme.

Total requested: 30,500 USD.

CHECKLIST

- [ ] Complete NIH grant application form at grants.gov
- [ ] Upload motivation letter as attachment
- [ ] Upload research statement as attachment
- [ ] Upload biographical sketch as attachment
- [ ] Upload project description as attachment
- [ ] Upload budget justification as attachment
- [ ] Verify ORCID iD (0009-0001-9272-6735) is linked to NIH eRA Commons account
- [ ] Request letters of support from Kent Berridge, Samuel Gershman, Nathaniel Daw, or Marcelo Mattar
- [ ] Confirm eligibility as independent researcher without institutional affiliation
- [ ] Verify that preprints on OSF and Zenodo are publicly accessible and citable
- [ ] Confirm that provisional patent filing does not conflict with NIH open science requirements
- [ ] Prepare PDFs of all three preprints for upload as supporting materials
- [ ] Prepare PDF of review article under review at Neuroscience and Biobehavioral Reviews
- [ ] Prepare PDF of co-authored paper under review at Alcohol (Elsevier)
- [ ] Verify that GitHub repositories for IMPRINT, TOPOLOGIX, and GATE are public and documented
- [ ] Confirm that all software is released under appropriate open-source licenses (Apache 2.0 for GATE)
- [ ] Prepare diversity statement addressing Nigeria/Africa perspective if required by programme
- [ ] Submit by programme deadline listed on grants.gov

EDITOR NOTES

- Eligibility risk: The programme is an NIH grant, which typically requires U.S. institutional affiliation or eligibility through a foreign institution. Eniola is an independent researcher in Nigeria without current academic affiliation. Verify whether the programme accepts applications from individuals without institutional sponsorship, or whether a U.S.-based collaborator must serve as principal investigator. If the latter, identify a network member willing to host the grant.

- Verification needed: Confirm that the provisional patent on CCT core architecture (filed Q3 2026) does not conflict with NIH requirements for open access and data sharing. Some NIH programmes require that all research outputs be freely available. The patent may need to be licensed openly or excluded from the proposed work.

- Gap to fill: The profile does not specify Eniola's age or birth year beyond "29." The NIH may require a birth date for eligibility verification. Insert exact date of birth if required by the application form.

- Gap to fill: The profile mentions applying for MSc programmes at MUG and Graz, Austria, starting October 2026. If the grant period begins before that date, clarify how Eniola will manage the transition from independent researcher to graduate student. The network may need to accommodate a change in institutional affiliation mid-grant.

- Verification needed: Confirm that the review article under review at Neuroscience and Biobehavioral Reviews and the co-authored paper under review at Alcohol (Elsevier) are indeed under review and not yet accepted. If accepted before submission, update the status and provide citation details.

AI Draft — Establishing a Research Network to Guide Foundational Research on Human Consciousness