Research Themes, 2020–2026
The lab's academic output during this time can be categorized into eight primary topical clusters, representing a diverse synthesis of developmental biology, cognitive science, and computational modeling:
Developmental Neurosimulation and Agent Learning: This area focuses on using Developmental Braitenberg Vehicles (dBVs) to model how nervous systems and behaviors emerge through morphogenetic growth and plastic learning periods. These papers emphasize developmental freedom and contingency as agents transition from undefined structures to functional phenotypes.
Alicea, B. and Hough, M. (2025). The Augmentation of Intelligent Agents for Human-Machine Superperformance. Studies in Computational Intelligence, 1243, 46-53.
Alicea, B. (2025). Allostasis Machines as Continuous Cognitive Modeling. Studies in Computational Intelligence, 477, 9-18.
Alicea, B., Chakrabarty, R., Dvoretskii, S., Gopiiswaminathan, A.V., Lim, A., and Parent, J. (2024). Continual Developmental Neurosimulation Using Embodied Computational Agents. IOP Conference Series: Materials Science and Engineering, 1321, 012013.
Dvoretskii, S., Gong, Z., Gupta, A., Parent, J., and Alicea, B. (2022). Braitenberg Vehicles as Developmental Neurosimulation. Artificial Life, 28(3), 369-395.
Meta-brain Models & Layered Representations: These research works propose hybrid architectures that combine different degrees of representational complexity. They explore how laminar organization (layering) and folding—common in the mammalian brain and gut—enable robust self-regulation and semi-neuronal information processing.
Alicea, B. and Parent, J. (2022). Layers, Folds, and Semi-Neuronal Information Processing. Procedia Computer Science, 213, 443-452.
Alicea, B. and Parent, J. (2022). Meta-brain Models: biologically-inspired cognitive agents. International Workshop on Embodied Intelligence, IOP Conference Series: Materials Science and Engineering, 1261, 012019.
Biological Morphogenesis & Connectomics: Centered primarily on C. elegans and Zebrafish (Danio rerio), these papers provide quantitative assessments of cell differentiation, lineage trees, and the emerging connectome. Key concepts include differentiation waves and hypergraphs used to analyze the divergent integration of tissues.
Alicea, B., Bastani, S., Gordon, N.K., Crawford-Young, S., and Gordon, R. (2024). The Molecular Basis of Differentiation Wave Activity in Embryogenesis. BioSystems, 105272.
Alicea, B., Gordon, R., and Parent, J. (2023). Embodied cognitive morphogenesis as a route to intelligent systems. Royal Society Interface Focus, 13(3), 20220067.
Alicea, B., Parent, J., and Singh, U. (2021). Periodicity in the Embryo: emergence of order in space, diffusion of order in time. Biosystems, 204, 104405.
Gibsonian Information & Ecological Psychology: Our work in this area has introduced the concept of Gibsonian Information (GI) as a way to characterize information through active perception and environmental interaction. GI focused on how agents extract spatiotemporal structure from the world.
Alicea, B., Cialfi, D., Lim, A., Parent, J. (2024). A Primer on Gibsonian Information. arXiv, 2403.18829.
Alicea, B., Cialfi, D., Lim, A., and Parent, J. (2022). Gibsonian Information: a new approach to quantitative information. Studies in Computational Intelligence, 1032.
Cybernetics & Allostatic Regulation: The lab has revitalized classical cybernetics through the Every Good Regulator (EGR) theorem, using it to understand "world models" for intelligent systems. The lab has also introduced the concept of Allostasis Machines to model internal regulatory states that maintain stability through variation.
Alicea, B., Hough, M., Nelson, A., and Parent, J. (2026). A "Good" Regulator May Provide a World Model for Intelligent Systems. Royal Society A, 384, 20250007.
Alicea, B., Hough, M., Nelson, A., and Parent, J. (2025). A Brief History of Cybernetic Imperatives and Behavior. OSF Preprints, doi:10.31234/osf.io/mnhux.
Intelligence Offloading & Stigmergy: This area examines how embodied agents and multi-agent collectives distribute cognitive tasks into the environment through affordances and stigmergic traces, or informational marks left in the environment.
Alicea, B., Pang, A., and Parent, J. (2023). Intelligence Offloading and the Neurosimulation of Developmental Agents. International Workshop on Embodied Intelligence, IOP Conference Series: Materials Science and Engineering, 1292, 012019.
Optimization and Literary NLP: This smaller cluster applies evolutionary computation and Natural language Processing (NLP) techniques to specific problems, such as Genetic Algorithms with Reversals for flight scheduling and sentiment progression models for searching and indexing literary artefacts.
Grover, A., Yadav, V., and Alicea, B. (2023). Flipping the Switch on Local Exploration: Genetic Algorithms with Reversals. Lecture Notes in Networks and Systems, 608, 719–734.
Kulkarni, H. and Alicea, B. (2021). Sentiment Progression based Searching and Indexing of Literary Textual Artefacts. Lecture Notes in Computer Science, 18201, 264-271.
Open Access and Meta-science: Largely completed in conjunction with other organizations such as eLife and Neuromatch, this area stresses new ways of conducting, disseminating, and accessing scholarship.
Jambor et.al (18 co-authors, 2021). Creating clear and informative image-based figures for scientific publications. PLoS Biology, 19(3), e3001161.
’t Hart et.al (156 co-authors, 2022). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education, 4(44), 118.
Developmental Neurosimulation and Agent Learning: This area focuses on using Developmental Braitenberg Vehicles (dBVs) to model how nervous systems and behaviors emerge through morphogenetic growth and plastic learning periods. These papers emphasize developmental freedom and contingency as agents transition from undefined structures to functional phenotypes.
Alicea, B. and Hough, M. (2025). The Augmentation of Intelligent Agents for Human-Machine Superperformance. Studies in Computational Intelligence, 1243, 46-53.
Alicea, B. (2025). Allostasis Machines as Continuous Cognitive Modeling. Studies in Computational Intelligence, 477, 9-18.
Alicea, B., Chakrabarty, R., Dvoretskii, S., Gopiiswaminathan, A.V., Lim, A., and Parent, J. (2024). Continual Developmental Neurosimulation Using Embodied Computational Agents. IOP Conference Series: Materials Science and Engineering, 1321, 012013.
Dvoretskii, S., Gong, Z., Gupta, A., Parent, J., and Alicea, B. (2022). Braitenberg Vehicles as Developmental Neurosimulation. Artificial Life, 28(3), 369-395.
Meta-brain Models & Layered Representations: These research works propose hybrid architectures that combine different degrees of representational complexity. They explore how laminar organization (layering) and folding—common in the mammalian brain and gut—enable robust self-regulation and semi-neuronal information processing.
Alicea, B. and Parent, J. (2022). Layers, Folds, and Semi-Neuronal Information Processing. Procedia Computer Science, 213, 443-452.
Alicea, B. and Parent, J. (2022). Meta-brain Models: biologically-inspired cognitive agents. International Workshop on Embodied Intelligence, IOP Conference Series: Materials Science and Engineering, 1261, 012019.
Biological Morphogenesis & Connectomics: Centered primarily on C. elegans and Zebrafish (Danio rerio), these papers provide quantitative assessments of cell differentiation, lineage trees, and the emerging connectome. Key concepts include differentiation waves and hypergraphs used to analyze the divergent integration of tissues.
Alicea, B., Bastani, S., Gordon, N.K., Crawford-Young, S., and Gordon, R. (2024). The Molecular Basis of Differentiation Wave Activity in Embryogenesis. BioSystems, 105272.
Alicea, B., Gordon, R., and Parent, J. (2023). Embodied cognitive morphogenesis as a route to intelligent systems. Royal Society Interface Focus, 13(3), 20220067.
Alicea, B., Parent, J., and Singh, U. (2021). Periodicity in the Embryo: emergence of order in space, diffusion of order in time. Biosystems, 204, 104405.
Gibsonian Information & Ecological Psychology: Our work in this area has introduced the concept of Gibsonian Information (GI) as a way to characterize information through active perception and environmental interaction. GI focused on how agents extract spatiotemporal structure from the world.
Alicea, B., Cialfi, D., Lim, A., Parent, J. (2024). A Primer on Gibsonian Information. arXiv, 2403.18829.
Alicea, B., Cialfi, D., Lim, A., and Parent, J. (2022). Gibsonian Information: a new approach to quantitative information. Studies in Computational Intelligence, 1032.
Cybernetics & Allostatic Regulation: The lab has revitalized classical cybernetics through the Every Good Regulator (EGR) theorem, using it to understand "world models" for intelligent systems. The lab has also introduced the concept of Allostasis Machines to model internal regulatory states that maintain stability through variation.
Alicea, B., Hough, M., Nelson, A., and Parent, J. (2026). A "Good" Regulator May Provide a World Model for Intelligent Systems. Royal Society A, 384, 20250007.
Alicea, B., Hough, M., Nelson, A., and Parent, J. (2025). A Brief History of Cybernetic Imperatives and Behavior. OSF Preprints, doi:10.31234/osf.io/mnhux.
Intelligence Offloading & Stigmergy: This area examines how embodied agents and multi-agent collectives distribute cognitive tasks into the environment through affordances and stigmergic traces, or informational marks left in the environment.
Alicea, B., Pang, A., and Parent, J. (2023). Intelligence Offloading and the Neurosimulation of Developmental Agents. International Workshop on Embodied Intelligence, IOP Conference Series: Materials Science and Engineering, 1292, 012019.
Optimization and Literary NLP: This smaller cluster applies evolutionary computation and Natural language Processing (NLP) techniques to specific problems, such as Genetic Algorithms with Reversals for flight scheduling and sentiment progression models for searching and indexing literary artefacts.
Grover, A., Yadav, V., and Alicea, B. (2023). Flipping the Switch on Local Exploration: Genetic Algorithms with Reversals. Lecture Notes in Networks and Systems, 608, 719–734.
Kulkarni, H. and Alicea, B. (2021). Sentiment Progression based Searching and Indexing of Literary Textual Artefacts. Lecture Notes in Computer Science, 18201, 264-271.
Open Access and Meta-science: Largely completed in conjunction with other organizations such as eLife and Neuromatch, this area stresses new ways of conducting, disseminating, and accessing scholarship.
Jambor et.al (18 co-authors, 2021). Creating clear and informative image-based figures for scientific publications. PLoS Biology, 19(3), e3001161.
’t Hart et.al (156 co-authors, 2022). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education, 4(44), 118.
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