Introduction

I build theory-driven computational models of neural circuits and systems "in the loop" with animal behavior and feedback from environmental interaction. Broadly, I seek out the inductive theoretical valleys often shadowed by rising mountains of data, which collectively threaten to detract the capacity of neuroscience to interpret and understand the brain's complexities. My work has centered on spatial cognition and episodic memory and their deeply entangled duality within the hippocampus. From that foundation, my focus has been evolving toward more general theories of timing (e.g., oscillatory phase coding, flexible interareal communication) and dynamics (e.g., transitory attractors, heteroclinic trajectories) in the embodied interactions that underpin the cognitive computations of intelligence.

Basic theoretical research like this is needed, now more than ever, to substantiate the views afforded by the neural data summits and to translate the resulting insights into applicable human neuropsychiatric knowledge of healthy vs. disordered or degenerative brain states. The overarching project is to build models, informatively data-constrained but theory-driven, that explain (or delineate the experiments needed to explain) how temporal and dynamical coordination in brain-body-environment systems enable intelligent agents to act on, and thus perceive, the world.

— Joseph D. Monaco, Ph.D.

Example modeling results with relevant data for comparison. A, My grid-to-place model (Monaco & Abbott, 2011) predicted four scale-based modules in the medial entorhinal cortex (MEC), as corroborated by the Moser lab (NTNU/Norway). B, My biophysical neuron model of sleep vs. wake firing in DN1p clock neurons of Drosophila (Tabuchi, Monaco, et al., 2018) explained spike waveforms (left) and diurnal spike regularity (right). C, My theta-bursting models of what I termed phaser cells (Monaco et al., 2019) demonstrated how a subpopulation of neurons in the lateral septum of rats might produce spatial phase codes that can drive downstream networks to learn arbitrary spatial-phase patterns for stabilizing path integration and other navigational processes.

Selected Publications

• Monaco JD, Hwang GM, Schultz KM, and Zhang K. (2020). Cognitive swarming in complex environments with attractor dynamics and oscillatory computing. Biological Cybernetics, 114, 269–284. doi: 10.1007/s00422-020-00823-z [code]
• Monaco JD, De Guzman RM, Blair HT, and Zhang K. (2019). Spatial synchronization codes from coupled rate-phase neurons. PLOS Computational Biology, 15(1), e1006741. doi: 10.1371/journal.pcbi.1006741 [code] [data]
• Tabuchi M, Monaco JD, Duan G, Bell BJ, Liu S, Zhang K, and Wu MN. (2018). Clock-generated temporal codes determine synaptic plasticity to control sleep. Cell, 175(5), 1213–27. doi: 10.1016/j.cell.2018.09.016
  • [News & Views] Colwell CS and Donlea J. (2018). “Temporal coding of sleep”. Cell, 175(5): 1177–9. doi: 10.1016/j.cell.2018.10.047
• Monaco JD, Rao G, Roth ED, and Knierim JJ. (2014). Attentive scanning behavior drives one-trial potentiation of hippocampal place fields. Nature Neuroscience, 17(5), 725–731. doi: 10.1038/nn.3687 [pdf] [suppinfo] [code]
  • [News & Views] Dupret D and Csicsvari J. (2014). “Turning heads to remember places”. Nature Neuroscience, 17(5), 643–644. doi: 10.1038/nn.3700
  • [F1000 “Exceptional” Rating] Moser E and Rowland D. (May 12, 2014). “This exciting study finds an unexpected relationship between exploratory head scanning behavior and the development of new place fields in the rat hippocampus…” F1000Prime/Faculty Opinions.
  • [F1000 “Exceptional” Rating] Maler L. (April 10, 2014). “This elegant and original study has demonstrated a strong link between the neural activity of hippocampal pyramidal neurons (PNs) during head scanning behavior and their subsequent acquisition of a new place field…” F1000Prime/Faculty Opinions.
• Monaco JD, Knierim JJ, and Zhang K. (2011). Sensory feedback, error correction, and remapping in a multiple oscillator model of place cell activity. Frontiers in Computational Neuroscience, 5:39. doi: 10.3389/fncom.2011.00039 [code]
• Monaco JD and Abbott LF. (2011). Modular realignment of entorhinal grid cell activity as a basis for hippocampal remapping. Journal of Neuroscience, 31(25), 9414–25. doi: 10.1523/JNEUROSCI.1433-11.2011 [code]
  • [F1000 “Very Good” Rating] Giocomo L and Moser E. (June 29, 2011). “This paper presents an interesting computational model which utilizes grid-cell modularity to generate robust remapping…” F1000Prime/Faculty Opinions.
  • [Reviewed by] Giocomo L, Moser M-B, and Moser E. (2011). Computational Models of Grid Cells. Neuron, 71(4), 589–603. doi: 10.1016/j.neuron.2011.07.023

Preprints

• Hadzic A, Hwang GM, Zhang K, Schultz KM, and Monaco JD. (2021). Bayesian optimization of distributed neurodynamical controller models for spatial navigation. ArXiv Preprint. arxiv:2111.00599
• Buckley E, Monaco JD, Schultz KM, Chalmers R, Hadzic A, Zhang K, Hwang GM, and Carr MD. (2021). An interdisciplinary approach to high school curriculum development: Swarming Powered by Neuroscience. ArXiv Preprint. arxiv:2109.05545
• Monaco JD, Rajan K, and Hwang GM. (2021). A brain basis of dynamical intelligence for AI and computational neuroscience. ArXiv Preprint. arxiv:2105.07284
• Levenstein D, Alvarez VA, Amarasingham A, Azab H, Gerkin RC, Hasenstaub A, Iyer R, Jolivet RB, Marzen S, Monaco JD, Prinz AA, Quraishi S, Santamaria F, Shivkumar S, Singh MF, Stockton DB, Traub R, Rotstein HG, Nadim F, and Redish AD. (2020). On the role of theory and modeling in neuroscience. ArXiv Preprint. arxiv:2003.13825
• Monaco JD, Hwang GM, Schultz KM, and Zhang K. (2019). Cognitive swarming in complex environments with attractor dynamics and oscillatory computing. ArXiv Preprint. arxiv:1909.06711
• Wang CH, Monaco JD, and Knierim JJ. (2019). Hippocampal place cells encode local surface texture boundaries. BioRxiv Preprint. doi: 10.1101/764282
• Monaco JD, Blair HT, and Zhang K. (2017). Spatial theta cells in competitive burst synchronization networks: Reference frames from phase codes. BioRxiv Preprint. doi: 10.1101/211458