About
I am a Postdoctoral Research Scientist at the T.J. Watson Research Center, IBM Research. I was previously a Swartz Fellow in Theoretical Neuroscience at Yale University working with John Murray. I completed my PhD in Neuroscience at Rutgers University with Michael Cole in 2020, and my BA in Mathematics at Washington University in St. Louis in 2013.
I live in NYC.
Research Interest
My research is in computational neuroscience and artificial intelligence. More recently, my interests lie in understanding how symbolic computations can be learned and executed in neural systems (primarily artificial neural networks). In the past, I have used neural data analysis and computational modeling to study the neural basis of flexible (e.g., multitask) cognition in humans and non-human primates.
Education
Rutgers University
PhD (2015-2020)
Neuroscience
Dissertation: Cognitive Information Transformation in Functional Brain Networks
Washington University in St. Louis
BA (2009-2013)
Majors: Mathematics, Philosophy-Neuroscience-Psychology
Minor: Computer Science
Selected publications
Ito T, Murray JD (2023). Multitask representations in the human cortex transform along a sensory-to-motor hierarchy. Nature Neuroscience. https://www.nature.com/articles/s41593-022-01224-0
Ito T, Klinger T, Schultz DH, Murray JD, Cole MW, Rigotti M (2022). Compositional generalization through abstract representations in human and artificial neural networks. Advances in Neural Information Processing Systems (NeurIPS). https://openreview.net/forum?id=177GzUAds8U
Ito T, Yang GR, Laurent P, Schultz DH, Cole MW (2022). Constructing neural network models from brain data reveals representational transformations underlying adaptive behavior. Nature Communications. https://doi.org/10.1038/s41467-022-28323-7
Ito T, Brincat SL, Siegel M, Mill RD, He BJ, Miller EK, Rotstein HG, Cole MW (2020). Task-evoked activity quenches neural correlations and variability across cortical areas. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1007983
Ito T, Kulkarni KR, Schultz DH, Mill RD, Chen RH, Solomyak LI, & Cole MW (2017). Cognitive task information is transferred between brain regions via resting-state network topology. Nature Communications. https://www.nature.com/articles/s41467-017-01000-w.
Publications
Preprint papers
Cocuzza CV, Sanchez-Romero R, Ito T, Mill RD, Keane BP, Cole MW (2022). Distributed network processes account for the majority of variance in localized visual category selectivity. bioRxiv. https://www.biorxiv.org/content/10.1101/2022.02.19.481103
Peer-reviewed papers
2024
Ito T, Dan S, Rigotti M, Kozloski J, Campbell M (2024). On the generalization capacity of neural networks during generic multimodal reasoning. International Conference on Learning Representations (ICLR). http://arxiv.org/abs/2401.15030.
Ito T, Murray JD (In press). The impact of functional correlations on task information coding. Network Neuroscience. https://www.biorxiv.org/content/10.1101/2022.11.23.517699
2023
Sanchez-Romero R, Ito T, Mill RD, Hanson SJ, Cole MW (2023). Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2023.120300
Ito T, Murray JD (2023). Multitask representations in the human cortex transform along a sensory-to-motor hierarchy. Nature Neuroscience. https://www.nature.com/articles/s41593-022-01224-0
2022
Ito T, Klinger T, Schultz DH, Murray JD, Cole MW, Rigotti M (2022). Compositional generalization through abstract representations in human and artificial neural networks. Advances in Neural Information Processing Systems (NeurIPS). https://openreview.net/forum?id=177GzUAds8U
Ito T, Yang GR, Laurent P, Schultz DH, Cole MW (2022). Constructing neural network models from brain data reveals representational transformations underlying adaptive behavior. Nature Communications. https://doi.org/10.1038/s41467-022-28323-7
*McCormick EM, *Arnemann KL, Ito T, Hanson SJ, Cole MW (2022). Latent functional connectivity underlying multiple brain states. Network Neuroscience. https://doi.org/10.1162/netn_a_00234
Schultz DH, Ito T, Cole MW (2022). The human brain’s intrinsic network architecture is organized to represent diverse cognitive task information. Cerebral Cortex. https://doi.org/10.1093/cercor/bhab495
2021
Cole MW, Ito T, Cocuzza CV, Sanchez-Romero R (2020). The functional relevance of task-state functional connectivity. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.1713-20.2021
Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW (2021). A whole-brain and cross-diagnostic perspective on functional brain network dysfunction. Cerebral Cortex. https://doi.org/10.1093/cercor/bhaa242
2020
Ito T, Hearne LJ, Cole MW (2020). A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.117141
Cocuzza CV, Ito T, Schultz DH, Bassett DS, Cole MW (2020). Flexible coordinator and switcher hubs for adaptive task control. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.2559-19.2020
Ito T, Brincat SL, Siegel M, Mill RD, He BJ, Miller EK, Rotstein HG, Cole MW (2020). Task-evoked activity quenches neural correlations and variability across cortical areas. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1007983
Kar K, Ito T, Cole MW, Krekelberg B (2020). Transcranial alternating current stimulation reduces BOLD adaptation and increases functional connectivity. Journal of Neurophysiology. https://doi.org/10.1152/jn.00376.2019
2019
Ito T, Hearne LJ, Mill RD, Cocuzza CV, Cole MW (2019). Discovering the Computational Relevance of Brain Network Organization. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2019.10.005
Cole MW, Ito T, Schultz DH, Mill RD, Chen RH, Cocuzza CV (2019). Task activations produce spurious but systematic inflation of task functional connectivity estimates. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.12.054
Schultz DH, Ito T, Solomyak LI, Chen RH, Mill RD, Kulkarni KR, Cole MW (2019). Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in undiagnosed individuals. Network Neuroscience. doi:10.1101/185306
2018
Chen RH, Ito T, Kulkarni KR, Cole MW. (2018). The human brain traverses a common activation-pattern state space across task and rest. Brain Connectivity. https://doi.org/10.1089/brain.2018.0586
2017
Ito T, Kulkarni KR, Schultz DH, Mill RD, Chen RH, Solomyak LI, & Cole MW (2017). Cognitive task information is transferred between brain regions via resting-state network topology. Nature Communications. https://www.nature.com/articles/s41467-017-01000-w.
Mill RD, Ito T, Cole MW (2017). From connectome to cognition: The search for mechanism in human functional brain networks. NeuroImage. http://dx.doi.org/10.1016/j.neuroimage.2017.01.060
2016
Cole MW, Ito T, Bassett DS, & Schultz DH (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience. doi:10.1038/nn.4406
2015
Cole MW, Ito T, & Braver TS (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497–504. http://doi.org/10.1089/brain.2015.0357
Cole MW, Ito T, & Braver TS (2015). The behavioral relevance of task information in human prefrontal cortex. Cerebral Cortex (New York, N.Y. : 1991), bhv072–. http://doi.org/10.1093/cercor/bhv072
Conference papers
Ito T, Keane BP, Mill RD, Chen RH, Hearne LJ, Arnemann KL, He BJ, Rotstein HG, Cole MW (September 2018). A dynamical systems model of intrinsic and evoked activity, variability, and functional connectivity. Conference on Cognitive Computational Neuroscience, Philadelphia PA. https://doi.org/10.32470/CCN.2018.1049-0
Cole MW, Ito T (September 2017). Computational network mechanisms of task-evoked functional connectivity. Conference on Cognitive Computational Neuroscience, New York NY. https://ccneuro.org/2017/abstracts/abstract_3000163.pdf
Conference abstracts
2022
Ito T, Murray JD (June 2022). Multi-task representations in human cortex transform along a sensory-to-motor hierarchy. Organization for Human Brain Mapping, Glasgow Scotland.
Ito T, Murray JD (April 2022). Multi-task representations in human cortex transform along a sensory-to-motor hierarchy. From Neuroscience to Artificially Intellignent Systems (NAISys), Cold Spring Harbor Laboratory.
Ito T, Murray JD (March 2022). Multi-task representations in human cortex transform along a sensory-to-motor hierarchy. Poster at Computational and Systems Neuroscience (Cosyne), Lisbon, Portugal.
2021
Ito T, Klinger T, Schultz DH, Cole MW, Rigotti M (February 2021). The role of compositional abstraction in human and artificial neural networks. Poster at Computational and Systems Neuroscience (Cosyne).
2020
Ito T, Hearne LJ, Cole MW (June 2020). Cognitive information differentiates between connectivity and activity across the cortical hierarchy. Poster and talk presented at Organization for Human Brain Mapping (Virtual).
2019
Ito T, Yang GR, Cocuzza CV, Schultz DH, Cole MW (June 2019). Predicting motor behavior using neural encoding models during complex cognitive tasks. Poster to be presented at Organization for Human Brain Mapping, Rome, Italy.
Ito T & Schultz KM (February 2019). Computation across scales: From receptive fields to cognitive maps. Poster presented at Present and Future Frameworks of Theoretical Neuroscience, San Antonio TX.
2018
Ito T, Rotstein HG, Cole MW (July 2018). A dynamical systems model of intrinsic and evoked activity, variability, and functional connectivity. Poster presented at Neurobiology of Cognition Gordon Research Conference, Newry Maine.
Ito T, Cole MW (June 2018). Dimensionality of intrinsic network connectivity underlies flexible task representation. Poster presented at Organization for Human Brain Mapping, Singapore.
2017
Ito T, Cole MW. (November 2017). Cognitive control networks contain a mixture of diverse connectivity patterns characteristic of predicted flexible hub mechanisms. Poster presented at Society for Neuroscience, Washington DC.
Rotstein HG, Ito T, Stark E. (November 2017). Inhibition-based theta spiking resonance in a hippocampal network. Poster presented at Society for Neuroscience, Washington DC.
Schultz DH, Ito T, Solomyak LI, Chen RH, Mill RD, Kulkarni KR, Cole MW. (November 2017). Systematic flexibility of global functional connectivity patterns supports flexible cognitive control. Society for Neuroscience, Washington DC.
Cole MW, Ito T, Schultz DH, Mill RD. (March 2017). Activity flows over task-evoked networks shape cognitive task activations across task switches. Poster presented at Cognitive Neuroscience Society, San Francisco, CA.
2016
Ito T, Schultz DH, Solomyak LI, Chen RH, Mill RD, Cole MW. (November 2016). Cognitive control networks route task information to other networks via intrinsic functional connectivity pathways. Society for Neuroscience, San Diego, CA.
Schultz DH, Ito T, Solomyak LI, Chen RH, Mill RD, Kulkarni KR, Cole MW. (November 2016). Cognitive control network global connectivity is related to the mental health of healthy individuals. Society for Neuroscience, San Diego, CA.
Ito T, Schultz DH, Solomyak LI, Chen RH, Mill RD, Cole MW. (October 2016). Resting-state network topology shapes task information transfer in the human brain. Dynamical Systems and Data Analysis in Neuroscience: Bridging the Gap, Mathematical Biosciences Institute, The Ohio State University, Columbus, OH.
Ito T, Schultz DH, Solomyak LI, Chen RH, Mill RD, Cole MW. (August 2016). Intrinsic functional connectivity shapes task information between networks. Neural Computation and Psychology Workshop, Philadelphia, PA.
Ito T, Schultz DH, Solomyak LI, Chen RH, Mill RD, Cole MW. (April 2016). Flexible hub updates between tasks associated with global informational connectivity changes. Cognitive Neuroscience Society, New York, NY.
Cole MW, Schultz DH, Chen RH, Kulkarni KR, Ito T. (April, 2016). The cognitive relevance of resting-state fMRI: Spontaneously organized networks and brain states across rest and task. Cognitive Neuroscience Society, New York, NY.