Nicholas Cain

Providing a bridge between Software Engineering and Research Science. Detail-oriented, and a reliable team player with excellent communication and problem-solving skills, and a proven track record of success. Software Development from DevOps to the customer, and publications from Machine Learning to Neuroscience.

• CalTrain: Training calibrated classification pipelines
• ATEM: Large-capacity embeddings for multilingual BERT
• ECE Sweep: Mitigating Bias in Calibration Error Estimation
• DiPDE: Neural Population Density Simulator (python, lead) http://alleninstitute.github.io/dipde/ -
• AllenSDK: Reading/processing Allen Institute data (python, core contributor)
• PyNWB: Python API for Neurodata Without Borders Tiles (python, core contributor) - https://github.com/NeurodataWithoutBorders/pynwb
• OpenScope Predictive Coding: (Project co-lead) Inspired by Findings presented in Cain et al.
• Detection-of-Change Visual Behavior: (Team Member) Microservice, informatics pipeline.
• Mesoscale Mouse Connectivity: (Team Member) Model of the connections between brain regions.
• Brain Observatory: (Team Member) Data-driven models of cortical responses.
• Modeling SDK: (Team Member) Building, parameterizing, simulating, and visualizing computational models.

• NeuroHackWeek 2018, Seattle, Washington. Accessing the Allen Brain Observatory with PyNWB (August 2018).
• Neurodata without Borders Hackathon (HCK05): Writing/Reading data with PyNWB. (April 2018).
• ESMI 2018 Symposium: Allen Mouse Brain Connectivity Atlas: Data/Tools Showcase. (March 2018).
• SfN 2017, Washington, DC. The Allen Cell Types Database: Introduction to the AllenSDK. (November 2017).
• Invited Seminar, UW Neuroinformatics Working Group. Accessing the Allen Brain Observatory with PyNWB. (November 2017).
• NeuroHackWeek 2017, Seattle, Washington. Allen Institute Data and Resources: An Interactive Tour. (September 2017).
• Neurodata Without Borders Hackathon, Janelia Research Campus, Ashburn, VA. Raw Data to NWB: Case studies and lessons. (July 2017).
• Network Neuroscience 2017, Indianapolis, Indiana. Allen Mouse Brain Connectivity Atlas: Data/Tools Showcase. (June 2017).
• Invited Seminar: Population density modeling with DiPDE. University of Washington. Seattle, WA (November 2016).
• Collaborative Development of Data-Driven Models of Neural Systems Conference, Janelia Research Campus, Ashburn, VA.
• Circuit-scale population density modeling with DiPDE. (September 2016).
• Invited Seminar: Large-scale population density modeling with DiPDE. Aix-Marseille Université, Marseille, France (February 2016).
• EITN Workshop on population models and mean-field approaches to in vivo brain activity states, Paris, France. DiPDE: Population density modeling in python. (February 2016).
• Human Brain Project CodeJam 2016, Manchester, England. DiPDE: A simulator for population density modeling. (January 2016)
• Invited Seminar: A Team Science Approach for Unraveling the Brain. University of Wisconsin, Madison. Madison, Wisconsin (October 2015)
• CNS*2015, Prague, Czech Republic. Resources for Open Collaboration at the Allen Brain Institute. (July 2015)
• “Are we building the right thing? Requirements from theory for simulation environments and neuromorphic computing”. Network modeling and simulation requirements at the Allen Institute. EITN, Paris, France (March 2015)
• Computational Neuroscience Connection, University of Washington, Seattle, WA. Towards a multi-scale model of the mouse visual system. (September 2013).
• MAA Pacific Northwest Section Meeting, Willamette University, Salem, OR. A Hybrid Statistical and Point-Neuron Simulation Environment for Neural Simulation (April 2013).
• Invited Seminar: Mathematical modeling of the mouse visual system: a multi-scale approach. Seattle University, Seattle, WA (February 2013).
• MAA Pacific Northwest Section Meeting, Seattle University, Seattle WA. Teaching mathematics with a strip of paper. (April 2010).
• Society for Neuroscience Meeting, Chicago IL. Robust Neural Integration: Impact on decision making. (October 2009).

• Roelofs, B. Cain, N., Shlens, J., Mozer, M. Mitigating Bias in Calibration Error Estimation. ICML 2021 (submitted).
• Dai, K. et al. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLOS Computational Biology. 2020.
• Buice, de Vries, et al. A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex. Nature Neuroscience. (2020).
• Cain, N., Iyer, R. Koch, C. and Mihalas, S. The Computational Properties of a Simplified Cortical Column Model. PLoS Comp. Biol. (2016).
• Oh, S.W., Harris, J.A., Ng, L. Ng, Winslow, B., Cain, N., et al. A mesoscale connectome of the mouse brain. Nature (2014).
• Stafford, J., Jarrett, B., et al. Large-scale topology and the default mode network in the mouse connectome. PNAS (2014).
• Cain, N. and Shea-Brown, E. Impact of correlated neural activity on decision making performance. Neural Computation (2013).
• Cain, N., Barreiro, A., Shadlen, M., and Shea-Brown, E. Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity. Journal of Neurophysiology (2013)
• Cain, N. and Shea-Brown, E. Computational models of decision making: integration, stability, and noise. Curr. Opin. Neurobiol. (2012)
• Teeter, C. et al., Generalized Leaky Integrate-And-Fire Models Classify Multiple Neuron Types. Nature Communications (2018)
• Hu, Y., Brunton, S., Cain, N., Mihalas, S., Kutz, N., Shea-Brown, E., Feedback through graph motifs relates structure and function in complex networks. (Physical Review E).
• Arkhipov et al. Visual physiology of the Layer 4 cortical circuit in silico. PLoS computational biology. (2018)
• Gratiy et al. BioNet: a Python interface to NEURON for large-scale network simulations. PLoS ONE. (2018)
• Cain, N. Statistical, Stochastic, and Dynamical Models of Neural Decision Making (Dissertation, advisor: Dr. Eric Shea-Brown), University of Washington. (2012).
• Cain, N., and Heyer, L. Committee Machine Motif Identification: Sequence Motif Recognition Using Artificial Neural Networks (Honors Thesis, advisor: Dr. Laurie Heyer) Davidson College. (2006).