Nicholas Cain, Ph.D.
Software Engineer III
Nicholas Cain is a scientist in the Allen Institute's neural coding program, where he develops mathematical models of neural dynamics that aim to discover how neural systems process information about the environment. By applying mathematical theories of network dynamics and processing to the quantitative data collected at the Allen Institute, he hopes to link theory and experiment through detailed mathematical models of neural function. His research interests include theoretical models of decision-making and accumulation of sensory evidence by circuits involved in short-term working memory. Cain received a B.S. in mathematics from Davidson College, and a M.S. and Ph.D. in applied mathematics from the University of Washington.
Research
Research Interests
Research Interests Since joining the modeling, analysis, and theory group at the Allen Institute in 2012, I have contributed to two main Institute-wide platforms. The first, ongoing, contribution is to the Allen Mouse Brain Connectivity Atlas, where I helped formulate reduced models of brain connectivity based on raw fluorescence data, and began a graph theoretic analysis of the whole mouse brain connectivity structure. I have also worked with other members of the modeling team to construct a large-scale population density modeling framework (DiPDE) capable of efficiently simulating the voltage distribution of coupled neuronal populations. These two projects converge in a broader project, in which whole-brain connectivity data are being used to parameterize large-scale neuronal simulations.
Expertise
- Computational neuroscience
- Numerical analysis
- Mathematical modeling
Research Programs
- Neural coding
Selected Publications View on PUBMED
BioNet: A Python interface to NEURON for modeling large-scale networks
PLOS | ONE
August 2, 2018
Gratiy SL, Billeh YN, Dai K, Mitelut C, Feng D, Gouwens NW, Cain N, Koch C, Anastassiou CA, Arkhipov A
bioRxiv
June 29, 2018
de Vries S, Lecoq J, Buice MA, Groblewski PA, Ocker GK, Oliver M, Feng F, Cain N, Ledochowitsch P, Millman D, Roll K, Garrett M, Keenan T, Kuan L, Mihalas S, Olsen S, Thompson C, Wakeman W, Waters J, Williams D, Barber C, Berbesque N, Blanchard B, Bowles N, Caldejon S, Casal L, Cho A, Cross S, Dang C, Dolbeare T, Edwards M, Galbraith J, Gaudreault N, Griffin F, Hargrave P, Howard R, Huang L, Jewell S, Keller N, Knoblich U, Larkin J, Larsen R, Lau C, Lee E, Lee F, Leon A, Li L, Long F, Luviano J, Mace K, Nguyen T, Perkins J, Robertson M, Seid S, Shea-Brown E, Shi J, Sjoquist N, Slaughterbeck C, Sullivan D, Valenza R, White C, Williford A, Witten D, Zhuang J, Zeng H, Farrell C, Ng L, Bernard A, Phillips JW, R Reid C, Koch C
Generalized leaky integrate-and-fire models classify multiple neuron types
Nature Communications
February 19, 2018
Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A, Cain N, Zeng H, Hawrylycz M, Koch C, Mihalas S
Visual physiology of the Layer 4 cortical circuit in silico
PLoS Computational Biology
November 12, 2018
Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-LLavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C
Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity
Journal of Neurophysiology
May 2013
Cain N, Barreiro Ak, Shadlen M, Seah-Brown E
Impact of correlated neural activity on decision-making performance
Neural Computation
February 2013
Cain N, Shea-Brown E
Computational models of decision making: integration, stability, and noise
Current Opinion in Neurobiology
December 2012
Cain N, Shea-Brown E