Staff Profiles

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 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.


  • 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

August 2, 2018

Gratiy SL, Billeh YN, Dai K, Mitelut C, Feng D, Gouwens NW, Cain N, Koch C, Anastassiou CA, Arkhipov A

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

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