Matthew A. Smith, PhD

  • Adjunct Associate Professor, Neuroscience




Personal Website

website link

Education & Training

PhD, New York University (2003)

Campus Address

115 Mellon Institute

One-Line Research Description

Cortical circuits and population codes that underlie visual perception

I am interested in how our visual perception of the world is constructed from the activity of populations of neurons. My laboratory employs neurophysiological and computational approaches to this problem, integrating cognitive phenomena, such as attention and memory, with computational analyses, neuroanatomy and circuitry, and motor planning and action. Specifically, we simultaneously record from dozens of individual neurons in visual cortex and relate the activity we observe in cortical circuitry to experimental manipulations of visual perception.

My research has revealed that measurement of the local circuitry in the visual cortex is critical for understanding the building blocks of visual processing, both within and across brain regions. We found that functional connections between neurons vary depending on the visual stimulus, the distance between neurons, the temporal scale, and the cortical layer. We are currently exploring a number of questions, including: (1) How interactions between cortical regions influence neural populations, such as feedback from prefrontal areas to visual cortex; (2) How functional connections among neurons are modulated by the animal's task, such as planning a saccade to different regions of the visual field; (3) How information flow among neurons is altered within and between cortical lamina based on cognitive demands; (4) How cortical circuitry is altered with abnormal visual experience (such as in amblyopia or glaucoma), and how a better understanding of cortical circuitry might lay the foundation for cortical visual prosthetic devices.

Representative Publications

Williamson RC, Doiron B, Smith MA, Yu BM. Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. Curr Opin Neurobiol. 2019 Apr;55:40-47. doi: 10.1016/j.conb.2018.12.009. Epub 2019 Jan 22. Review. PubMed PMID: 30677702; PubMed Central PMCID: PMC6548625.


Distinct population codes for attention in the absence and presence of visual stimulation. Snyder AC, Yu BM, Smith MA. Nature communications. 2018; 9(1):4382.


What does scalp electroencephalogram coherence tell us about long-range cortical networks? Snyder AC, Issar D, Smith MA. The European journal of neuroscience. 2018; 48(7):2466-2481.


The spatial structure of correlated neuronal variability. Rosenbaum R, Smith MA, Kohn A, Rubin JE, Doiron B. Nature neuroscience. 2017; 20(1):107-114. NIHMSID: NIHMS821164


Cowley BR, Smith MA, Kohn A, Yu BM. Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex.PLoS Comput Biol. 2016 Dec;12(12):e1005185. doi: 10.1371/journal.pcbi.1005185. eCollection 2016 Dec. PubMed PMID: 27935935; PubMed Central PMCID: PMC5147778. 5.


Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. Williamson RC, Cowley BR, Litwin-Kumar A, Doiron B, Kohn A, Smith MA, Yu BM. PLoS computational biology.2016; 12(12):e1005141.


Dynamics of excitatory and inhibitory networks are differentially altered by selective attention. Snyder AC, Morais MJ, Smith MA. Journal of neurophysiology. 2016; 116(4):1807-1820.

All Publications >