Matthew A. Smith, PhD

Assistant Professor, Ophthalmology, Bioengineering


914 Eye & Ear Institute
F: 412-647-5880
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PhD, New York University (2003)


Cortical circuits and population codes that underlie visual perception

Research Summary

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.


Rosenbaum R, Smith MA, Kohn A, Rubin JE, Doiron B (2016) The spatial structure of correlated neuronal variability. Nature Neurosci, in press
Cowley BR, Smith MA, Kohn A, Yu BM (2016) Stimulus-driven population activity patterns in macaque primary visual cortex. PLoS Comput Biol, 12(12):e1005185 
Williamson RC, Cowley BR, Litwin-Kumar A, Doiron B, Kohn A, Smith MA*, Yu BM* (2016) Scaling properties of dimensionality reduction for neural populations and network models. PLoS Comput Biol, 12(12):e1005141 [*equal contribution] 
Snyder AC, Morais MJ, Smith MA (2016) Dynamics of excitatory and inhibitory networks are differentially altered by selective attention. J Neurophysiol, 116:1807-1820 
Mayo JP, Morrison RM, Smith MA (2016) A probabilistic approach to receptive field mapping in the frontal eye fields. Front Syst Neurosci, 10: 25 
Vinci G, Ventura V, Smith MA, Kass RE (2016) Separating spike count correlation from firing rate correlation. Neural Comput, 28: 849-881 

Jia X, Smith MA, Kohn A. Flexible relationship between gamma components of the local field potential and spiking activity. J Neurosci, (2011, in press)

Kelly RC, Smith MA, Kass RE, Lee TS. Local field potentials indicate network state and account for neuronal response variability. J Comp Neurosci, 29: 567_579, 2010.

Kohn A, Zandvakili A, Smith MA. Correlations and brain states: from electrophysiology to functional imaging. Curr Opin Neurobiol, 19: 434_438, 2009.

Smith MA, Kohn A. Spatial and temporal scales of neuronal correlation in primary visual cortex. J Neurosci, 28: 12591_12603, 2008.

Kelly RC*, Smith MA*, Samonds JM, Kohn A, Bonds AB, Movshon JA, Lee TS. Comparison of recordings from microelectrode arrays and single electrodes in visual cortex. J Neurosci, 27: 261_264 [*contributed equally to this work], 2007.

Smith MA, Kelly RC, Lee TS. Dynamics of response to perceptual pop-out stimuli in macaque V1. J Neurophysiol, 98: 3436_3449, 2007.

Kohn A, Smith MA. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J Neurosci, 25: 3661_3673, 2005.