EHUD KAPLAN, Ph.D.

Information Processing in the Nervous (Visual) System
Jules and Doris Stein Research to Prevent Blindness Professor
Depts. of Neuroscience, Ophthalmology, Physiology & Biophysics

Ph.D.: Syracuse University, NY
Post-Doctoral training: Biophysics Laboratory, The Rockefeller University
email address: ehud.kaplan@mssm.edu

We explore information processing in the nervous system, and use the visual system as a convenient model. We employ systems analysis, borrowed from communication engineering, as the conceptual and methodological framework. We use single and multi-neuron recordings and optical imaging of neuronal activity, and have developed sophisticated techniques for stimulation of the visual system, and for the analysis of neuronal responses to these stimuli. We also record simultaneously the input and output of neurons in the visual thalamic nucleus, the lateral geniculate nucleus (LGN), in order to learn how information transmission from the retina to the visual cortex through the LGN is controlled. Applied mathematics and computers are used extensively for stimulation, analysis and modeling of the systems under study.

Currently we are investigating the properties of visual neurons in the retina, LGN and visual cortex of monkeys and cats, with an emphasis on the temporal, spatial and chromatic selectivities of these cells, and on the nature, source and function of the variability (noise) of neuronal responses. Since perception and behavior involve the coordinated activity of many interacting neurons, we also use optical imaging to visualize the spatio-temporal distribution of activity in neuronal ensembles in the visual cortex. In addition, we are recording from groups of neurons in the LGN and visual cortex, and study their dynamical interractions and the information that these neuronal ensembles carry. The experimental approaches are augmented by computational modeling of LGN neurons and cortical circuits. The topics we are focused on currently include: the representation of color in the primate visual cortex, the transmission of information from retina to cortex via the LGN, and the role of the descending pathways in the brain.

SELECTED PUBLICATIONS:

Kaplan, E. & Shapley, R.M. (1984). The origin of the slow (S) potential in the mammalian lateral geniculate nucleus: Exp Brain Res. 55:111-116.

Kaplan, E. & Shapley, R.M. (1986). The primate retina contains two types of ganglion cells, with high and low contrast sensitivity Proc. of Nat. Acad. Sci. USA. 83:2755-2757.

Benardete, E.A., Kaplan, E. & Knight, B.W. (1992). Contrast gain control in the primate retina: P cells are not X-like, some M cells are. Visual Neurosci. 8:483-486.

Croner, L., Purpura, K. & Kaplan, E. (1993). Response variability in retinal ganglion cells of primates Proc. of Nat. Acad. Sci. USA. 90:8128-8130.

Croner, L. & Kaplan, E. (1995). Receptive Fields of P and M Ganglion Cells Across the Primate Retina, Vision Res. 35:7-24.

Edwards, D. P., Purpura, K. & Kaplan, E. (1995). Contrast sensitivity and spatial frequency response of primate cortical neurons in and around the cytochrome oxidase blobs Vision Res. 35:1501-1523.

Mukherjee, P. & Kaplan, E. (1995). The Dynamics of Neurons in the Cat Lateral Geniculate Nucleus: In Vivo Electrophysiology and Computational Modeling. J. Neurophys. 74:1222-1243.

Sirovich, L., Everson, R., Kaplan, E., Knight, B.W., O'Brien, E.V. & Orbach, D. (1996). Modeling the Functional Organization of the Visual Cortex. Physica D, 96 (1-4):355-366.

Reich, D., Victor, J.D., Knight, B.W., Ozaki, T. & Kaplan, E. (1997). Response Variability and Timing Precision of Neuronal Spike Trains In Vivo J. Neurophysiol. , 77:2936-2841.

Benardete, E. & Kaplan, E. (1997a). The receptive field of the primate P retinal ganglion cell, I: linear dynamics. Visual Neuroscience ,14:169-186.

Benardete, E. & Kaplan, E. (1997b). The receptive field of the primate P retinal ganglion cell, II: nonlinear dynamics. Visual Neuroscience ,14:187-205.

Everson, R.M., Prashanth, AK, Knight,B.W., Sirovich, L., & Kaplan, E. (1998). Representation of spatial frequency and orientation in the visual cortex. Proc. Nat. Acad. Sci., USA. 95:8334-8338.

Mukherjee, P. & Kaplan, E. (1998). The maintained discharge of neurons in the cat lateral geniculate nucleus: spectral analysis and computational modeling. Visual Neuroscience , 15(3):529-539.

Benardete, E. & Kaplan, E. (1999). The dynamics of primate M retinal ganglion cells. Visual Neuroscience, 16(2):355-368.

Gabbay, M., Brennan, C., Kaplan, E. & Sirovich, L. (2000). A Principal Components-Based Method for the Detection of Neuronal Activity Maps: Application to Optical Imaging. NeuroImage, 11:313-325.

Kaplan, E., & Benardete, E. (2001). The dynamics of primate retinal ganglion cells. in: Progress in Brain Reseeach , 134:1-18.

Sirovich, L. & Kaplan, E. (2002). Analysis methods for Optical Imaging. in: Optical Imaging: CRC Reviews, (R. Frostig, Ed.), pp 43-76.

Ozaki, T. & Kaplan, E. (2006). Brainstem input modulates globally the transmission through the lateral geniculate nucleus, Inter. Journal of Neurosci. 116:247-264.

Xiao, Y., Casti, A., Xiao, J. & Kaplan, E. (2007). Hue maps in primate striate cortex. Neuroimage 35(2):771-786.

Casti, A., Hayot, F., Xiao, Y. & Kaplan, E. (2008). A simple model of retina-LGN transmission. J. Comp. Neurosci. 24(2):235-252.

Kaplan, E. (2008). The P, M, and K stream of the primate visual system: What do they do for vision? In: The Senses: a comprehensive reference (R. Masland & T. Albright, Eds); Elsevier, I:370-378.

Xiao, Y., Rao, R., Cecchi, G. & Kaplan, E. (2008). Improved mapping of information distribution across the cortical surface with the Support Vector Machine. Neuroal Networks 21(2-3):341-348. .

OTHER MEMBERS OF THE LABORATORY OF VISUAL NEUROSCIENCE:

Youping Xiao, Ph.D. (Color representation in the brain, Imaging)
Yunguo Yu, MD-Ph.D. (Multi-neuron recordings, information processing in neuronal populations)
Alex Casti, Ph.D. (Cooper Union College; Dynamics of neuronal populations)
Chris Kavanau, Ph.D. student.
Y. Gopher, Ph.D. student
M. Crumiller, Ph.D. student
Larry Sirovich, Ph.D. (Mathematical modeling).
Bruce Knight, (The Rockefeller University) (Neuronal dynamics).
Fernand Hayot, Ph.D. (Simulations of neuronal elements).
Olga Khazanov, Programmer.
Ellen Paley, Program Coordinator (and Chief Angel .)
Affiliated associates:
Andrew Sornborger, Ph.D. (University of Georgia; Methods of signal processing)
Robert Uglesich. Ph.D. (Cooper Union College; Advanced analysis and imaging)

$\{Fig. 1\}$	$\{Fig. 2\}$	${\fig. 3\}$
Optical imaging of ocular dominance columns in monkey visual cortex. Dark bands respond to left eye stimulation; light bands respond to right eye stimulation (From E. O'Brien's Ph.D. thesis).	Left: A map of the primate cortical region that process chromatic information (red/green in this case). Right: Autocorrelation of the image on the left, showing the spatial scale of the chromatic system (From D. Orbach's Ph.D. thesis).	Spatio-Temporal map of a cat LGN receptive field. Each frame shows the response across the RF 6.25 msec after the previous frame.(A. Casti).

$\{Fig. 1\}$ $\{Fig. 2\}$

Construction of a continuous rate function (red line, upper left panel) for a population of identical (uncoupled) neurons. The responses of 128 repeated presentations of a pseudo-random intensity stimulus (lower panels) are converted to a a smooth continuous function by convolving them with cosine bells (Knight et al., 2008). Interval histograms from a cat retinal ganglion cell (red) and from an integrate-and-fire model neuron (blue). Note that after time rescaling, which converts an inhomogeneous Poisson process into a homogeneous one, all the histograms are very similar to each other, regardless of the average firing rate or its derivative (Knight et al., 2008).

To see a short movie that shows some of our imaging data from the mammalian visual cortex (in response to drifting gratings of various orientations), and also illustrating some of the analysis approaches we use, click here. Use VLC or Dragon player in Linux!

$\{Fig. 1\}$	$\{Fig. 2\}$
Construction of a continuous rate function (red line, upper left panel) for a population of identical (uncoupled) neurons. The responses of 128 repeated presentations of a pseudo-random intensity stimulus (lower panels) are converted to a a smooth continuous function by convolving them with cosine bells (Knight et al., 2008).	Interval histograms from a cat retinal ganglion cell (red) and from an integrate-and-fire model neuron (blue). Note that after time rescaling, which converts an inhomogeneous Poisson process into a homogeneous one, all the histograms are very similar to each other, regardless of the average firing rate or its derivative (Knight et al., 2008).

EHUD KAPLAN, Ph.D.

Information Processing in the Nervous (Visual) System Jules and Doris Stein Research to Prevent Blindness Professor Depts. of Neuroscience, Ophthalmology, Physiology & Biophysics

Ph.D.: Syracuse University, NY Post-Doctoral training: Biophysics Laboratory, The Rockefeller University email address: ehud.kaplan@mssm.edu

SELECTED PUBLICATIONS:

OTHER MEMBERS OF THE LABORATORY OF VISUAL NEUROSCIENCE:

Affiliated associates:

Information Processing in the Nervous (Visual) System
Jules and Doris Stein Research to Prevent Blindness Professor
Depts. of Neuroscience, Ophthalmology, Physiology & Biophysics

Ph.D.: Syracuse University, NY
Post-Doctoral training: Biophysics Laboratory, The Rockefeller University
email address: ehud.kaplan@mssm.edu