Click here to see the NeuronC User's Manual. Click here to receive the NeuronC distribution.
Understanding how the structure, biophysical properties, and synaptic
connections of a neuron influence its signal processing function within the
surrounding neural circuit. |
RESEARCH TECHNIQUES: Analysis of the role of synaptic and biophyical properties, morphology, and noise in a neural circuit's performance. Electrophysiological recordings. Computer simulation of neural circuitry. Ideal observer analysis of neural circuit performance.
RESEARCH SUMMARY: My laboratory studies how retinal circuitry processes visual signals. We analyze what is known about a circuit, construct a biophysically realistic model of it, and through simulation attempt to reconcile the circuit's known physiological properties with the function of its neural components. This allows us to suggest a functional interpretation for biophysical features such as dendritic branching, density of voltage-gated membrane channels, and specific location, strength, and properties of synaptic inputs. Including the noise properties of membrane channels and synaptic vesicle release, we generate realistic noise properties that we compare directly with recordings from live neurons. We currently study 5 circuits: 1) the cone photoreceptor to horizontal cell network; 2) signal transmission in the cone bipolar cells, including their feedback from amacrine cells; 3) the pathway from rod photoreceptors to ganglion cells used during dark adaptation; 4) the brisk-transient (alpha) ganglion cell, its spike generator, and its presynaptic circuitry; and 5) the direction-selective ganglion cell and the starburst amacrine network that shapes its responses.
Former lab members:Mark Van Rossum worked on the rod bipolar and beta ganglion cell models.
Sneha Ravi worked on the ephaptic synapse model.
Xiao Li worked on the asymmetrical DSGC model.
Tony Tan worked on the ephaptic synapse model.
James Guevara worked on the ephaptic synapse model.
Richard Yim worked on the contrast sensitivity for stimuli in the On and Off ganglion cell models.
Umar Sheikh worked on the signal to noise ratio of the dendrites and soma of the DSGC.
Aubrey Moat and Sergey Kuznetsov worked on bipolar cell reconstructions and models.
Other vision researchers at Penn with similar interests:
Stincic T, Smith RG, Taylor WR (2016) Time-course of EPSCs in On-type starburst amacrine cells in mouse retina is independent of dendritic location. J. Physiol. (2016), doi: 10.1113/jp272384.
Lipin MY, Taylor WR, Smith RG. (2015) Inhibitory Input to the Direction Selective Ganglion Cells Is Saturated at Low Contrast. J. Neurophysiol. (2015) 114:927-941. doi: 10.1152/jn.00413.2015.
Trenholm S, McLaughlin AJ, Schwab DJ, Turner MH, Smith RG, Rieke F, Awatramani GB. (2014) Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations. Nat Neurosci.17(12):1759-66. doi: 10.1038/nn.3851.
Puthussery T, Venkataramani S, Gayet-Primo J, Smith RG, Taylor WR (2013) NaV1.1 Channels in axon initial segments of retinal bipolar cells augment input to magnocellular visual pathways. J Neurosci 33(41): 16045-16059, doi: 10.1523/jneurosci.1249-13.2013.
Abbas SY, Hamade KC, Yang EJ, Nawy S, Smith RG, Pettit DL (2013) Directional Summation in non-Direction Selective Retinal Ganglion Cells. PLoS Comput Biol. 9(3):e1002969. doi: 10.1371/journal.pcbi.1002969.
Nikonov SS, Lyubarsky A, Fina ME, Nikonova ES, Sengupta A, Chinniah C, Ding X-Q, Smith RG, Pugh EN Jr, Vardi N, Dhingra A. (2013) Cones Respond to Light in the Absence of Beta Transducin Subunit, J Neurosci 33:5182-5194.
Taylor WR, Smith RG (2012) The role of starburst amacrine cells in visual signal processing, Visual Neuroscience 29:73-81.
Trenholm S, Johnson K, Li X, Smith RG, Awatramani GB (2011) Parallel Mechanisms Encode Direction in the Retina, Neuron 71:683-694 DOI 10.1016/j.neuron.2011.06.020
Schachter MJ, Oesch N, Smith RG, Taylor WR. (2010) Dendritic Spikes Amplify the Synaptic Signal to Enhance Detection of Motion in a Simulation of the Direction-Selective Ganglion Cell. PLoS Comput Biol 6(8): e1000899. doi:10.1371/journal.pcbi.1000899.
Lipin MY, Smith RG, Taylor WR. (2010) Maximizing contrast resolution in the outer retina of mammals. Biol Cybern. 103:57-77. Epub 2010 Apr 2.
Borghuis, B.G., Sterling, P. and Smith, R.G. (2009) Loss of sensitivity in an analog neural circuit. J Neurosci 29: 3045-3058.
Yin L, Smith, R.G., Sterling, P. and Brainard D.H (2009) Physiology and morphology of color-opponent ganglion cells in a retina expressing a dual gradient of S and M opsins. J Neurosci 29: 2706-2724.
Xu, Y, Sulaiman, P, Fedderson, R., Liu, J., Smith, R.G. and Vardi, N. (2008) Retinal On- bipolar cells express a new PCP-2 splice variant that accelerates the light response. J Neurosci 28:8873-8884.
Borghuis BG, Ratliff CP, Smith RG, Sterling P,
Balasubramanian V. (2008) Design of a neuronal array. J Neurosci 28:3178-3189.
Yin L, Smith RG, Sterling P, Brainard DH (2006) Chromatic properties of horizontal and ganglion cell responses follow a dual gradient in cone opsin expression. J Neurosci 26:12351-12361.
Dhingra NK, Freed, MA, Smith RG (2005) Voltage-gated sodium channels improve contrast sensitivity of a retinal ganglion cell. J Neurosci 25:8097-8103.
Xu Y, Dhingra NK, Smith RG, Sterling P (2005) Sluggish and brisk ganglion cells detect contrast with similar sensitivity. J Neurophsiol 93:2388-2395.
Berntson A, Smith RG, Taylor WR (2004) Postsynaptic calcium feedback between rods and rod bipolar cells in the mouse retina. Visual Neurosci 21:913-924.
Berntson A, Smith RG, Taylor WR (2004) Transmission of single photon signals through a binary synapse in the mammalian retina. Visual Neurosci 21:693-702.
Taylor WR, and Smith RG (2004) Transmission of scotopic signals from the rod to rod-bipolar cell in the mammalian retina. Vision Res. (2004) 44: 3269-3276.
Tukker JJ, Taylor WR, and Smith, RG. (2004) Direction selectivity in a model of the starburst amacrine cell. Visual Neurosci. (2004)21: 611-625.
Dhingra NK, and Smith RG (2004) Spike generator limits efficiency of information transfer in a retinal ganglion cell J. Neurosci. (2004) 24: 2914-2922.
Freed, M.A., Smith, R.G., and Sterling, P. (2003) Timing of quantal release from the retinal bipolar terminal is regulated by a feedback circuit. Neuron, 38: 89-101.
van Rossum, M.C.W., O'Brien, B., and Smith, R.G. (2003) Effects of noise on the spike timing precision of retinal ganglion cells. J. Neurophysiol. 89:2406-2419.
Dhingra, N.K., Kao, Y.-H., Sterling, P., and Smith, R. (2003) Contrast treshold of a brisk-transient ganglion cell in vitro. J. Neurophysiol. 89:2360-2369.
DeVries S, Qi X-F, Smith R, Makous W, and Sterling, P. (2002) Electrical coupling enhances contrast sensitivity of foveal cones. Current Biology 12:1900-1907.
Hsu, A., Smith, R.G., Buchsbaum, G., and Sterling, P. (2000) Cost of coupling to trichomacy in foveal cones. J Optical Soc. Am. A 17:(3) 635-640.
van Rossum, M.C.W., and Smith, R.G. (1998) Noise removal at the rod synapse of mammalian retina. Visual Neurosci., 15: 809-821.
Hsu, A., Tsukamoto, Y., Smith, R.G., and Sterling, P (1998) Functional architecture of primate cone and rod axons. Vision Research 38: 2539-2549.
Vardi, N. and Smith, R.G. (1996) The AII amacrine array: coupling can increase correlated activity. Vision Res. 36: 3743-3757.
Smith, R.G. (1995) Simulation of an anatomically-defined local circuit: the cone-horizontal cell network in cat retina. Visual Neurosci., 12: 545-561.
Smith, R.G., and Vardi, N. (1995) Simulation of the AII amacrine cell in cat retina: Functional consequences of electrical coupling and regenerative membrane properties. Visual Neurosci., 12: 851-860.
Hsu, A., and Smith, R.G. (1994) Simulating the foveal cone receptive field. In: Computation in Neurons and Neural Systems, Ed. by Frank H. Eeckman. Kluwer Academic Publishers, Boston.
Smith, R.G. (1994) Measurement of simulation speed: its relation to simulation accuracy. In: Computation in Neurons and Neural Systems, Ed. by Frank H. Eeckman. Kluwer Academic Publishers, Boston.
Freed, M.A., Smith, R.G., and Sterling, P. (1992) Computational model of the on-alpha ganglion cell receptive field based on bipolar cell circuitry. Proc. Nat. Acad. Sci. 89: 236-240.
Smith, R.G.(1992) NeuronC: a computational language for investigating functional architecture of neural circuits. J. Neurosci. Meth 43: 83- 108.
Sterling, P., Cohen, E., Smith, R.G., and Tsukamoto, Y. (1992) Retinal circuits for daylight: why ballplayers don't wear shades. In: Analysis and Modeling of Neural Systems, Ed. by Frank H. Eeckman. Kluwer Academic Publishers.
Tsukamoto, Y., Smith, R.G., and Sterling, P. (1990) "Collective coding" of correlated cone signals in the retinal ganglion cell. Proc. Nat. Acat. Sci. 87: 1860-1864.
Smith, R.G., and Sterling, P. (1990) Cone receptive field in cat retina computed from microcircuitry. Visual Neurosci. 5: 453-461.
Sterling, P., Freed, M.A., and Smith, R.G. (1988) Architecture of the rod and cone circuits to the On-beta ganglion cell. J. Neurosci. 8:623- 642.
Smith, R.G. (1987) Montage: a system for three-dimensional reconstruction by personal computer. J. Neurosci. Meth. 21:55-69.
Sterling, P., Cohen, E., Freed, M.A., and Smith, R.G. (1987) Microcircuitry of the on-beta ganglion cell in daylight, twilight and starlight. Neurosci. Research, Suppl. 6, S269-S285.
Freed, M.A., Smith, R.G., and Sterling, P. (1987) Rod bipolar array in the cat retina: pattern of input from rods and GABA-accumulating amacrine cells. J. Comp. Neurol. 266: 445-455.
Smith, R.G., Freed, M.A., and Sterling, P. (1986) Microcircuitry of the dark-adapted cat retina: Functional architecture of the rod-cone network. J. Neurosci 6:3505-3517.
Sterling, P., Freed, M.A., and Smith, R.G. (1986) Microcircuitry and functional architecture of the cat retina. Trends in Neurosci. 9:186-192.
Kauffman, S.A., and Smith, R.G. (1986) Adaptive automata based on darwinian selection. Physica 22D:68-82.
Smith RG, Taylor WR (2013) Dendritic Computation of Direction in Retinal Neurons. In Eds: Cuntz H, Remme MWH, Torben-Nielsen B The Computing Dendrite: From Structure to Function, Springer Series in Computational Neuroscience, ISBN-13: 978-1461480938
Taylor WR, Smith RG. (2012) The role of starburst amacrine cells in visual signal processing. Vis Neurosci. 29(1):73-81.
Taylor WR, Smith RG. (2011) Trigger features and excitation in the retina, Curr Opin Neurobiol, 21:672-678. doi:10.1016/j.conb.2011.07.001
Smith, R.G. (20010b) Rod photoreceptor cells: soma and synapse. In Eds, Besharse J, Dana R, and Dartt DA, et al, Encyclopedia of the Eye, 4-volume set, May, 2010, Academic Press, ISBN-13: 9780123741981.
Smith, R.G. (20010a) Cone photoreceptor cells: soma and synapse. In Eds, Besharse J, Dana R, and Dartt DA, et al, Encyclopedia of the Eye, 4-volume set, May, 2010, Academic Press, ISBN-13: 9780123741981.
Smith, R.G. and Dhingra, N.K. (2009) Ideal observer analysis of signal quality in retinal circuits. Prog Ret Eye Research. 28:263-288. Epub 2009 May 13.
Smith, R.G. (2008) Contributions of Horizontal Cells. In: Allan I. Basbaum, Akimichi Kaneko, Gordon M. Shepherd, and Gerald Westheimer (Editors) The Senses: A Comprehensive Reference, Vol 1, Vision I, Richard Masland, and Thomas D Albright, Eds. San Diego, Academic Press, p 348-350.
Dhingra NK, Smith R, Sterling P (2003) Psychophysics to biophysics: how perception depends on circuits, synapses, and vesicles. In: A. Kaneko (Ed) The Neural Basis of Early Vision. Keio University International symposia for Life Sciences and Medicine, Springer-Verlag, Tokyo, Vol. 11.
Smith, R.G. (2003) Retina. In: Michael A. Arbib (Ed.): The Handbook of Brain Theory and Neural Networks (second ed). MIT Press.
Smith RG, Dhingra NK, Kao YH, Sterling P (2001) How efficiently a ganglion cell codes the visual signal. Proc. IEEE Eng. Med. Biol. Soc., IEEE, Piscataway, NJ, Vol. 1, pp 663-665.
Sterling, P., Smith, R.G., Rao, R., and Vardi, N. (1995) Functional architecture of mammalian outer retina and bipolar cells. In: Archer S., Djamgoz, M.B.A., and Vallerga, S. (Eds.): Neurobiology of the vertebrate outer retina. Chapman & Hall, Ltd., London.
Smith, R.G. Neuron-C - A simulation language for testing hypotheses about neural performance.