Research Area: Computational Neuroscience.
Computational Neuroscience Laboratory, Professor Patricio Orio.
Ph.D. in Biomedical Engineering, Duke University, USA (2016).
Master degree in Biomedical Engineering, Universidad de Chile, Chile (2006).
Bachelor in Electrical Engineering, Universidad de Chile, Chile (2004)
Fax: (56)-(32)-250 8047
Address: Centro Interdisciplinario de Neurociencia de Valparaíso.
Facultad de Ciencias, Universidad de Valparaíso.
Gran Bretaña 1111. Playa Ancha. Valparaíso. Chile.
Computational neuroscience applies quantitative techniques to improve the understanding of the nervous system and, in collaboration with engineers and clinicians, exploits this knowledge to clinical treatment and engineering applications. My goal is to use cutting edge techniques and theories that I learnt during my PhD to assist the national development in computational neuroscience.
My specific research topic at CINV involves modeling the detection of motion direction observed in the retinal circuitry. Mathematical models of direction selectivity (DS) developed with individual cells or cell networks have been proposed to understand the effects of the different components of the retinal circuitry, e.g., starburst amacrine cells (SACs) and DSGCs, on DS. However, there is currently no model combining SACs and DSGCs in a network model to analyze the retinal DS system more comprehensibly. My goal is to build a network model of DS in the retina, including SACs and DSGCs, based on wiring connectivity observed in the mammalian retina, and then determine the contribution of each of the components of the network to DS using a wide range of stimulus velocities and amplitudes.
- Medina, L.E., Grill, W.M., 2016. Nerve excitation using an amplitude-modulated signal with kilohertz-frequency carrier and non-zero offset. J NeuroEng Rehab 13, 63.
- Howell, B.*, Medina, L.E.*, Grill, W.M., 2015. Effects of frequency dependent membrane capacitance on neural excitation. J Neural Eng 12, 056015. *Both of these authors contributed equally to this work.
- Medina, L.E., Grill, W.M., 2014. Volume conductor model of transcutaneous electrical stimulation with kilohertz signals. J Neural Eng 11, 066012.
- Medina, L.E., Lebedev, M.A., O’Doherty, J.E., Nicolelis, M.A.L., 2012. Stochastic facilitation of artificial tactile sensation in primates. J Neurosci 32, 14271–14275.
- Perez, C.A., Estévez, P.A., Vera, P.A., Castillo, L.E., Aravena, C.M., Schulz, D.A., Medina, L.E., 2011. Ore grade estimation by feature selection and voting using boundary detection in digital image analysis. Int J Mineral Proc 101, 28–36.
- Perez, C.A., Donoso, J.R., Medina, L.E., 2010. A critical experimental study of the classical tactile threshold theory. BMC Neurosci 11, 76.
- Perez, C.A., Cohn, T.E., Medina, L.E., Donoso, J.R., 2007. Coincidence-enhanced stochastic resonance: experimental evidence challenges the psychophysical theory behind stochastic resonance. Neurosci Lett 424, 31–35.
- Perez, C.A., Gonzalez, G.D., Medina, L.E., Galdames, F.J., 2005. Linear versus nonlinear neural modeling for 2-D pattern recognition. IEEE Trans Sys Man & Cybern, Part A 35, 955–964.