The broad goals of this project are to understand the set of computations performed by a neural circuit. The mammalian retina is chosen as a model system, because of our ability to achieve large-scale recordings of its output while stimulating with realistic patterns of light, as well as its intricate and well-characterized anatomy. The retina's representation of visual stimuli changes dramatically between the photoreceptors, which use a camera-like representation where each cell is a pixel, to the ganglion cells, which represent the image with a complex and highly overlapping basis set of visual features. We seek to understand how visual information is organized within and among the retina's parallel visual channels.
The Specific Aims are: 1) characterize the diversity and stereotypy of the visual features represented within the population of ganglion cells by performing functional classification; 2) use recently developed techniques of information-theoretic analysis to measure the correlation and redundancy in large populations of ganglion cells; 3) explore how correlated populations formed both within and among parallel channels can be used to perform fundamental visual tasks, such as spatial localization, shape discrimination, and temporal pattern recognition. A detailed knowledge of how the population of retinal ganglion cells represents the visual world is of fundamental interest to neuroscience and is also important for guiding the development of a retinal prosthesis to restore vision successfully.
| Marre, Olivier; Botella-Soler, Vicente; Simmons, Kristina D et al. (2015) High Accuracy Decoding of Dynamical Motion from a Large Retinal Population. PLoS Comput Biol 11:e1004304 |
| Palmer, Stephanie E; Marre, Olivier; Berry 2nd, Michael J et al. (2015) Predictive information in a sensory population. Proc Natl Acad Sci U S A 112:6908-13 |
| da Silveira, Rava Azeredo; Berry 2nd, Michael J (2014) High-fidelity coding with correlated neurons. PLoS Comput Biol 10:e1003970 |
| Aljadeff, Johnatan; Segev, Ronen; Berry 2nd, Michael J et al. (2013) Spike triggered covariance in strongly correlated gaussian stimuli. PLoS Comput Biol 9:e1003206 |
| Schwartz, Greg; Macke, Jakob; Amodei, Dario et al. (2012) Low error discrimination using a correlated population code. J Neurophysiol 108:1069-88 |
| Marre, Olivier; Amodei, Dario; Deshmukh, Nikhil et al. (2012) Mapping a complete neural population in the retina. J Neurosci 32:14859-73 |
| Vasquez, J C; Marre, O; Palacios, A G et al. (2012) Gibbs distribution analysis of temporal correlations structure in retina ganglion cells. J Physiol Paris 106:120-7 |
| Soo, Frederick S; Schwartz, Gregory W; Sadeghi, Kolia et al. (2011) Fine spatial information represented in a population of retinal ganglion cells. J Neurosci 31:2145-55 |
| Schneidman, Elad; Puchalla, Jason L; Segev, Ronen et al. (2011) Synergy from silence in a combinatorial neural code. J Neurosci 31:15732-41 |
| Gao, Juan; Schwartz, Greg; Berry 2nd, Michael J et al. (2009) An oscillatory circuit underlying the detection of disruptions in temporally-periodic patterns. Network 20:106-35 |
