Visual object recognition is crucial for most everyday tasks including face identification, reading and navigation. In spite of the massive increase in computational power over the last two decades, a 3-year-old still outperforms the most sophisticated algorithms even in simple recognition tasks. Understanding the computations performed by the human visual system to recognize objects will have profound implications not only to understand the functions (and malfunction) of the cerebral cortex but also for developing visual prosthetic devices for the visually impaired. We combine neurophysiology, electrical stimulation and tools from machine learning to further our understanding of the neuronal circuits, algorithms and computations performed by the human visual system to perform visual pattern recognition. In the vast majority of visually impaired or blind people, the problems originate at the level of the retina while the visual cortex remains unimpaired. Our proposal constitutes a proof- of-principle approach towards developing visual prosthetic devices that rely on electrical stimulation of visual cortex.
The specific aims of this proposal are designed to test the possibility of decoding and recoding information in visual cortex: (1) Read-out of visual information from human visual cortex on line (2) Write-in of visual information in human visual cortex. We take advantage of a rare opportunity to study the human brain at high spatial and temporal resolution by studying patients who have electrodes implanted for clinical reasons. Our electrophysiological recordings provide us with a unique view of the human temporal lobe circuitry and allow us to test the feasibility of cortical visual prosthetics in behaving human subjects.
Towards cortical visual prosthetics one of the key challenges for the visually impaired and blind people is the lack of visual object recognition capabilities. Visual recognition is crucial for most everyday tasks including navigation and face identification. Our proposal is a proof-of-principle approach towards the development of visual prosthetics devices based on electrical stimulation in visual cortex.
| Burbank, Kendra S; Kreiman, Gabriel (2012) Depression-biased reverse plasticity rule is required for stable learning at top-down connections. PLoS Comput Biol 8:e1002393 |
| Kreiman, Gabriel (2011) Decoding ensemble activity from neurophysiological recordings in the temporal cortex. Conf Proc IEEE Eng Med Biol Soc 2011:5904-7 |
| Tang, Hanlin; Kreiman, Gabriel (2011) Face recognition: vision and emotions beyond the bubble. Curr Biol 21:R888-90 |
| Anderson, William S; Kreiman, Gabriel (2011) Neuroscience: what we cannot model, we do not understand. Curr Biol 21:R123-5 |
| Hemberg, Martin; Kreiman, Gabriel (2011) Conservation of transcription factor binding events predicts gene expression across species. Nucleic Acids Res 39:7092-102 |
| Kim, Tae-Kyung; Hemberg, Martin; Gray, Jesse M et al. (2010) Widespread transcription at neuronal activity-regulated enhancers. Nature 465:182-7 |
| Agam, Yigal; Liu, Hesheng; Papanastassiou, Alexander et al. (2010) Robust selectivity to two-object images in human visual cortex. Curr Biol 20:872-9 |
| Blumberg, Julie; Kreiman, Gabriel (2010) How cortical neurons help us see: visual recognition in the human brain. J Clin Invest 120:3054-63 |
| Quian Quiroga, Rodrigo; Kreiman, Gabriel (2010) Measuring sparseness in the brain: comment on Bowers (2009). Psychol Rev 117:291-7 |
| Rasch, Malte; Logothetis, Nikos K; Kreiman, Gabriel (2009) From neurons to circuits: linear estimation of local field potentials. J Neurosci 29:13785-96 |
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