The candidate seeks to become independent as a neuroscientist and to work at the interface between neuroscience and physics. The Sloan-Swartz Center at UCSF provides a unique opportunity to become immersed in neuroscience at its cutting edge. While the majority of her time will be spent in research, she will attend a range of systems neuroscience and computer vision courses. The immediate goals of her research are to develop an information-theoretic method that allows for a rigorous statistical analysis of neural responses to natural stimuli, which are non- Gaussian and have strong spatiotemporal correlations; and to use this method to analyze the responses of visual cortical neurons to natural time-varying images. There are numerous indications that the responses of neurons to natural stimuli cannot be completely predicted from their responses to stimuli with simple statistical properties, such as white noise ensembles. However, existing methods for analyzing single neuron responses may be rigorously applied only to Gaussian ensembles. The information-theoretic method involves finding the stimulus dimensions that carry the most information about the neuron's response. The stimulus ensemble is not assumed to be Gaussian. The only assumption made is that the neuron is selective for a small number of stimulus dimensions out of the high-dimensional stimulus space: responses with respect to the relevant dimensions might be arbitrarily nonlinear. After testing the method on model neurons and verifying that it gives reasonable results for cortical neurons probed by natural scenes, the method will be used to test the categorical distinction between simple and complex cells in the primary visual cortex. The method will then be modified so that a large set of directions related to each other via certain symmetry operations, such as translation or scaling, can be simultaneously found, with the goal of systematically probing neurons in extrastriate areas with natural scenes.
| Saremi, Saeed; Sejnowski, Terrence J; Sharpee, Tatyana O (2013) Double-gabor filters are independent components of small translation-invariant image patches. Neural Comput 25:922-39 |
| Sharpee, Tatyana O (2013) Computational identification of receptive fields. Annu Rev Neurosci 36:103-20 |
| Kaardal, Joel; Fitzgerald, Jeffrey D; Berry 2nd, Michael J et al. (2013) Identifying functional bases for multidimensional neural computations. Neural Comput 25:1870-90 |
| Atencio, Craig A; Sharpee, Tatyana O; Schreiner, Christoph E (2012) Receptive field dimensionality increases from the auditory midbrain to cortex. J Neurophysiol 107:2594-603 |
| Eickenberg, Michael; Rowekamp, Ryan J; Kouh, Minjoon et al. (2012) Characterizing responses of translation-invariant neurons to natural stimuli: maximally informative invariant dimensions. Neural Comput 24:2384-421 |
| Sharpee, Tatyana O; Nagel, Katherine I; Doupe, Allison J (2011) Two-dimensional adaptation in the auditory forebrain. J Neurophysiol 106:1841-61 |
| Jeanne, James M; Thompson, Jason V; Sharpee, Tatyana O et al. (2011) Emergence of learned categorical representations within an auditory forebrain circuit. J Neurosci 31:2595-606 |
| Sharpee, Tatyana O; Atencio, Craig A; Schreiner, Christoph E (2011) Hierarchical representations in the auditory cortex. Curr Opin Neurobiol 21:761-7 |
| Imaizumi, Kazuo; Priebe, Nicholas J; Sharpee, Tatyana O et al. (2010) Encoding of temporal information by timing, rate, and place in cat auditory cortex. PLoS One 5:e11531 |
| Atencio, Craig A; Sharpee, Tatyana O; Schreiner, Christoph E (2009) Hierarchical computation in the canonical auditory cortical circuit. Proc Natl Acad Sci U S A 106:21894-9 |
Showing the most recent 10 out of 17 publications
