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Thomas Mrsic-Flogel

Department of Neuroscience, Physiology & Pharmacology

Visual cortical processing

Based on the findings of Hubel and Wiesel (1), generations of neuroscientists have employed primary visual cortex (V1) as an experimentally accessible system for understanding cortical sensory processing and its emergence during postnatal development. While neurons in the retina and visual thalamus respond best to small and roughly circular stimuli of high contrast, neurons in V1 integrate this information to detect more complex visual stimulus features, such as stimulus orientation, direction of motion, spatial frequency, and position. Thus, the function of neurons at early stages of the visual pathway is generally thought to be the analysis of local features of images that fall within the neuron’s ‘receptive field’ (2). However, in order to understand the cortical basis of sensory processing, it is essential to investigate how neurons with different functional properties interact within cortical subnetworks during vision and how their activities are combined into a population code that represents the sensory environment.

Our ignorance of how numerous cortical neurons interact at the single-cell level during sensory processing stems from the fact that the majority of our knowledge is either based on intra- and extra-cellular electrical recordings, or on functional population mapping methods, such as intrinsic signal imaging or fMRI. While microelectrode recordings, which sample only one or a few neurons at a time, are often biased towards large and highly responsive neurons, functional imaging methods lack single-cell resolution. These technical limitations have been recently overcome by in vivo two-photon calcium imaging of neuronal populations (3). By combining two-photon microscopy (4,5) with labelling cortical tissue with the cell-permeable form of a calcium sensitive dye, this method is unique in its ability to record the activity of an unbiased set of multiple neurons in a local cortical volume (300-650 um in diameter). We and others have used this approach to determine not only the functional properties of hundreds of neurons simultaneously, but also map their precise locations within the cortical network (6-9). Importantly, when combined with the cell-type-specific labelling of genetically-encoded fluorescent markers, in vivo two-photon calcium imaging permits the functional investigation of specific neuronal subtypes during cortical network processing (10, 11).


Methods and approaches used in the following projects include: in vivo two-photon microscopy, in vivo whole cell patch clamp recordings, imaging of calcium signals in the intact visual cortex during visual stimulation, in vivo surgical procedures, data analysis in Matlab and ImageJ.

Project 1. Maturation of sensory coding in visual cortex. One approach to improve our understanding of visual cortex function is to investigate how it is brought about during cortical circuit development, and to learn when functional circuits become mature enough for efficient processing of sensory information. By using two-photon calcium imaging in visual cortex during presentation of visual stimuli to anaesthetised mice, the project will investigate how sensory representations emerge during development at the level of population activity, and relate this to the functional maturation of receptive fields.

Project 2. Comparing cortical responses to simple and complex visual stimuli. What happens in the visual cortex when we see an image? Traditionally, visual neuroscientists have used artificial visual stimuli (e.g. gratings, bars, spots) to map neuronal response properties in visual cortex, and found different neurons to be responsive to different visual features. However, emerging evidence suggests that neuronal responses change substantially when tested with naturalistic stimuli, such as images or movies of forests, faces, landscapes, etc. This project will investigate the differences in evoked neuronal population activity in visual cortex between simple artificial and complex naturalistic stimuli using two-photon calcium imaging.

Project 3. Investigating how synaptic inputs generate receptive fields in visual cortex. Neurons in the visual cortex receive thousands of different inputs. It is not known how these inputs are integrated in individual cells to generate receptive fields selective for different visual features, and how these inputs correlate with the activity of surrounding neurons? This collaborative project (with laboratory of Troy Margrie) will address these questions with simultaneous in vivo two-photon calcium imaging of neuronal populations and single-cell patch clamp recordings.

Project 4. Long-term observation of neuronal receptive fields. One important yet unsolved question in neuroscience is how stable are neuronal receptive fields over an individual’s lifetime. On one hand, new experiences are known to modify receptive fields. On the other, the coding strategy of neuronal populations must be stable if the network is to reliably report occurrences in the sensory environment. To investigate the stability and plasticity of receptive fields, we are developing new methods for chronic measurement of neuronal response in visual cortex. The project explores the suitability of new genetically-encoded calcium indicators (12) and new synthetic calcium dyes in chronic preparations with two-photon microscopy in vivo.

1. D. H. Hubel, T. N. Wiesel (1959) J Physiol 148, 574.
2. P. Lennie (2003) Curr Biol 13, R216.
3. C. Stosiek, O. Garaschuk, K. Holthoff et al. (2003) Proc Natl Acad Sci U S A 100, 7319.
4. W. Denk, J. H. Strickler, W. W. Webb (1990) Science 248, 73.
5. F. Helmchen, W. Denk (2005) Nat Methods 2, 932.
6. K. Ohki, S. Chung, Y. H. Ch'ng et al. (2005) Nature 433, 597.
7. T. D. Mrsic-Flogel, S. B. Hofer, K. Ohki et al. (2007) Neuron 54, 961.
8. K. Ohki, S. Chung, P. Kara et al. (2006) Nature 442, 925.
9. J. N. Kerr, D. Greenberg, F. Helmchen (2005) Proc Natl Acad Sci U S A 102, 14063.
10. K. Sohya, K. Kameyama, Y. Yanagawa et al. (2007) J Neurosci 27, 2145.
11. O. Garaschuk, R. I. Milos, C. Grienberger et al. (2006) Pflugers Arch 453, 385.
12. M. Mank, A. Ferrao Santos, S. Direnberger, T. D. Mrsic-Flogel et al. (2008) Nature Methods (in press)


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