Yang Dan

230D Li Ka Shing
(510) 643-2833
Lab Page
AFFILIATIONS Professor of Neurobiology

Visual neurophysiology; Computational neurosciences

Our research aims to elucidate (1) how visual information is encoded and processed in the mammalian brain, and (2) how neural circuits are shaped by visual experience. We use a multidisciplinary approach combining computational analyses and experimental studies at multiple levels, from single neurons and dendrites to animal behavior. Current projects include:

Study of cortical circuits and dynamics in slice. Patch-clamp recording experiments are performed in visual cortical slices to study the interaction between different pathways – feedforward, recurrent, and feedback – in information processing and activity-dependent plasticity. Two-photon Ca2+ imaging allows us to study processing at the dendritic level.

Characterization of neural circuits underlying receptive field properties in vivo. In addition to extracellular recordings, whole-cell (intracellular) recordings are made in the visual cortex in vivo to monitor excitatory and inhibitory synaptic inputs into cortical neurons during visual stimulation and to manipulate the postsynaptic membrane potential. Linear and nonlinear computational techniques are used to analyze the responses to complex stimuli (e.g., white noise, natural scenes) to understand the cortical circuitry underlying various response properties of visual neurons, the effects of neuromodulatory inputs, and the mechanisms underlying experience-dependent receptive field plasticity.

Studying ensemble coding with multielectrode recording and imaging. Activity of multiple neurons will be measured simultaneously with multielectrode recording, two-photon imaging, or voltage-sensitive dye imaging. These experiments will allow us to characterize the spatiotemporal patterns of ensemble neural activity and their roles in visual coding.

Exploring the neural correlate of perception in awake animals. In addition to human psychophysics experiments, we have established a rodent behavioral paradigm for studying visual perception. Multielectrode recording are also made in awake rodents to understand the neural activity patterns underlying perception.

Selected Publications

Goard, M and Dan, Y. (2009). Basal forebrain activation enhances cortical coding of natural scenes Nat. Neurosci., in press.

Li, C.T., Poo, M.-m. and Dan, Y. (2009). Burst spiking of a single cortical neuron modifies global brain state. Science 324, 643-646.

Han, F., Caporale, N and Dan, Y. (2008). Reverberation of recent visual experience in spontaneous cortical waves. Neuron 60, 321-327.

Chen, X., Han, F., Poo, M.-m., and Dan, Y. (2007). Excitatory and suppressive receptive field subunits in awake monkey V1. PNAS 104, 19120-19125.

Yao, H., Shi, L., Han, F., Gao, H., and Dan, Y. (2007). Rapid learning in cortical coding of visual scenes. Nat. Neurosci. 10, 772-778.

Meliza, C. D. and Dan, Y. (2006). Receptive-field modification in rat visual cortex induced by paired visual stimulation and single cell spiking. Neuron 49, 183-189.

Touryan, J, Felsen, G., and Dan, Y. (2005). Spatial structure of complex cell receptive fields measured with natural images, Neuron 45, 781-791.

Froemke, R.C., Poo, M.-m., and  Dan, Y. (2005). Spike-timing-dependent synaptic plasticity depends on dendritic location, Nature 434, 221-225.

Felsen, G., Touryan, J., Han, F., and Dan, Y. (2005). Cortical sensitivity to visual features in natural scenes, PLoS Biol. 3 (10), e342.

Felsen, G. and Dan, Y. (2005). A natural approach to studying vision, Nature Neurosci. 8, 1643-1646.

Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30.

Froemke, R.C. and Dan, Y. (2002). Spike timing-dependent synaptic modification induced by natural spike trains. Nature 416, 433-438.

Fu, Y., Djupsund, K., Gao, H., Hayden, B., Shen, K., and Dan, Y. (2002). Temporal specificity in the cortical plasticity of visual space representation. Science 296, 1999-2003.

Lau, B., Stanley, G.B., and Dan, Y. (2002). Computational subunits of visual cortical neurons revealed by artificial neural networks. Proc. Natl. Acad. Sci. USA 99, 8974-8979.