Simo Vanni

HUT, Vail, CO, United States 
Visual System
"Simo Vanni"
Mean distance: 16.24 (cluster 29)
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Vanni S, Hokkanen H, Werner F, et al. (2020) Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models. Cerebral Cortex (New York, N.Y. : 1991)
Hokkanen H, Andalibi V, Vanni S. (2019) Controlling Complexity of Cerebral Cortex Simulations, II: Streamlined Microcircuits. Neural Computation. 1-19
Andalibi V, Hokkanen H, Vanni S. (2018) Controlling Complexity of Cerebral Cortex Simulations-I: CxSystem, a Flexible Cortical Simulation Framework. Neural Computation. 1-18
Inverso SA, Goh XL, Henriksson L, et al. (2016) From evoked potentials to cortical currents: Resolving V1 and V2 components using retinotopy constrained source estimation without fMRI. Human Brain Mapping
Salmela VR, Henriksson L, Vanni S. (2016) Radial Frequency Analysis of Contour Shapes in the Visual Cortex. Plos Computational Biology. 12: e1004719
Sharifian F, Heikkinen H, Vigário R, et al. (2015) Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex. Frontiers in Computational Neuroscience. 9: 155
Vanni S, Heikkinen H. (2015) [Is there unused capacity in our brain?]. Duodecim; LääKetieteellinen Aikakauskirja. 131: 1644-9
Vanni S, Sharifian F, Heikkinen H, et al. (2015) Modeling fMRI signals can provide insights into neural processing in the cerebral cortex. Journal of Neurophysiology. 114: 768-80
Heikkinen H, Sharifian F, Vigario R, et al. (2015) Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex. Journal of Neurophysiology. 114: 57-69
Sharifian F, Heikkinen H, Vigário R, et al. (2015) Area summation is related to efficient neural representation Bmc Neuroscience. 16
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