Computational Neuroscience Commons^™

A Two-Layer Model Explains Higher-Order Feature Selectivity Of V2 Neurons, Timothy D. Oleskiw, Justin D. Lieber, J. Anthony Movshon, Eero P. Simoncelli

MODVIS Workshop

Neurons in cortical area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture. However, a functional account of how V2 neurons build selectivity for complex natural image features from their inputs – V1 neurons locally tuned for orientation and spatial frequency – remains elusive.

We made single-unit recordings in area V2 in two fixating rhesus macaques. We presented stimuli composed of multiple superimposed grating patches that localize contrast energy in space, orientation, and scale. V2 activity is modeled via a two-layer linear-nonlinear network, optimized to use a sparse combination of V1-like outputs to account …

Learning How To Build A Neural Network Model Of The Tactile Periphery, Vicky Chang

Undergraduate Student Research Internships Conference

First order neurons in the hairless skin of human hands have spatially complex receptive fields that allow for the detection of spatial details. These spatially complex receptive fields arise from the branching of mechanoreceptors, which converge and connect to first order neurons. This arrangement allows us to process our sensory environment through detecting the edge orientation of a touched object for instance, and do things like read braille.

These spatially complex receptive fields can studied by using a feedforward neural network to model the tactile periphery. By understanding the processing at the level of the tactile periphery, we can better …

Computations Of Top-Down Attention By Modulating V1 Dynamics, David Berga, Xavier Otazu

MODVIS Workshop

The human visual system processes information defining what is visually conspicuous (saliency) to our perception, guiding eye movements towards certain objects depending on scene context and its feature characteristics. However, attention has been known to be biased by top-down influences (relevance), which define voluntary eye movements driven by goal-directed behavior and memory. We propose a unified model of the visual cortex able to predict, among other effects, top-down visual attention and saccadic eye movements. First, we simulate activations of early mechanisms of the visual system (RGC/LGN), by processing distinct image chromatic opponencies with Gabor-like filters. Second, we use a cortical …

A Model Of 1d And 2d Motion Processing In The Primate Brain, Alan Johnston

MODVIS Workshop

Velocity encoding in the primate brain can be modelled by a spatiotemporal gradient approach, with neurons characterized as spatio-temporal derivative operators (Johnston et al. 1999). This strategy works well for moving 1D spatial patterns, but it can produce systematic errors, as it can be overly influenced by the direction of the local spatial gradient of the image brightness. For 2D pattern it is possible to develop a similar spatio-temporal approach, in which the system solves a set of over-determined linear equations directly, to provide an estimate for the 2D image motion. However, in this case the matrix one needs to …

Unifying Binocular, Spatial, And Spatio-Temporal Frequency Integration In Models Of Mt Neurons, Pamela M. Baker, Wyeth Bair

MODVIS Workshop

No abstract provided.