The complexity of the neural activity we use to process visual images reflects the intricacy of those images, a team of New York University scientists has found. Their study offers new insights into how our brain extracts information about our natural surroundings from the light captured by our eyes.
“In order to efficiently process the thousands of images we come across on a daily basis, our brains calibrate in ways that are in sync with the characteristics of these images,” explains Robbe Goris, an NYU postdoctoral fellow and the lead author of the study, which appears in the journal Neuron. The study’s other co-authors include Eero Simoncelli, a professor in NYU’s Center for Neural Science, and J. Anthony Movshon, a professor in NYU’s Center for Neural Science as well as its director.
The research sought to better understand “orientation selectivity,” a fundamental property of the neurons used by our brain to build a representation of the visual world that surrounds us. In the late 1950s, Torsten Wiesel and David Hubel discovered that the activity of neurons in the primary visual cortex strongly depends on the orientation of the visual features seen by those neurons—work for which they later received the Nobel Prize in Physiology or Medicine. However, not all orientation selective neurons are the same. Some are very selective and will only be activated by a single orientation (for example, a vertical line), but others are much less selective and will be activated by many different orientations (for example, a vertical line and slightly tilted lines). The origin and purpose of this diversity have remained open questions for many years.
In their study, the NYU researchers measured the activity of cells in the primary visual cortex and developed a mathematical model designed to predict the exact patterns of activity produced by individual cells. The model succeeded in reproducing the measured activity patterns and revealed that multiple mechanisms are responsible for the diversity in orientation selectivity. The most important mechanism, the model suggested, is the manner in which cortical neurons gather inputs from neurons in other parts of the brain.
This insight enabled the researchers to investigate the consequences of neural diversity in a systematic way. They computed how much information small groups of model neurons collectively transmitted when responding to real-world images. They varied the diversity of those groups and found that groups whose diversity matched the brain’s diversity transmitted most information.
“The brain is a perplexing organ that we will probably never fully understand,” observes Goris. “But sometimes, we find that a simple principle goes a long way in explaining some of its intricacy: The visual world is diverse, and the brain seems to mimic this diversity to maximize the amount of information it can extract.”
About this visual neuroscience research
Funding: This work was supported, in part, by grants from the National Institutes of Health (EY04440, EY022428) and the Howard Hughes Medical Institute.
Source: James Devitt – NYU Image Source: The image is credited to Chavez01 and is in the public domain Original Research:Abstract for “Origin and Function of Tuning Diversity in Macaque Visual Cortex” by Robbe L.T. Goris, Eero P. Simoncelli, and J. Anthony Movshon in Neuron. Published online November 5 2015 doi:10.1016/j.neuron.2015.10.009
Origin and Function of Tuning Diversity in Macaque Visual Cortex
Highlights •Visual cortex neurons differ in the precision of their orientation selectivity •Using novel stimuli and a model, we dissect mechanisms that create tuning diversity •Variations in linear receptive fields and in nonlinear sharpening create diversity •Tuning diversity makes neural populations more efficient encoders of natural images
Summary Neurons in visual cortex vary in their orientation selectivity. We measured responses of V1 and V2 cells to orientation mixtures and fit them with a model whose stimulus selectivity arises from the combined effects of filtering, suppression, and response nonlinearity. The model explains the diversity of orientation selectivity with neuron-to-neuron variability in all three mechanisms, of which variability in the orientation bandwidth of linear filtering is the most important. The model also accounts for the cells’ diversity of spatial frequency selectivity. Tuning diversity is matched to the needs of visual encoding. The orientation content found in natural scenes is diverse, and neurons with different selectivities are adapted to different stimulus configurations. Single orientations are better encoded by highly selective neurons, while orientation mixtures are better encoded by less selective neurons. A diverse population of neurons therefore provides better overall discrimination capabilities for natural images than any homogeneous population.
“Origin and Function of Tuning Diversity in Macaque Visual Cortex” by Robbe L.T. Goris, Eero P. Simoncelli, and J. Anthony Movshon in Neuron. Published online November 5 2015 doi:10.1016/j.neuron.2015.10.009