Researchers at the Picower Center for Learning and Memory have uncovered an important new way that the brain performs complex functions such as pattern recognition. The study will appear in the Feb. 1 issue of Nature Neuroscience.
The work, led by Mriganka Sur, Sherman Fairchild Professor of Neuroscience and head of the Department of Brain and Cognitive Sciences at MIT, has implications for understanding the cellular mechanisms underlying many higher level functions, including consciousness.
Within the visual cortex, brain cells work together in localized circuits on tasks such as pattern recognition. At a molecular level, this involves matching the correct positive, or excitatory wires, with the correct negative, or inhibitory wires. An exquisite balance in the interplay between plus and minus inputs on individual neurons is essential to stabilize and shape circuits of thousands of cells.
Earlier work has shown that brain cells contain many individual processing modules that each collect a set number of excitatory and inhibitory inputs. When the two types of inputs are correctly connected together, powerful processing can occur at each module. What's more, the modules have their own built-in intelligence that allows them to accommodate defects in the wiring or electrical storms in the circuitry. If any of the connections break, new ones automatically form to replace the old ones. If the positive, excitatory connections are overloading, new negative, inhibitory connections quickly form to balance out the signaling, immediately restoring the capacity to transmit information.
"We describe a key principle by which neuronal networks in the brain compute new properties from simple inputs. We use a set of sophisticated techniques, including optical imaging, patch clamp recording from neurons in the intact brain, high-resolution tracing of connections and computational modeling, to show that networks in the visual cortex exquisitely balance plus and minus inputs as a critical element of computing a new property such as orientation selectivity or sensitivity to an edge of light," Sur said. "Indeed, neurons and networks cannot function appropriately without such a balance."
Patterns of activity
The primary visual cortex of monkeys and cats contain regions where neurons are tuned to the vertical, horizontal and diagonal lines that give shape to images we see. These regions are dotted with "pinwheel centers" around which all orientations are represented. Areas far from the pinwheel centers contain neurons that are tuned to a specific line orientation, not all of them at once. Visual stimulation evokes different patterns of synaptic inputs at the pinwheel centers and the surrounding areas.
Yet in all regions, neurons are finely tuned to line orientation and edges. In this study, Sur and colleagues look at how processing networks in the brain transform the inputs they receive through visual stimuli to create outputs that can be used for perception and action.
Their results suggest that a key principle is at work through which higher functions are accomplished. This principle is a simple rule of spatial integration--a single mechanism that is able to balance plus and minus inputs to allow neurons to respond quickly and accurately no matter what their location in the cortex.
It's almost as if neurons in less-than-prime real estate compensate for their location by altering the way they respond to inputs. They accomplish this through a fine-tuning mechanism that adjusts to the type of input they receive at their particular location. Thus, the inhibitory tuning balances excitation and yields sharp spike tuning at all locations.
"These ideas form the beginning of an important new way to understand how the brain creates new functions," Sur said. "All higher functions of the brain, particularly complex functions such as pattern recognition or even consciousness, likely use such principles as a basic building block."
In addition to Sur, authors include MIT postdoctoral fellows Jorge Marino, James Schummers and David C. Lyon, who collaborated with a group led by Klaus Obermayer at Berlin University of Technology.
This work was supported by the National Institutes of Health.
A version of this article appeared in MIT Tech Talk on February 9, 2005 (download PDF).
