The great Masao Ito, originator of one of the classic theories of cerebellar function, has published a new theory in the recent issue of Nature Neuroscience regarding how the cerebellum may be involved in control of cognition.
The basic idea is that while the cerebellum has evolutionarily had a role of refining motor commands for the purpose of controlling the skeleton, in the human the cerebellum is capable of refining commands from frontal cortex to “control” internal representations of the outside world. Ito uses the increasingly popular language of control theory to describe the effect that the cerebellum may have on different parts of the brain.
From the abstract:
The intricate neuronal circuitry of the cerebellum is thought to encode internal models that reproduce the dynamic properties of body parts. These models are essential for controlling the movement of these body parts: they allow the brain to precisely control the movement without the need for sensory feedback. It is thought that the cerebellum might also encode internal models that reproduce the essential properties of mental representations in the cerebral cortex. This hypothesis suggests a possible mechanism by which intuition and implicit thought might function and explains some of the symptoms that are exhibited by psychiatric patients. This article examines the conceptual bases and experimental evidence for this hypothesis.
An extremely interesting trend in neuroscience has been to use the language of Control Theory to explain brain function. A recent paper by Shadmehr and Krakauer does a very nice job of summarizing this trend and assembling a comprehensive theory of how the brain controls the body. Using control theory, they put forward a mathematically precise description of their theory. Because their theory uses blocks that are direct analogues of specific brain regions like the basal ganglia, motor cortex, and cerebellum, they can use brain lesion studies to undergird their ideas about these components. From the paper:
The theory explains that in order to make a movement, our brain needs to solve three kinds of problems: we need to be able to accurately predict the sensory consequences of our motor commands (this is called system identification), we need to combine these predictions with actual sensory feedback to form a belief about the state of our body and the world (called state estimation), and then given this belief about the state of our body and the world, we have to adjust the gains of the sensorimotor feedback loops so that our movements maximize some measure of performance (called optimal control).
At the heart of the approach is the idea that we make movements to achieve a rewarding state. This crucial description of why we are making a movement, i.e., the rewards we expect to get and the costs we expect to pay, determines how quickly we move, what trajectory we choose to execute, and how we will respond to sensory feedback.
This approach of describing brain lesion studies in the context of a well-thought out theory ought to be further encouraged.
Researchers at the University of Nevada, Reno have an interesting and ambitious set-up for doing research in AI that the describe in a recent paper.
From the paper:
We define virtual neurorobotics as follows: a computer-facilitated behavioral loop wherein a human interacts with a projected robot that meets five criteria: (1) the robot is sufficiently embodied for the human to tentatively accept the robot as a social partner, (2) the loop operates in real time, with no pre-specified parcellation into receptive and responsive time windows, (3) the cognitive control is a neuromorphic brain emulation incorporating realistic neuronal dynamics whose time constants reflect synaptic activation and learning, membrane and circuitry properties, and (4) the neuromorphic architecture is expandable to progressively larger scale and complexity to track brain development, (5) the neuromorphic architecture can potentially provide circuitry underlying intrinsic motivation and intentionality, which physiologically is best described as “emotional” rather than rule-based drive.
What’s interesting to me about this is the combination of a embodied robot in a virtual world with a neurally inspired controller for that robot. While there are pros and cons of embodiment in virtual world (some of which have been touched on here before), I think that if your priority is closing the loop from embodiment to research on neural systems, the importance of this kind of approach cannot be ignored.
Neurons come in many shapes and sizes. Frequently, the shape of a neuron is characteristic to its type. Several theoretical papers have demonstrated that the shape of a neuron can crucially determine its pattern of activity, independently of other factors (Mainen & Sejnowski, 1996, for example). Several resources on the web such as neuromorpho.org and the Cell Centered Database are dedicated to maintaining repositories of different neuronal shapes (also known as morphologies).
Any computer scientist worth their salt, noticing this trend, is tempted to say: if neuronal shape is so important, maybe we ought to have good data standards to describe it. That’s just what a paper last year did. It surveyed the popular data standards for modeling, primarily in the NEURON and Genesis simulation packages. The result is a data standard called MorphML, which is part of a larger effort called NeuroML.
Neuronal shape is a weird data type for the computer science world, but I think an incredibly important and fundamental one for deeply coping with the complexity of real brain tissue. It seems to me that many areas of neuroscience research could benefit from the construction of more explicit models of the circuits they study.
A friend recently alerted me to The Journal of Visualized Experiments, a revolutionary way to present science by showing the actual experimental procedures. Poking around the site I already picked up tips for my own research just by watching others perform procedures that I do myself in the lab (eg. use Sparkle glass cleaner not just for objectives but also for sample coverglass, how to properly interpret the OD ratio on the spectrophotometer for RNA purity, etc.)
Click more to see some of my favorite videos on the site.