Donald Doherty's blog

Tag: Brain Science

  • Human Behavior Potentially 93% Predictable: Unpredictable 7% of the Time

    Intuitively we know that humans are relatively unpredictable, right? It turns out there is quite a gap between our intuition and what scientists are finding out about human dynamics.

    The recent paper “Limits of Predictability in Human Mobility” published February 19, 2010 in Science asks “What is the role of randomness in human behavior and to what degree are individual human actions predictable?”

    The research team focused on the movements of 45,000 anonymous mobile phone users. User mobility was analyzed using 3 months data collect by mobile phone carriers.

    They found that most people stayed in a relatively small neighborhood within about an half a mile (1 km) to 6 miles (10 km) during their daily activities. Another much smaller population was seen to regularly travel much greater distances. The research team expected that the predictability of where these people would go would be much less than those who traveled much smaller distances. They were surprised to find that predictability was about equal across all 45,000 people at about 93% potential predictability. In fact, this number held up across every demographic breakdown they tried.

    The measure used defined potential predictability or, in other words, the fundamental limit for each individual’s predictability. That means they found that where people go is 93% predictable in principle. It would be interesting to see to what extent computer algorithms tracking mobility could realize this in practice.

  • NeuronBank: Neuronal Circuit Online Data Repository

    The neuron doctrine has been central to neuroscience for more than a century with the idea that the neuron (the individual brain cell) is the fundamental building block of the brain. Ramon y Cajal, the originator of the neuron doctrine, began classifying neurons based on cell shape and connectivity. Ramon y Cajal suggested that a relationship exists between a neuron’s shape, the connections (synapses) it receives and the synapses it makes with other neurons, and the neuron’s function.

    Individual neurons are connected by synapses into functional units known as neuronal circuits. We may consider neuronal circuits as the fundamental units underlying signal processing (information processing) in the brain.

    A recent paper describes an online data repository called NeuronBank that is focused on neurons and the neuronal circuits they participate in. The paper “NeuronBank: a tool for cataloging neuronal circuitry” was published April 19, 2010 in Frontiers in Systems Neuroscience.

    Classifying neurons is far from straight forward and identifying the neuronal circuits they participate in is even more difficult. This is true for even the simplest animals with nervous system. Nevertheless, the problem is relatively more tractable in invertebrate nervous systems where individual neurons can be uniquely identified and have similar properties from animal to animal, which isn’t true in vertebrate nervous systems.

    In some invertebrate animals it is possible to identify every neuron in the nervous system, as has been done for a worm (Caenorhabditis elegans) which has precisely 302 neurons. The online data repository WormAtlas.org includes data on all of the worm’s neurons along with their synaptic connections, gene expression profiles, anatomy, neurotransmitters, and developmental lineage.

    The team that created the NeuronBank decided to start with animals that were more complex than the worm but far simpler than vertebrates. They focused on the nervous systems in gastropod molluscs, which have around 8,000 to 10,000 neurons. Individual neurons and classes of neurons can be identified along with neural circuits underlying specific behaviors.

    NeuronBank was designed to use terminology commonly agreed upon by the community of users. NeuronBank uses a two part hierarchical ontology to represent the knowledge about neurons and connections: (a) a core ontology applicable across species, and (b) an extensible list of attributes that can be tailored for a specific species. Their ontology appears to follow Semantic Web standards.

    Currently NeuronBank contains data from two invertebrate species. The site is in what I’d call an early alpha version (proof of concept). If you’d like to try it out I suggest going to the paper and following the “An Example Search” section under “Results.” It looks like the site only works with Firefox and visualization doesn’t seem to work at all. There is supposed to be a LocationVis plug-in that I was unable to find and nothing showed up.

    Clearly when I look up, for instance, a Purkinje cell I should be one link away from information on the circuit or circuits that the cell type is involved in. The NeuronBank team has made a commendable start on a neuron and neuronal circuit repository that may be integrated into the global neuroscience knowledge base.

    Other related blog posts:

    Brain Research Using Online Data Repositories: Network Structure of the Brain

    Brain Research Using Online Data Repositories: Brain Cell Shape and Function

  • A Question of Synchrony, Correlation or Active Decorrelation among Brain Cell Responses

    In an earlier post (“Correlated Response Fluctuations Between Cortical Neurons Rare“) I discussed a research paper that challenged a large body of literature that claimed a high degree of synchrony or correlated responses between brain cells (neurons) in the cerebral cortex. They concluded that either 1) “adjacent neurons share only a few percent of their inputs” or 2) “their activity is actively decorrelated.”

    In the same issue of Science a paper by different team of researchers addresses the question of active decorrelation of signals (action potentials) in the cerebral cortex. (“The Asynchronous State in Cortical Circuits” was published January 29, 2010 in Science.)

    These researchers used a combination of theory, computer modeling, and actual recordings from rat somatosensory and auditory cerebral cortex to address the relationship between correlations and shared input.

    They found that neurons displayed a high degree of correlated (synchronous) activity if the neurons receiving shared input were either all excitatory or all inhibitory. However, neural circuits in the cerebral cortex contain both types of neurons. When both excitatory and inhibitory neurons were driven by common excitatory input their spiking activity was actively decorrelated.

    The authors provide a plausible mechanism for the active decorrelation. They explain that asynchronous activity persists in the presence of shared input because the two types of neurons (inhibitory and excitatory) spontaneously track one another with a time lag that is inversely proportional to the number of neurons in the cortical circuit.

    The sum of the evidence suggests that careful recording techniques will show more asynchrony (less synchrony or correlation) between signals in nearby cortical neurons than previously thought. Circuit dynamics will assure a great deal of asynchrony even in the presence of significant common input. These findings suggest that asynchronous signals predominate in the cerebral cortex and, therefore, efficient signal processing (most would say information processing) is possible.

    Other related blog posts:

    Correlated Response Fluctuations Between Cortical Neurons Rare