Mathematicians help to unlock brain function. Mathematicians from Queen Mary, University of London will bring researchers one-step closer to understanding how the structure of the brain relates to its function in two recently published studies.
Publishing in Physical Review Letters the researchers from the Complex Networks group at Queen Mary's School of Mathematics describe how different areas in the brain can have an association despite a lack of direct interaction. The team, in collaboration with researchers in Barcelona, Pamplona and Paris, combined two different human brain networks - one that maps all the physical connections among brain areas known as the backbone network, and another that reports the activity of different regions as blood flow changes, known as the functional network. Model Suggests Link between Intelligence and Entropy. +Enlarge image A.
Wissner-Gross/Harvard Univ. & MIT A. D. Wissner-Gross and C. A pendulum that is free to swing through all angles in a plane can be stabilized in the inverted position by sliding the pivot horizontally, in the same way that you can balance a meter stick on your finger. The smallest disks, subjected to causal entropy forces, tend to work in a synchronized fashion to pull down the largest disk, in what the authors present as a primitive example of social cooperation. The second law of thermodynamics—the one that says entropy can only increase—dictates that a complex system always evolves toward greater disorderliness in the way internal components arrange themselves. Q-learning. Q-learning is a model-free reinforcement learning technique.
Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and following the optimal policy thereafter. When such an action-value function is learned, the optimal policy can be constructed by simply selecting the action with the highest value in each state.
One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. Additionally, Q-learning can handle problems with stochastic transitions and rewards, without requiring any adaptations. Algorithm The problem model, the MDP, consists of an agent, states S and a set of actions per state A. . , the agent can move from state to state. Where is the reward observed after performing. IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer. A network of neurosynaptic cores derived from long-distance wiring in the monkey brain: Neuro-synaptic cores are locally clustered into brain-inspired regions, and each core is represented as an individual point along the ring.
Arcs are drawn from a source core to a destination core with an edge color defined by the color assigned to the source core. (Credit: IBM) Announced in 2008, DARPA’s SyNAPSE program calls for developing electronic neuromorphic (brain-simulation) machine technology that scales to biological levels, using a cognitive computing architecture with 1010 neurons (10 billion) and 1014 synapses (100 trillion, based on estimates of the number of synapses in the human brain) to develop electronic neuromorphic machine technology that scales to biological levels.” Simulating 10 billion neurons and 100 trillion synapses on most powerful supercomputer Neurosynaptic core (credit: IBM) Two billion neurosynaptic cores DARPA SyNAPSE Phase 0DARPA SyNAPSE Phase 1DARPA SyNAPSE Phase 2. Daniel Romano B Martinho. Daniel Romano B Martinho. Artificial Intelligence - foundations of computational agents. Later Terminator: We’re Nowhere Near Artificial Brains. I can feel it in the air, so thick I can taste it.
Can you? It’s the we’re-going-to-build-an-artificial-brain-at-any-moment feeling. It’s exuded into the atmosphere from news media plumes (“IBM Aims to Build Artificial Human Brain Within 10 Years”) and science-fiction movie fountains…and also from science research itself, including projects like Blue Brain and IBM’s SyNAPSE. For example, here’s a recent press release about the latter: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. Now, I’m as romantic as the next scientist (as evidence, see my earlier post on science monk Carl Sagan), but even I carry around a jug of cold water for cases like this.