13 thoughts on “Blue Brain’s Henry Markram

  1. 1.Smart guy. 2.Quiet audience. 3.Cool lighting.
    4-1000. People are more interested in appearance and sex. Post a video with a naked girl explaining why shoes are vital to happiness and you will get your 1000 comments. Tell the world how the universe was created and it might make page seven in the Life section of the paper.

  2. The TED talk by Bertrand Piccard about his solar airplane is very interesting and I think it applies to your topic. There’s also an Oregon connection to it, kind of obscure.

  3. Hey Joe, I so agree with you, I also think that this is the most amazing project ever, with a HUGE potential to create a REAL Artificial Intelligence (AI) for the first time on earth.

    Check here this 2 ASTONISHING lectures of Henry Markram:

    1. http://neuroinformatics2008.org/congress-movies/Henry%20Markram.flv/view

    2. ditwww.epfl.ch/cgi-perl/EPFLTV/home.pl?page=start_video&lang=2&connected=0&id=365&video_type=10&win_close=0

    and this very interesting article from the ‘Seed’ magazine:

    Part 1:

    Part 2:

    And a few more interesting links about the project:




  4. Thanks for posting this, Joe. I agree this is an incredibly important project. I’ve long felt this line of research is the only reliable way to build an AI. Obviously when we combine this with ever-improving technology to scan a human’s brain and we’re getting awfully close to uploading. Interesting philosophical questions about the nature of individuality — regardless of whether the scanning can be done destructively or not.

    If (when) the whole thing works really well, it raises questions about free will, which is a specific topic I’ve mused upon previously: http://www.embracingchaos.com/2007/02/turing_complete.html

  5. Pingback: Embracing Chaos » Blog Archive » Blue Brain: the first steps towards uploading - Leo Parker Dirac on Business and Technology Trends

    By Dr. Ronald J. Swallow
    610 704 0914

    I do not understand why the neocortex is a mystery to everyone. Its neuron net circuit is repeated throughout the cortex. It consists of excitatory and inhibitory neurons whose functions, each, have been known for decades. The neuron net circuit is repeated over layers whose axonal outputs feed on as inputs to other layers. The neurons of each layer, each receive axonal inputs from one or more sending layers and all that they can do is correlate the axonal input stimulus pattern with their axonal connection patterns from those inputs and produce an output frequency related to the resultant PSPs. Axonal growth toward a neuron is definitely the mechanism for permanent memory formation and it is just what is needed to implement conditioned reflex learning. This axonal growth must be under the control of the glial cells and must be a function of the signals surrounding the neurons.

    The cortex is known to be able to do pattern recognition and the correlation between an axonal input stimulus and an axonal input connection pattern is just what is needed to do pattern recognition. However, pattern recognition needs normalized correlations and a means to compare these correlations so that the largest correlation is recognized by the neurons. Without normalization, the PSPs relative values would not be bounded properly and could not be used to determine the best pattern match. In order to get PSPs to be compared so that the maximum PSP neuron would fire, the inhibitory neuron is needed. By having a group of excitatory neurons feed an inhibitory neuron that feeds back inhibitory axonal signals to those excitatory neurons, one is able to have the PSPs of the excitatory neurons compared, with the neuron with the largest PSP firing before the others do as the inhibitory signal decays after each excitatory stimulus, thus inhibiting the other excitatory neurons with the smaller PSPs. This inhibitory neuron is needed in order to achieve PSP comparisons, no question about it. For a meaningful comparison, the PSPs must be normalized. As unlikely as it may seem possible, it comes out that the inhibitory connections growing by the same rules as excitatory connections grow to a value which accomplishes the normalization. That is, as the excitatory axon pattern grows via conditioned reflex rules, the inhibitory axon to each excitatory neuron grows to a value equal to the square root of the sum of the squares of the excitatory connections. This can be shown by a mathematical analysis of a group of mutually inhibiting neurons under conditioned reflex learning. This normalization does not require the neurons to behave differently from that known for decades, but rather requires that they interact with an inhibitory neuron as described.

    Thus, by simply having the inhibitory neurons receive from neighboring excitatory neurons with large connection strengths where if the excitatory neuron fires, the inhibitory neuron fires and by allowing the inhibitory axonal signals be included with the excitatory axonal input signals as the inputs to those excitatory neurons, the neocortex is able to do normalized conditioned reflex pattern recognition as its basic function.

    If one thinks about it, layers of mutually inhibiting groups of neurons are all that are needed to explain the neocortex functions. The layers of neurons are able to exhibit conditioned reflex behavior between sub-patterns, generating new learned behaviors as observed by the human. With layer to layer feedback, multi-stable behavior of layers of neurons results, forming short term memory patterns that become part of the stimulus to other neurons. With normalized correlations, there is always an axonal input stimulus pattern that will excite every excitatory neuron.

    The only way to prove this cortex model is to build a simulator modeling large nets of neurons and to observe resultant human behaviors. Most certainly we will never be able to measure the neuron nets of the cortex due to their small sizes. This means, that research projects must be formed that do these simulations and do not waste R&D efforts to try to measure properties of the cortex as the main means to understand the cortex. Certainly the area to area connection scheme is needed, but it likely can be varied, still with intelligence being exhibited. Trials will be needed to determine the initial connection strengths when initiating the simulator. These connections will need to be simple such as non-zero between corresponding neurons of the mutually inhibiting groups.

    Axon growth toward pulsing neurons is the likely mechanism for memory alteration. Alternatively, having neuron axons back away from neurons has no physical basis and it is well known that the number of axons increases with age in the human. Certainly axon connection strengths never become proportional to axon pulsing frequencies, otherwise the nets of neurons will never exhibit permanent past memories, but rather be a function of recent events only. Glial cells are likely participants to axonal growth control. It is likely that they will inhibit axonal growth physically, unless a chemical falls below a concentration. In particular, this would be when the excitatory stimulus (chemically emitted to a neuron by axons to that neuron) to a cell, falls below a critical level, where the correlation between stimulus and connection pattern falls below a limit. The result of such a rule is that learning would only occur if stimulus patterns are new and don’t sufficiently match the connection patterns to neurons. The psychological effect would be a curiosity behavior, observed in humans. Also, it would result in old age reduction of ability to learn, also observed in humans.

    Progress in understanding how the brain works has been basically non-existent over the last 40 years due to limits in measurement. Rather, progress requires simulation to work out the missing details. I predict that simulation will dominate the future efforts of researchers.

    Also, I predict that special purpose hardware will dominate the approach. Using conventional computers to simulate nets of neurons in real-time will go out of style very soon due to their high cost and poor performance.

    Simulation permits an evolution process to arrive upon a successful understanding of the brain. If a logical conclusion is wrong, simulation will eliminate it. If it is right, simulation will verify it.

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