[Blog] #CCNeuro asks: “How can we find out how the brain works?”

posted Nov 5, 2017, 7:24 PM by DaWoon Heo

- Thoughts and discussion about the brain from Peter Bandettini and Eric Wong

In this blog, the professor Eric Wong discussed about "How can we find out how the brain works?", which is asked from the Cognitive Computational Neuroscience (CCNeuro) conference.
In short, he mentioned that the most typical conceptual approach to understanding the brain is considering the brain is modular. Thus, the modularity is important for understanding the brain from complexity.

The more details is shown in the brain blog (

Beside this issues, you could find many neuroscience related thought and discussion from Peter Bandettini and Eric Wong in this blog.

[Article] 'Consciousness' in the machine learning (deep learning) perspective

posted Oct 1, 2017, 1:38 PM by Jong-Hwan Lee   [ updated Oct 9, 2017, 9:34 AM ]

The Consciousness Prior


Yoshua Bengio


Université de Montréal, MILA


September 26, 2017



A new prior is proposed for representation learning, which can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by the phenomenon of conscious-ness seen as the formation of a low-dimensional combination of a few concepts constituting a conscious thought, i.e., consciousness as awareness at a particular time instant. This provides a powerful constraint on the representation in that such low-dimensional thought vectors can correspond to statements about reality which are either true, highly probable, or very useful for taking decisions. The fact that a few elements of the current state can be combined into such a predictive or useful statement is a strong constraint and deviates considerably from the maximum likelihood approaches to modeling data and how states unfold in the future based on an agent’s actions. Instead of making predictions in the sensory (e.g. pixel) space, the consciousness prior allow the agent to make predictions in the abstract space, with only a few dimensions of that space being involved in each of these predictions. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in the form of facts and rules, although the conscious states may be richer than what can be expressed easily in the form of a sentence, a fact or a rule.

[NEWS] Robotic system monitors specific neurons

posted Aug 31, 2017, 7:08 PM by 조성만   [ updated Aug 31, 2017, 7:11 PM ]

(from MIT news)

Recording electrical signals from inside a neuron in the living brain can reveal a great deal of information about that neuron’s function and how it coordinates with other cells in the brain. However, performing this kind of recording is extremely difficult, so only a handful of neuroscience labs around the world do it.
To make this technique more widely available, MIT engineers have now devised a way to automate the process, using a computer algorithm that analyzes microscope images and guides a robotic arm to the target cell. This technology could allow more scientists to study single neurons and learn how they interact with other cells to enable cognition, sensory perception, and other brain functions. Researchers could also use it to learn more about how neural circuits are affected by brain disorders.

If you interested in, please click the following link.

[News] What the brain's wiring looks like

posted Jul 11, 2017, 5:17 AM by 김동율

(from BBC news)

The world's most detailed scan of the brain's internal wiring has been produced by scientists at Cardiff University.
The MRI machine reveals the fibres which carry all the brain's thought processes.
It's been done in Cardiff, Nottingham, Cambridge and Stockport, as well as London England and London Ontario.
Doctors hope it will help increase understanding of a range of neurological disorders and could be used instead of invasive biopsies.

If you interested in, please click the following link.

[News] Peering into neural networks

posted Jul 10, 2017, 10:02 PM by DaWoon Heo
  (Image: Christine Daniloff/MIT)
[ "New technique helps elucidate the inner workings of neural networks trained on visual data" ]

 Neural networks is for performing computational tasks by learning. The learning is conducted by analyze large sets of training data.
However, it is difficult to recognize which data they are processing between input and output.
A researchers from Computer Science and Artificial Intelligence Laboratory (CSAIL) of MIT, showed a method to find out the process during training the visual scenes identification. From this study, Bau, one of the researcher of this study, mentioned that it suggests that "neural networks are actually trying to approximate getting a grandmother neuron".

If you interested in, please click the following link.

[NEWS] Scientists identify brain circuit that drives pleasure-inducing behavior

posted May 2, 2017, 2:34 AM by 조성만
(from MIT news)

Scientists have long believed that the central amygdala, a structure located deep within the brain, is linked with fear and responses to unpleasant events.
However, a team of MIT neuroscientists has now discovered a circuit in this structure that responds to rewarding events. In a study of mice, activating this circuit with certain stimuli made the animals seek those stimuli further. The researchers also found a circuit that controls responses to fearful events, but most of the neurons in the central amygdala are involved in the reward circuit, they report.

If you are interested in, please click following link :

[NEWS] Brain-controlled robots

posted Mar 19, 2017, 6:04 PM by 조성만
(from MIT news)

For robots to do what we want, they need to understand us. Too often, this means having to meet them halfway: teaching them the intricacies of human language, for example, or giving them explicit commands for very specific tasks.
But what if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking?
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University is working on this problem, creating a feedback system that lets people correct robot mistakes instantly with nothing more than their brains.
Using data from an electroencephalography (EEG) monitor that records brain activity, the system can detect if a person notices an error as a robot performs an object-sorting task. The team’s novel machine-learning algorithms enable the system to classify brain waves in the space of 10 to 30 milliseconds.

If you are interested in, please click below.

[NEWS] Sensor traces dopamine released by single cells

posted Feb 27, 2017, 6:33 PM by 조성만   [ updated Feb 27, 2017, 6:34 PM by DaWoon Heo ]
(from MIT news)

MIT chemical engineers have developed an extremely sensitive detector that can track single cells’ secretion of dopamine, a brain chemical responsible for carrying messages involved in reward-motivated behavior, learning, and memory. Using arrays of up to 20,000 tiny sensors, the researchers can monitor dopamine secretion of single neurons, allowing them to explore critical questions about dopamine dynamics. Until now, that has been very difficult to do.

Strano and his colleagues have already demonstrated that dopamine release occurs differently than scientists expected in a type of neural progenitor cell, helping to shed light on how dopamine may exert its effects in the brain.

If you are interested in, please click following link:

[Award] Best Poster Paper Award, BESK Workshop 2017 (Feb. 09)

posted Feb 12, 2017, 12:34 AM by DaWoon Heo   [ updated Mar 13, 2017, 12:09 AM by Hyun-Chul Kim ]

Award winner: Hyun-Chul Kim

Many congratulations!

[News] Better wisdom from crowds

posted Feb 10, 2017, 1:30 AM by 조성만   [ updated Feb 10, 2017, 1:40 AM by DaWoon Heo ]
(from MIT news)
The wisdom of crowds is not always perfect. But two scholars at MIT’s Sloan Neuroeconomics Lab, along with a colleague at Princeton University, have found a way to make it better.
Their method, explained in a newly published paper, uses a technique the researchers call the “surprisingly popular” algorithm to better extract correct answers from large groups of people. As such, it could refine wisdom-of-crowds surveys, which are used in political and economic forecasting, as well as many other collective activities, from pricing artworks to grading scientific research proposals.

If you are interested in, please click the following link.

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