Showing posts with label Human Brain Project. Show all posts
Showing posts with label Human Brain Project. Show all posts

Artificial Intelligence - The Human Brain Project


The European Union's major brain research endeavor is the Human Brain Project.

The project, which encompasses Big Science in terms of the number of participants and its lofty ambitions, is a multidisciplinary coalition of over one hundred partner institutions and includes professionals from the disciplines of computer science, neurology, and robotics.

The Human Brain Project was launched in 2013 as an EU Future and Emerging Technologies initiative with a budget of over one billion euros.

The ten-year project aims to make fundamental advancements in neuroscience, medicine, and computer technology.

Researchers working on the Human Brain Project hope to learn more about how the brain functions and how to imitate its computing skills.

Human Brain Organization, Systems and Cognitive Neuroscience, Theoretical Neuroscience, and implementations such as the Neuroinformatics Platform, Brain Simulation Platform, Medical Informatics Platform, and Neuromorphic Computing Platform are among the twelve subprojects of the Human Brain Project.

Six information and communication technology platforms were released by the Human Brain Project in 2016 as the main research infrastructure for ongoing brain research.

The project's research is focused on the creation of neuromorphic (brain-inspired) computer chips, in addition to infrastructure established for gathering and distributing data from the scientific community.

BrainScaleS is a subproject that uses analog signals to simulate the neuron and its synapses.

SpiNNaker (Spiking Neural Network Design) is a supercomputer architecture based on numerical models operating on special multicore digital devices.

The Neurorobotic Platform is another ambitious subprogram, where "virtual brain models meet actual or simulated robot bodies" (Fauteux 2019).

The project's modeling of the human brain, which includes 100 billion neurons with 7,000 synaptic connections to other neurons, necessitates massive computational resources.

Computer models of the brain are created on six supercomputers at research sites around Europe.

These models are currently being used by project researchers to examine illnesses.

The show has been panned.

Scientists protested in a 2014 open letter to the European Commission about the program's lack of openness and governance, as well as the program's small breadth of study in comparison to its initial goal and objectives.

The Human Brain Project has a new governance structure as a result of an examination and review of its financing procedures, needs, and stated aims.


Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Blue Brain Project; Cognitive Computing; SyNAPSE.

Further Reading:

Amunts, Katrin, Christoph Ebell, Jeff Muller, Martin Telefont, Alois Knoll, and Thomas Lippert. 2016. “The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain.” Neuron 92, no. 3 (November): 574–81.

Fauteux, Christian. 2019. “The Progress and Future of the Human Brain Project.” Scitech Europa, February 15, 2019.

Markram, Henry. 2012. “The Human Brain Project.” Scientific American 306, no. 6 

(June): 50–55.

Markram, Henry, Karlheinz Meier, Thomas Lippert, Sten Grillner, Richard Frackowiak, 

Stanislas Dehaene, Alois Knoll, Haim Sompolinsky, Kris Verstreken, Javier 

DeFelipe, Seth Grant, Jean-Pierre Changeux, and Alois Sariam. 2011. “Introduc￾ing the Human Brain Project.” Procedia Computer Science 7: 39–42.

Artificial Intelligence - What Is Cognitive Computing?


Self-learning hardware and software systems that use machine learning, natural language processing, pattern recognition, human-computer interaction, and data mining technologies to mimic the human brain are referred to as cognitive computing.

The term "cognitive computing" refers to the use of advances in cognitive science to create new and complex artificial intelligence systems.

Cognitive systems aren't designed to take the place of human thinking, reasoning, problem-solving, or decision-making; rather, they're meant to supplement or aid people.

A collection of strategies to promote the aims of affective computing, which entails narrowing the gap between computer technology and human emotions, is frequently referred to as cognitive computing.

Real-time adaptive learning approaches, interactive cloud services, interactive memo ries, and contextual understanding are some of these methodologies.

To conduct quantitative assessments of organized statistical data and aid in decision-making, cognitive analytical tools are used.

Other scientific and economic systems often include these tools.

Complex event processing systems utilize complex algorithms to assess real-time data regarding events for patterns and trends, offer choices, and make judgments.

These kinds of systems are widely used in algorithmic stock trading and credit card fraud detection.

Face recognition and complex image recognition are now possible with image recognition systems.

Machine learning algorithms build models from data sets and improve as new information is added.

Neural networks, Bayesian classifiers, and support vector machines may all be used in machine learning.

Natural language processing entails the use of software to extract meaning from enormous amounts of data generated by human conversation.

Watson from IBM and Siri from Apple are two examples.

Natural language comprehension is perhaps cognitive computing's Holy Grail or "killer app," and many people associate natural language processing with cognitive computing.

Heuristic programming and expert systems are two of the oldest branches of so-called cognitive computing.

Since the 1980s, there have been four reasonably "full" cognitive computing architectures: Cyc, Soar, Society of Mind, and Neurocognitive Networks.

Speech recognition, sentiment analysis, face identification, risk assessment, fraud detection, and behavioral suggestions are some of the applications of cognitive computing technology.

These applications are referred regarded as "cognitive analytics" systems when used together.

In the aerospace and defense industries, agriculture, travel and transportation, banking, health care and the life sciences, entertainment and media, natural resource development, utilities, real estate, retail, manufacturing and sales, marketing, customer service, hospitality, and leisure, these systems are in development or are being used.

Netflix's movie rental suggestion algorithm is an early example of predictive cognitive computing.

Computer vision algorithms are being used by General Electric to detect tired or distracted drivers.

Customers of Domino's Pizza can place orders online by speaking with a virtual assistant named Dom.

Elements of Google Now, a predictive search feature that debuted in Google applications in 2012, assist users in predicting road conditions and anticipated arrival times, locating hotels and restaurants, and remembering anniversaries and parking spots.

In IBM marketing materials, the term "cognitive" computing appears frequently.

Cognitive computing, according to the company, is a subset of "augmented intelligence," which is preferred over artificial intelligence.

The Watson machine from IBM is frequently referred to as a "cognitive computer" since it deviates from the traditional von Neumann design and instead draws influence from neural networks.

Neuroscientists are researching the inner workings of the human brain, seeking for connections between neuronal assemblies and mental aspects, and generating new mental ideas.

Hebbian theory is an example of a neuroscientific theory that underpins cognitive computer machine learning implementations.

The Hebbian theory is a proposed explanation for neural adaptation during the learning process.

Donald Hebb initially proposed the hypothesis in his 1949 book The Organization of Behavior.

Learning, according to Hebb, is a process in which the causal induction of recurrent or persistent neuronal firing or activity causes neural traces to become stable.

"Any two cells or systems of cells that are consistently active at the same time will likely to become'associated,' such that activity in one favors activity in the other," Hebb added (Hebb 1949, 70).

"Cells that fire together, wire together," is how the idea is frequently summarized.

According to this hypothesis, the connection of neuronal cells and tissues generates neurologically defined "engrams" that explain how memories are preserved in the brain as biophysical or biochemical changes.

Engrams' actual location, as well as the procedures by which they are formed, are currently unknown.

IBM machines are stated to learn by aggregating information into a computational convolution or neural network architecture made up of weights stored in a parallel memory system.

Intel introduced Loihi, a cognitive chip that replicates the functions of neurons and synapses, in 2017.

Loihi is touted to be 1,000 times more energy efficient than existing neurosynaptic devices, with 128 clusters of 1,024 simulated neurons on per chip, for a total of 131,072 simulated neurons.

Instead of relying on simulated neural networks and parallel processing with the overarching goal of developing artificial cognition, Loihi uses purpose-built neural pathways imprinted in silicon.

These neuromorphic processors are likely to play a significant role in future portable and wire-free electronics, as well as automobiles.

Roger Schank, a cognitive scientist and artificial intelligence pioneer, is a vocal opponent of cognitive computing technology.

"Watson isn't thinking. You can only reason if you have objectives, plans, and strategies to achieve them, as well as an understanding of other people's ideas and a knowledge of prior events to draw on.

"Having a point of view is also beneficial," he writes.

"How does Watson feel about ISIS, for example?" Is this a stupid question? ISIS is a topic on which actual thinking creatures have an opinion" (Schank 2017).

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Computational Neuroscience; General and Narrow AI; Human Brain Project; SyNAPSE.

Further Reading

Hebb, Donald O. 1949. The Organization of Behavior. New York: Wiley.

Kelly, John, and Steve Hamm. 2013. Smart Machines: IBM’s Watson and the Era of Cognitive Computing. New York: Columbia University Press.

Modha, Dharmendra S., Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh. 2011. “Cognitive Computing.” Communications of the ACM 54, no. 8 (August): 62–71.

Schank, Roger. 2017. “Cognitive Computing Is Not Cognitive at All.” FinTech Futures, May 25.

Vernon, David, Giorgio Metta, and Giulio Sandini. 2007. “A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents.” IEEE Transactions on Evolutionary Computation 11, no. 2: 151–80.

Artificial Intelligence - What Is The Blue Brain Project (BBP)?


The brain, with its 100 billion neurons, is one of the most complicated physical systems known.

It is an organ that takes constant effort to comprehend and interpret.

Similarly, digital reconstruction models of the brain and its activity need huge and long-term processing resources.

The Blue Brain Project, a Swiss brain research program supported by the École Polytechnique Fédérale de Lausanne (EPFL), was founded in 2005. Henry Markram is the Blue Brain Project's founder and director.

The purpose of the Blue Brain Project is to simulate numerous mammalian brains in order to "ultimately, explore the stages involved in the formation of biological intelligence" (Markram 2006, 153).

These simulations were originally powered by IBM's BlueGene/L, the world's most powerful supercomputer system from November 2004 to November 2007.

In 2009, the BlueGene/L was superseded by the BlueGene/P.

BlueGene/P was superseded by BlueGene/Q in 2014 due to a need for even greater processing capability.

The BBP picked Hewlett-Packard to build a supercomputer (named Blue Brain 5) devoted only to neuroscience simulation in 2018.

The use of supercomputer-based simulations has pushed neuroscience research away from the physical lab and into the virtual realm.

The Blue Brain Project's development of digital brain reconstructions enables studies to be carried out in a "in silico" environment, a Latin pseudo-word referring to modeling of biological systems on computing equipment, using a regulated research flow and methodology.

The possibility for supercomputers to turn the analog brain into a digital replica suggests a paradigm change in brain research.

One fundamental assumption is that the digital or synthetic duplicate will act similarly to a real or analog brain.

Michael Hines, John W. Moore, and Ted Carnevale created the software that runs on Blue Gene hardware, a simulation environment called NEURON that mimics neurons.

The Blue Brain Project may be regarded a typical example of what was dubbed Big Science following World War II (1939–1945) because of the expanding budgets, pricey equipment, and numerous interdisciplinary scientists participating.


Furthermore, the scientific approach to the brain via simulation and digital imaging processes creates issues such as data management.

Blue Brain joined the Human Brain Project (HBP) consortium as an initial member and submitted a proposal to the European Commission's Future & Emerging Technologies (FET) Flagship Program.

The European Union approved the Blue Brain Project's proposal in 2013, and the Blue Brain Project is now a partner in a larger effort to investigate and undertake brain simulation.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

General and Narrow AI; Human Brain Project; SyNAPSE.

Further Reading

Djurfeldt, Mikael, Mikael Lundqvist, Christopher Johansson, Martin Rehn, Örjan Ekeberg, Anders Lansner. 2008. “Brain-Scale Simulation of the Neocortex on the IBM Blue Gene/L Supercomputer.” IBM Journal of Research and Development 52, no. 1–2: 31–41.

Markram, Henry. 2006. “The Blue Brain Project.” Nature Reviews Neuroscience 7, no. 2: 153–60.

Markram, Henry, et al. 2015. “Reconstruction and Simulation of Neocortical Microcircuitry.” Cell 63, no. 2: 456–92.

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