Scientists Afflict Computers With 'Schizophrenia' to Better Understand the Human Brain

Scientists Afflict Computers With 'Schizophrenia' to Better Understand the Human Brain

  5:30:00 PM    Science Lover

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Computer networks that can't forget fast enough can 
show symptoms of a kind of virtual schizophrenia, 
giving researchers further clues to the inner workings 
of schizophrenic brains, researchers have found. 
(Credit: © Nikolai Sorokin / Fotolia)

Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

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Brain Coprocessors The need for operating systems to help brains and machines work together.

Brain Coprocessors The need for operating systems to help brains and machines work together.

  9:50:00 AM    Science Lover

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The last few decades have seen a surge of invention of technologies that enable the observation or perturbation of information in the brain. Functional MRI, which measures blood flow changes associated with brain activity, is being explored for purposes as diverse as lie detection, prediction of human decision making, and assessment of language recovery after stroke.

Implanted electrical stimulators, which enable control of neural circuit activity, are borne by hundreds of thousands of people to treat conditions such as deafness, Parkinson's disease, and obsessive-compulsive disorder. And new methods, such as the use of light to activate or silence specific neurons in the brain, are being widely utilized by researchers to reveal insights into how to control neural circuits to achieve therapeutically useful changes in brain dynamics. We are entering a neurotechnology renaissance, in which the toolbox for understanding the brain and engineering its functions is expanding in both scope and power at an unprecedented rate.

This toolbox has grown to the point where the strategic utilization of multiple neurotechnologies in conjunction with one another, as a system, may yield fundamental new capabilities, both scientific and clinical, beyond what they can offer alone. For example, consider a system that reads out activity from a brain circuit, computes a strategy for controlling the circuit so it enters a desired state or performs a specific computation, and then delivers information into the brain to achieve this control strategy. Such a system would enable brain computations to be guided by predefined goals set by the patient or clinician, or adaptively steered in response to the circumstances of the patient's environment or the instantaneous state of the patient's brain.

Some examples of this kind of "brain coprocessor" technology are under active development, such as systems that perturb the epileptic brain when a seizure is electrically observed, and prosthetics for amputees that record nerves to control artificial limbs and stimulate nerves to provide sensory feedback. Looking down the line, such system architectures might be capable of very advanced functions--providing just-in-time information to the brain of a patient with dementia to augment cognition, or sculpting the risk-taking profile of an addiction patient in the presence of stimuli that prompt cravings.

Given the ever-increasing number of brain readout and control technologies available, a generalized brain coprocessor architecture could be enabled by defining common interfaces governing how component technologies talk to one another, as well as an "operating system" that defines how the overall system works as a unified whole--analogous to the way personal computers govern the interaction of their component hard drives, memories, processors, and displays. Such a brain coprocessor platform could facilitate innovation by enabling neuroengineers to focus on neural prosthetics at an algorithmic level, much as a computer programmer can work on a computer at a conceptual level without having to plan the fate of every individual bit. In addition, if new technologies come along, e.g., a new kind of neural recording technology, they could be incorporated into a system, and in principle rapidly coupled to existing computation and perturbation methods, without requiring the heavy readaptation of those other components.

Developing such brain coprocessor architectures would take some work--in particular, it would require technologies standardized enough, or perhaps open enough, to be interoperable in a variety of combinations. Nevertheless, much could be learned from developing relatively simple prototype systems. For example, recording technologies by themselves can report brain activity, but cannot fully attest to the causal contribution that the observed brain activity makes to a specific behavioral or clinical outcome; control technologies can input information into neural targets, but by themselves their outcomes might be difficult to interpret due to endogenous neural information and unobserved neural processing. These scientific issues can be disambiguated by rudimentary brain coprocessors, built with readily available off-the-shelf components, that use recording technologies to assess how a given neural circuit perturbation alters brain dynamics. Such explorations may begin to reveal principles governing how best to control a circuit--revealing the neural targets and control strategies that most efficaciously lead to a goal brain state or behavioral effect, and thus pointing the way to new therapeutic strategies. Miniature, implantable brain coprocessors might be able to support new kinds of personalized medicine, for example continuously adapting a neural control strategy to the goals, state, environment, and history of an individual patient--important powers, given the dynamic nature of many brain disorders.

In the future, the computational module of a brain coprocessor may be powerful enough to assist in high-level human cognition or complex decision making. Of course, the augmentation of human intelligence has been one of the key goals of computer engineers for well over half a century. Indeed, if we relax the definition of brain coprocessor just a bit, so as not to require direct physical access to the brain, many consumer technologies being developed today are converging upon brain coprocessor-like architectures. A large number of new technologies are attempting to discover information useful to a user and to deliver this information to the user in real time. Also, these discovery and delivery processes are increasingly shaped by the environment (e.g., location) and history (e.g., social interactions, searches) of the user. Thus we are seeing a departure from the classical view (as initially anticipated by early thinkers about human-machine symbiosis such as J. C. R. Licklider) in which computers receive goals from humans, perform defined computations, and then provide the results back to humans.

Of course, giving machines the authority to serve as proactive human coprocessors, and allowing them to capture our attention with their computed priorities, has to be considered carefully, as anyone who has lost hours due to interruption by a slew of social-network updates or search-engine alerts can attest. How can we give the human brain access to increasingly proactive coprocessing technologies without losing sight of our overarching goals? One idea is to develop and deploy metrics that allow us to evaluate the IQ of a human plus a coprocessor, working together--evaluating the performance of collaborating natural and artificial intelligences in a broad battery of problem-solving contexts. After all, humans with Internet-based brain coprocessors (e.g., laptops running Web browsers) may be more distractible if the goals include long, focused writing tasks, but they may be better at synthesizing data broadly from disparate sources; a given brain coprocessor configuration may be good for some problems but bad for others. Thinking of emerging computational technologies as brain coprocessors forces us to think about them in terms of the impacts they have on the brain, positive and negative, and importantly provides a framework for thoughtfully engineering their direct, as well as their emergent, effects.

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'Plasmobot': Scientists To Design First Robot Using Mould

'Plasmobot': Scientists To Design First Robot Using Mould

  10:23:00 PM    Science Lover

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Scientists at the University of the West of England are to design the first ever biological robot using mould.

Plasmodium used in the research.
(Credit: Image courtesy of University of the West of England)

Researchers have received a Leverhulme Trust grant worth £228,000 to develop the amorphous non-silicon biological robot, plasmobot, using plasmodium, the vegetative stage of the slime mould Physarum polycephalum, a commonly occurring mould which lives in forests, gardens and most damp places in the UK. The Leverhulme Trust funded research project aims to design the first every fully biological (no silicon components) amorphous massively-parallel robot.

This project is at the forefront of research into unconventional computing. Professor Andy Adamatzky, who is leading the project, says their previous research has already proved the ability of the mould to have computational abilities.

Professor Adamatzky explains, “Most people’s idea of a computer is a piece of hardware with software designed to carry out specific tasks. This mould, or plasmodium, is a naturally occurring substance with its own embedded intelligence. It propagates and searches for sources of nutrients and when it finds such sources it branches out in a series of veins of protoplasm. The plasmodium is capable of solving complex computational tasks, such as the shortest path between points and other logical calculations. Through previous experiments we have already demonstrated the ability of this mould to transport objects. By feeding it oat flakes, it grows tubes which oscillate and make it move in a certain direction carrying objects with it. We can also use light or chemical stimuli to make it grow in a certain direction.

“This new plasmodium robot, called plasmobot, will sense objects, span them in the shortest and best way possible, and transport tiny objects along pre-programmed directions. The robots will have parallel inputs and outputs, a network of sensors and the number crunching power of super computers. The plasmobot will be controlled by spatial gradients of light, electro-magnetic fields and the characteristics of the substrate on which it is placed. It will be a fully controllable and programmable amorphous intelligent robot with an embedded massively parallel computer.”

This research will lay the groundwork for further investigations into the ways in which this mould can be harnessed for its powerful computational abilities.

Professor Adamatzky says that there are long term potential benefits from harnessing this power, “We are at the very early stages of our understanding of how the potential of the plasmodium can be applied, but in years to come we may be able to use the ability of the mould for example to deliver a small quantity of a chemical substance to a target, using light to help to propel it, or the movement could be used to help assemble micro-components of machines. In the very distant future we may be able to harness the power of plasmodia within the human body, for example to enable drugs to be delivered to certain parts of the human body. It might also be possible for thousands of tiny computers made of plasmodia to live on our skin and carry out routine tasks freeing up our brain for other things. Many scientists see this as a potential development of amorphous computing, but it is purely theoretical at the moment.”

Professor Adamatzky has recently edited and had published by Springer, ‘Artificial Life Models in Hardware’ aimed at students and researchers of robotics. The book focuses on the design and real-world implementation of artificial life robotic devices and covers a range of hopping, climbing, swimming robots, neural networks and slime mould and chemical brains.

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