Gatsby Computational Neuroscience Unit

London, United Kingdom

Gatsby Computational Neuroscience Unit

London, United Kingdom
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Beck J.,University of Rochester | Ma W.,Baylor College of Medicine | Pitkow X.,University of Rochester | Latham P.,Gatsby Computational Neuroscience Unit | And 3 more authors.
Neuron | Year: 2012

Behavior varies from trial to trial even when the stimulus is maintained as constant as possible. In many models, this variability is attributed to noise in the brain. Here, we propose that there is another major source of variability: suboptimal inference. Importantly, we argue that in most tasks of interest, and particularly complex ones, suboptimal inference is likely to be the dominant component of behavioral variability. This perspective explains a variety of intriguing observations, including why variability appears to be larger on the sensory than on the motor side, and why our sensors are sometimes surprisingly unreliable. Behavioral variability has often been attributed to noise in the brain. In this Perspective, Pouget and colleagues propose that there is another major source of variability, suboptimal inference, which is the dominant component of behavioral variability in complex tasks. © 2012 Elsevier Inc.

Kumaran D.,Google | Kumaran D.,University College London | Hassabis D.,Google | Hassabis D.,Gatsby Computational Neuroscience Unit | McClelland J.L.,Stanford University
Trends in Cognitive Sciences | Year: 2016

We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning. © 2016 Elsevier Ltd.

Montague P.R.,Virginia Polytechnic Institute and State University | Montague P.R.,University College London | Dolan R.J.,University College London | Friston K.J.,University College London | Dayan P.,Gatsby Computational Neuroscience Unit
Trends in Cognitive Sciences | Year: 2012

Computational ideas pervade many areas of science and have an integrative explanatory role in neuroscience and cognitive science. However, computational depictions of cognitive function have had surprisingly little impact on the way we assess mental illness because diseases of the mind have not been systematically conceptualized in computational terms. Here, we outline goals and nascent efforts in the new field of computational psychiatry, which seeks to characterize mental dysfunction in terms of aberrant computations over multiple scales. We highlight early efforts in this area that employ reinforcement learning and game theoretic frameworks to elucidate decision-making in health and disease. Looking forwards, we emphasize a need for theory development and large-scale computational phenotyping in human subjects. © 2011 Elsevier Ltd.

Dayan P.,Gatsby Computational Neuroscience Unit
Neuron | Year: 2012

Neural processing faces three rather different, and perniciously tied, communication problems. First, computation is radically distributed, yet point-to-point interconnections are limited. Second, the bulk of these connections are semantically uniform, lacking differentiation at their targets that could tag particular sorts of information. Third, the brain@s structure is relatively fixed, and yet different sorts of input, forms of processing, and rules for determining the output are appropriate under different, and possibly rapidly changing, conditions. Neuromodulators address these problems by their multifarious and broad distribution, by enjoying specialized receptor types in partially specific anatomical arrangements, and by their ability to mold the activity and sensitivity of neurons and the strength and plasticity of their synapses. Here, I offer a computationally focused review of algorithmic and implementational motifs associated with neuromodulators, using decision making in the face of uncertainty as a running example.

Kumaran D.,Google | Kumaran D.,University College London | Banino A.,Google | Blundell C.,Google | And 3 more authors.
Neuron | Year: 2016

Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. © 2016 The Authors

Lloyd K.,Gatsby Computational Neuroscience Unit | Dayan P.,Gatsby Computational Neuroscience Unit
Behavioral and Brain Functions | Year: 2016

We enjoy a sophisticated understanding of how animals learn to predict appetitive outcomes and direct their behaviour accordingly. This encompasses well-defined learning algorithms and details of how these might be implemented in the brain. Dopamine has played an important part in this unfolding story, appearing to embody a learning signal for predicting rewards and stamping in useful actions, while also being a modulator of behavioural vigour. By contrast, although choosing correct actions and executing them vigorously in the face of adversity is at least as important, our understanding of learning and behaviour in aversive settings is less well developed. We examine aversive processing through the medium of the role of dopamine and targets such as D2 receptors in the striatum. We consider critical factors such as the degree of control that an animal believes it exerts over key aspects of its environment, the distinction between 'better' and 'good' actual or predicted future states, and the potential requirement for a particular form of opponent to dopamine to ensure proper calibration of state values. © 2016 The Author(s).

Bejjanki V.R.,University of Rochester | Beck J.M.,University of Rochester | Beck J.M.,Gatsby Computational Neuroscience Unit | Lu Z.-L.,University of Southern California | Pouget A.,University of Rochester
Nature Neuroscience | Year: 2011

Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations. © 2011 Nature America, Inc. All rights reserved.

News Article | December 7, 2016

When you start a new job, it's normal to spend the first day working out who's who in the pecking order, information that will come in handy for making useful connections in the future. In an fMRI study published December 7 in Neuron, researchers at DeepMind and University College London provide new insights into how we acquire knowledge about social hierarchies, reveal the specific mechanisms at play when that hierarchy is our own (as compared to that of another person), and demonstrate that the brain automatically generates signals of social rank even when they're not needed to perform a task. The work could prove useful in guiding future research, not only in neuroscience, but also in artificial intelligence. In order to determine how we learn about social hierarchies, the authors asked 30 healthy college students to perform a task in the fMRI scanner. In this task, they learned about the power structure of a fictitious company that they imagined working in the future and that of one of their friends. They learned about the relative power of different people in each company, through watching "contests" between pairs of individuals and seeing who won. Once they understood the power structures of both companies, they then saw pictures of individual people from each company and had to decide which company the person worked for. "We found that the way in which participants learn about the power of individuals was best explained by a process of Bayesian inference" says Dharshan Kumaran, a research scientist at DeepMind. "Essentially you have an estimate about the level of power of each person, which you update as you receive new information (i.e., the outcome of a contest between 2 people." In this context, you can actually gain knowledge about how powerful someone is when they're not around: for example, if you see that Jane wins a contest against Paul, and later Paul wins many contests against other people, you should probably up your estimate of Jane's power because the evidence suggests that Paul is much better than you might have previously thought. So what this means is that people are able to rapidly form a coherent understanding of the whole hierarchy through putting together the outcome of different interactions between people, filling in missing pieces. "We found that different processes seem to be used for learning about and representing a social structure that you yourself are part of, compared to a social structure that involves someone else" says Dharshan Kumaran. "The prefrontal cortex, a region that is highly developed in humans, was particularly important when participants were learning about the power of people in their own social group, as compared to that of another person. This points towards the special nature of representing information that relates to the self." Indeed, sophisticated social interactions necessitate distinguishing one's own thoughts, goals, and preferences from those of other people--a cognitive function we know humans in particular excel at. "Part of the reason we do neuroscience research at DeepMind is because our ultimate goal is to develop artificial general intelligence that can be applied to solve some of the world's most intractable problems." says Kumaran. "Understanding how we ourselves learn structured forms of knowledge is a key component of what we'd call 'intelligence,' and it is therefore an important focus for our research." This work is supported by DeepMind in London, the Gatsby Computational Neuroscience Unit, the Institute of Cognitive Neuroscience at the University College London, and the Wellcome Trust. Neuron (@NeuroCellPress), published by Cell Press, is a bimonthly journal that has established itself as one of the most influential and relied upon journals in the field of neuroscience and one of the premier intellectual forums of the neuroscience community. It publishes interdisciplinary articles that integrate biophysical, cellular, developmental, and molecular approaches with a systems approach to sensory, motor, and higher-order cognitive functions. Visit: http://www. . To receive Cell Press media alerts, contact

News Article | March 4, 2016

The first major results of the Blue Brain Project, a detailed simulation of a bit of rat neocortex about the size of a grain of coarse sand, were published last year1. The model represents 31,000 brain cells and 37 million synapses. It runs on a supercomputer and is based on data collected over 20 years. Furthermore, it behaves just like a speck of brain tissue. But therein, say critics, lies the problem. “It's the best biophysical model we have of any brain, but that's not enough,” says Christof Koch, a neuroscientist at the Allen Institute for Brain Science in Seattle, Washington, which has embarked on its own large-scale brain-modelling effort. The trouble with the model is that it holds no surprises: no higher functions or unexpected features have emerged from it. Some neuroscientists, including Koch, say that this is because the model was not built with a particular hypothesis about cognitive processes in mind. Its success will depend on whether specific questions can be asked of it. The irony, says neuroscientist Alexandre Pouget, is that deriving answers will require drastic simplification of the model, “unless we figure out how to adjust the billions of parameters of the simulations, which would seem to be a challenging problem to say the least”. By contrast, Pouget's group at the University of Geneva, Switzerland, is generating and testing hypotheses on how the brain deals with uncertainty in functions such as attention and decision-making. There is a widespread preference for hypothesis-driven approaches in the brain-modelling community. Some models might be very small and detailed, for example, focusing on a single synapse. Others might explore the electrical spiking of whole neurons, the communication patterns between brain areas, or even attempt to recapitulate the whole brain. But ultimately a model needs to answer questions about brain function if we are to advance our understanding of cognition. Blue Brain is not the only sophisticated model to have hit the headlines in recent years. In late 2012, theoretical neuroscientist Chris Eliasmith at the University of Waterloo in Canada unveiled Spaun, a whole-brain model that contains 2.5 million neurons (a fraction of the human brain's estimated 86 billion). Spaun has a digital eye and a robotic arm, and can reason through eight complex tasks such as memorizing and reciting lists, all of which involve multiple areas of the brain2. Nevertheless, Henry Markram, a neurobiologist at the Swiss Federal Institute of Technology in Lausanne who is leading the Blue Brain Project, noted3 at the time: “It is not a brain model.” Although Markram's dismissal of Spaun amused Eliasmith, it did not surprise him. Markram is well known for taking a different approach to modelling, as he did in the Blue Brain Project. His strategy is to build in every possible detail to derive a perfect imitation of the biological processes in the brain with the hope that higher functions will emerge — a 'bottom-up' approach. Researchers such as Eliasmith and Pouget take a 'top-down' strategy, creating simpler models based on our knowledge of behaviour. These skate over certain details, instead focusing on testing hypotheses about brain function. Rather than dismiss the criticism, Eliasmith took Markram's comment on board and added bottom-up detail to Spaun. He selected a handful of frontal cortex neurons, which were relatively simple to begin with, and swapped them for much more complicated neurons — ones that account for multiple ion channels and changes in electrical activity over time. Although these complicated neurons were more biologically realistic, Eliasmith found that they brought no improvement to Spaun's performance on the original eight tasks. “A good model doesn't introduce complexity for complexity's sake,” he says. For many years, computational models of the brain were what theorists call unconstrained: there were not enough experimental data to map onto the models or to fully test them. For instance, scientists could record electrical activity, but from only one neuron at a time, which limited their ability to represent neural networks. Back then, brain models were simple out of necessity. In the past decade, an array of technologies has provided more information. Imaging technology has revealed previously hidden parts of the brain. Researchers can control genes to isolate particular functions. And emerging statistical methods have helped to describe complex phenomena in simpler terms. These techniques are feeding newer generations of models. Nevertheless, most theorists think that a good model includes only the details needed to help answer a specific question. Indeed, one of the most challenging aspects of model building is working out which details are important to include and which are acceptable to ignore. “The simpler the model is, the easier it is to analyse and understand, manipulate and test,” says cognitive and computational neuroscientist Anil Seth of the University of Sussex in Chichester, UK. An oft-cited success in theoretical neuroscience is the Reichardt detector — a simple, top-down model for how the brain senses motion — proposed by German physicist Werner Reichardt in the 1950s. “The big advantage of the Reichardt model for motion detection was that it was an algorithm to begin with,” says neurobiologist Alexander Borst of the Max Planck Institute of Neurobiology in Martinsried, Germany. “It doesn't speak about neurons at all.” When Borst joined the Max Planck Society in the mid-1980s, he ran computational simulations of the Reichardt model, and got surprising results. He found, for instance, that neurons oscillated when first presented with a pattern that was moving at constant velocity — a result that he took to Werner Reichardt, who was also taken aback. “He didn't expect his model to show that,” says Borst. They confirmed the results in real neurons, and continued to refine and expand Reichardt's model to gain insight into how the visual system detects motion. In the realm of bottom-up models, the greatest success has come from a set of equations developed in 1952 to explain how flow of ions in and out of a nerve cell produces an axon potential. These Hodgkin–Huxley equations are “beautiful and inspirational”, says neurobiologist Anthony Zador of Cold Spring Harbor Laboratory in New York, adding that they have allowed many scientists to make predictions about how neuronal excitability works. The equations, or their variants, form some of the basic building blocks of many of today's larger brain models of cognition. Although many theoretical neuroscientists do not see value in pure bottom-up approaches such as that taken by the Blue Brain Project, they do not dismiss bottom-up models entirely. These types of data-driven brain simulations have the benefit of reminding model-builders what they do not know, which can inspire new experiments. And top-down approaches can often benefit from the addition of more detail, says theoretical neuroscientist Peter Dayan of the Gatsby Computational Neuroscience Unit at University College London. “The best kind of modelling is going top-down and bottom-up simultaneously,” he says. Borst, for example, is now approaching the Reichardt detector from the bottom up to explore questions such as how neurotransmitter receptors on motion-sensitive neurons interact. And Eliasmith's more complex Spaun has allowed him to do other types of experiment that he couldn't before — in particular, he can now mimic the effect of sodium-channel blockers on the brain. Also taking a multiscale approach is neuroscientist Xiao-Jing Wang of New York University Shanghai in China, whose group described a large-scale model of the interaction of circuits across different regions of the macaque brain4. The model is built, in part, from his previous, smaller models of local neuronal circuits that show how neurons in a group fire in time. To scale up to the entire brain, Wang had to include the strength of the feedback between areas. Only now has he got the right data — thanks to the burgeoning field of connectomics (the study of connection maps within an organism's nervous system) — to build in this important detail, he says. Wang is using his model to study decision-making, the integration of sensory information and other cognitive processes. In physics, the marriage between experiment and theory led to the development of unifying principles. And although neuroscientists might hope for a similar revelation in their field, the brain (and biology in general) is inherently more noisy than a physical system, says computational neuroscientist Gustavo Deco of the Pompeu Fabra University in Barcelona, Spain, who is an investigator on the Human Brain Project. Deco points out that equations describing the behaviour of neurons and synapses are non-linear, and neurons are connected in a variety of ways, interacting in both a feedforward and a feedback manner. That said, there are examples of theory allowing neuroscientists to extract general principles, such as how the brain balances excitation and inhibition, and how neurons fire in synchrony, Wang says. Complex neuroscience often requires huge computational resources. But it is not a want of supercomputers that limits good, theory-driven models. “It is a lack of knowledge about experimental facts. We need more facts and maybe more ideas,” Borst says. Those who crave vast amounts of computer power misunderstand the real challenge facing scientists who are trying to unravel the mysteries of the brain, Borst contends. “I still don't see the need for simulating one million neurons simultaneously in order to understand what the brain is doing,” he says, referring to the large-scale simulation linked with the Human Brain Project. “I'm sure we can reduce that to a handful of neurons and get some ideas.” Computational neuroscientist Andreas Herz, of the Ludwig-Maximilians University in Munich, Germany, agrees. “We make best progress if we focus on specific elements of neural computation,” he says. For example, a single cortical neuron receives input from thousands of other cells, but it is unclear how it processes this information. “Without this knowledge, attempts to simulate the whole brain in a seemingly biologically realistic manner are doomed to fail,” he adds. At the same time, supercomputers do allow researchers to build details into their models and see how they compare to the originals, as with Spaun. Eliasmith has used Spaun and its variations to see what happens when he kills neurons or tweaks other features to investigate ageing, motor control or stroke damage in the brain. For him, adding complexity to a model has to serve a purpose. “We need to build bigger and bigger models in every direction, more neurons and more detail,” he says. “So that we can break them.”

Bahrami B.,University College London | Bahrami B.,Aarhus University Hospital | Olsen K.,Aarhus University Hospital | Latham P.E.,Gatsby Computational Neuroscience Unit | And 4 more authors.
Science | Year: 2010

In everyday life, many people believe that two heads are better than one. Our ability to solve problems together appears to be fundamental to the current dominance and future survival of the human species. But are two heads really better than one? We addressed this question in the context of a collective low-level perceptual decision-making task. For two observers of nearly equal visual sensitivity, two heads were definitely better than one, provided they were given the opportunity to communicate freely, even in the absence of any feedback about decision outcomes. But for observers with very different visual sensitivities, two heads were actually worse than the better one. These seemingly discrepant patterns of group behavior can be explained by a model in which two heads are Bayes optimal under the assumption that individuals accurately communicate their level of confidence on every trial.

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