Padmanabhan K.,Carnegie Mellon University |
Padmanabhan K.,Center for the Neural Basis of Cognition |
Urban N.N.,Carnegie Mellon University |
Urban N.N.,Center for the Neural Basis of Cognition |
Urban N.N.,University of Pittsburgh
Nature Neuroscience | Year: 2010
Although examples of variation and diversity exist throughout the nervous system, their importance remains a source of debate. Even neurons of the same molecular type have notable intrinsic differences. Largely unknown, however, is the degree to which these differences impair or assist neural coding. We examined the outputs from a single type of neuron, the mitral cells of the mouse olfactory bulb, to identical stimuli and found that each cell's spiking response was dictated by its unique biophysical fingerprint. Using this intrinsic heterogeneity, diverse populations were able to code for twofold more information than their homogeneous counterparts. In addition, biophysical variability alone reduced pair-wise output spike correlations to low levels. Our results indicate that intrinsic neuronal diversity is important for neural coding and is not simply the result of biological imprecision. © 2010 Nature America, Inc. All rights reserved.
Rosenbaum R.,University of Pittsburgh |
Doiron B.,Center for the Neural Basis of Cognition
Physical Review X | Year: 2014
Networks of model neurons with balanced recurrent excitation and inhibition capture the irregular and asynchronous spiking activity reported in cortex. While mean-field theories of spatially homogeneous balanced networks are well understood, a mean-field analysis of spatially heterogeneous balanced networks has not been fully developed. We extend the analysis of balanced networks to include a connection probability that depends on the spatial separation between neurons. In the continuum limit, we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. Notably, this implies that network models with broad recurrent inhibition are inconsistent with the balanced state. For finite size networks, we investigate the pattern-forming dynamics arising when balanced conditions are not satisfied. Our study highlights the new challenges that balanced networks pose for the spatiotemporal dynamics of complex systems.
Ever see something that isn't really there? Could your mind be playing tricks on you? The "tricks" might be your brain reacting to feedback between neurons in different parts of the visual system, according to a study published in The Journal of Neuroscience by Carnegie Mellon University Assistant Professor of Biological Sciences Sandra J. Kuhlman and colleagues. Understanding this feedback system could provide new insight into the visual system's neuronal circuitry and could have further implications for understanding how the brain interprets and understands sensory stimuli. Many optical illusions make you see something that's not there. Take the Kanizsa triangle: when you place three Pac-Man-like wedges in the right spot, you see a triangle, even though the edges of the triangle aren't drawn. "We see with both our brain and our eyes. Your brain is making inferences that allow you to see the triangle. It's connecting the dots between the corners of the wedges," said Kuhlman, who is a member of Carnegie Mellon's BrainHub neuroscience initiative and the joint Carnegie Mellon/University of Pittsburgh Center for the Neural Basis of Cognition (CNBC). "Optical illusions illustrate some of the amazing things our visual system can do." When we look at an object, information about what we see travels through circuits of neurons beginning in the retina, through the thalamus and into the brain's visual cortex. In the visual cortex, the information gets processed in multiple stages and is ultimately sent to the prefrontal cortex — the area of the brain that makes decisions, including how to respond to a given stimulus. However, not all information stays on this forward moving path. At the secondary stage of processing in the visual cortex some neurons reverse course and send information back to the first stage of processing. Researchers at Carnegie Mellon wondered if this feedback could change how the neurons in the visual cortex respond to a stimulus and alter the messages being sent to the prefrontal cortex. While there has been a good deal of research studying how information moves forward through the visual system, less has been done to study the impact of the information that moves backward. To find out if the information traveling from the secondary stage of processing back to the first stage impacted how information is encoded in the visual system, the researchers needed to quantify the magnitude of information that was being sent from the second stage back to the first stage. Using a mouse model, they recorded normal neuronal firing in the first stage of the visual cortex as the mouse looked at moving patterns that represented edges. They then silenced the neurons in the second stage using modified optogenetic technology. This halted the feedback of information from the second stage back to the first stage, and allowed the researchers to determine how much of the neuronal activity in the first stage of visual processing was the result of feedback. Twenty percent of the neuronal activity in the visual cortex was the result of feedback, a concept Kuhlman calls reciprocal connectivity. This indicates that some of the information coming from the visual cortex is not a direct response to a visual stimuli, but is a response to how the stimuli was perceived by higher cortical areas. The feedback, she said, might be what causes our brain to complete the undrawn lines in the Kanizsa triangle. But more importantly, it signifies that studying neuronal feedback is important to our understanding of how the brain works to process stimuli. "This represents a new way to study visual perception and neural computation. If we want to truly understand the visual pathway, and cortical function in general, we have to understand these reciprocal connection," Kuhlman said.
Carnegie Mellon University is embarking on a five-year, $12 million research effort to reverse-engineer the brain, seeking to unlock the secrets of neural circuitry and the brain’s learning methods. Researchers will use these insights to make computers think more like humans. The research project, led by Tai Sing Lee, professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC), is funded by the Intelligence Advanced Research Projects Activity (IARPA) through its Machine Intelligence from Cortical Networks (MICrONS) research program. MICrONS is advancing President Barack Obama’s BRAIN Initiative to revolutionize the understanding of the human brain. “MICrONS is similar in design and scope to the Human Genome Project, which first sequenced and mapped all human genes,” Lee said. “Its impact will likely be long-lasting and promises to be a game changer in neuroscience and artificial intelligence.” Lee will work with co-principal investigators Sandra Kuhlman, assistant professor of biological sciences at Carnegie Mellon and the CNBC, and Alan Yuille, the Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University, to discover the principles and rules the brain’s visual system uses to process information. This deeper understanding could serve as a springboard to revolutionize machine learning algorithms and computer vision. In particular, the researchers seek to improve the performance of neural networks — computational models for artificial intelligence inspired by the central nervous systems of animals. Interest in “neural nets,” which initially peaked in the 1990s, has recently undergone a resurgence thanks to growing computational power and datasets. Neural nets now are used in a wide variety of applications in which computers can learn to recognize faces, understand speech and handwriting, make decisions for self-driving cars, perform automated trading and detect financial fraud. “But today’s neural nets use algorithms that were essentially developed in the early 1980s,” Lee said. “Powerful as they are, they still aren’t nearly as efficient or powerful as those used by the human brain. For instance, to learn to recognize an object, a computer might need to be shown thousands of labeled examples and taught in a supervised manner, while a person would require only a handful and might not need supervision.” Artificial neural nets process information in one direction, from input nodes to output nodes. But the brain likely works in quite a different way. Neurons in the brain are highly interconnected, suggesting possible feedback loops at each processing step. What these connections are doing computationally is a mystery; solving that mystery could enable the design of more capable neural nets. To better understand these connections, Kuhlman will use a technique called “2-photon calcium imaging microscopy” to record signaling of tens of thousands of individual neurons in mice as they process visual information, an unprecedented feat. In the past, only a single neuron, or tens of neurons, typically have been sampled in an experiment, she noted. “By incorporating molecular sensors to monitor neural activity in combination with sophisticated optical methods, it is now possible to simultaneously track the neural dynamics of most, if not all, of the neurons within a brain region,” Kuhlman said. “As a result we will produce a massive dataset that will give us a detailed picture of how neurons in one region of the visual cortex behave.” Meanwhile, the CMU-led team will collaborate with another MICrONS team at the Wyss Institute for Biologically Inspired Engineering, led by George Church, professor of genetics at Harvard Medical School. The Harvard-led team, working with investigators at Cold Spring Harbor Laboratory, MIT and Columbia University, is developing revolutionary techniques to reconstruct the complete circuitry of the neurons recorded at CMU. The database, along with two other databases contributed by other MICrONS teams, unprecedented in scale, will be made publicly available for research groups all over the world. In this MICrONS project, CMU researchers and their collaborators in other universities will use these massive databases to evaluate a number of computational and learning models as they improve their understanding of the brain’s computational principles and reverse-engineer the data to build better computer algorithms for learning and pattern recognition. “The hope is that this knowledge will lead to the development of a new generation of machine learning algorithms that will allow AI machines to learn without supervision and from a few examples, which are hallmarks of human intelligence,” Lee said. “Extracting the brain’s secret algorithms in learning and inference from this massive amount of data to advance machine learning is extremely ambitious and might be the most uncertain part of this project,” said Andrew Moore, dean of CMU’s School of Computer Science. “It’s the equivalent of a moonshot, but CMU is one of the world’s best places to do this, because we have a very strong tradition and community in artificial intelligence. We also have a strong community of theoretical and experimental neuroscientists working with the Center for the Neural Basis of Cognition and the university’s BrainHub initiative.” The CNBC is a collaborative center between Carnegie Mellon and the University of Pittsburgh. BrainHub is a neuroscience research initiative that brings together the university’s strengths in biology, computer science, psychology, statistics and engineering to foster research on understanding how the structure and activity of the brain give rise to complex behaviors. In addition to Kuhlman and Yuille, the MICrONS team includes Abhinav Gupta, assistant professor of robotics; Gary Miller, professor of computer science; Rob Kass, professor of statistics and machine learning and interim co-director of the CNBC; Byron Yu, associate professor of electrical and computer engineering and biomedical engineering and the CNBC; and Steve Chase, assistant professor of biomedical engineering and the CNBC. Another member is Ruslan Salakhutdinov, one of the co-creators of the deep belief network, a new model of machine learning that was inspired by recurrent connections in the brain. Salakhutdinov will join CMU as an assistant professor of machine learning in the fall. Other members of the team include Brent Doiron, associate professor of mathematics at Pitt, and Spencer Smith, assistant professor of neuroscience and neuro-engineering at the University of North Carolina.
Totah N.K.B.,University of Pittsburgh |
Totah N.K.B.,Center for the Neural Basis of Cognition |
Kim Y.,University of Pittsburgh |
Moghaddam B.,University of Pittsburgh
Journal of Neurophysiology | Year: 2013
Dopamine neurons of the ventral tegmental area (VTA) signal the occurrence of a reward-predicting conditioned stimulus (CS) with a subsecond duration increase in post-CS firing rate. Important theories about reward-prediction error and reward expectancy have been informed by the substantial number of studies that have examined post-CS phasic VTA neuron activity. On the other hand, the role of VTA neurons in anticipation of a reward-predicting CS and analysis of prestimulus spike rate rarely has been studied. We recorded from the VTA in rats during the 3-choice reaction time task, which has a fixed-duration prestimulus period and a difficult-to-detect stimulus. Use of a stimulus that was difficult to detect led to behavioral errors, which allowed us to compare VTA activity between trials with correct and incorrect stimulus-guided choices. We found a sustained increase in firing rate of both putative dopamine and GABA neurons during the pre-CS period of correct and incorrect trials. The poststimulus phasic response, however, was absent on incorrect trials, suggesting that the stimulusevoked phasic response of dopamine neurons may relate to stimulus detection. The prestimulus activation of VTA neurons may modulate cortical systems that represent internal states of stimulus expectation and provide a mechanism for dopamine neurotransmission to influence preparatory attention to an expected stimulus. © 2013 the American Physiological Society.