The striking spatial correlates of hippocampal place cells and grid cells have provided unique insights into how the brain constructs internal, cognitive representations of the environment and uses these representations to guide behavior. These spatially selective cells are influenced by both self-motion signals and by external sensory landmarks.
A fundamental goal for understanding the brain and mammalian and human intelligence, and to understand how processing goes awry in genetic and developmental diseases, is to understand the principles of operation of cerebral cortex. A key step is to understand "canonical" operations carried out by cortex. Here we will explore the operations of cortical circuitry in experiments guided by a new theory of a candidate canonical circuit operation. Sensory cortex must globally integrate localized sensory input to parse objects and support perception.
Understanding how different parts of the brain communicate is perhaps the most fundamental question of neuroscience because it is at the heart of understanding all brain functions and disorders. It is of clinical importance because numerous brain diseases - autism, schizophrenia, attention deficit disorder, and many others - are thought to be due to impaired communication among regions of the brain, and attention in particular is impaired in every major neurological disorder.
The network of peripheral nerves offers extraordinary potential for modulating and/or monitoring the functioning of internal organs or the brain. The nervous system functions by generating patterns of neural activity. To influence neural activity for desired outcomes, neural interface technology must access the appropriate peripheral nerve tissue, activate it in a focal targeted manner, and alter the patterns of activity.
Different parts of the brain share and transmit Information through long-range connections that connect nerve cells in each part of the brain with nerve cells in other brain areas. These connections form bundles of nerve cells, and these bundles comprise a complex and expansive network within the human brain. Understanding these brain networks and their relation to the way that the brain Implements perception and cognition, and understanding how these networks break down In various brain disorders are major challenges In contemporary neuroscience.
Human brainstem serves many plays critical roles in health and disease. Unfortunately, it has been vastly under-studied because of its physical inaccessibility in animal models, and its low contrast-to-noise ratio (CNR) for functional magnetic resonance imaging (fMRI) in human studies. At conventional fMRI field strengths, CNR is an order-of-magnitude lower in brainstem than in cerebral cortex. Recently, ultra-high-field (UHF) scanners are becoming more-and-more available for fMRI.
Deep Neural Network Approaches for Closed- Loop Deep Brain Stimulation Using Cortical and Subcortical Sensing Principal Investigators: R. Mark Richardson, MD, PhD, Department ofNeurologicaJ Surgery and Robert S. Turner, PhD, Department ofNeurobiology, University ofPittsburgh; Wolf-Julian Neumann, MD, and Andrea A. Kiihn, MD, Department ofNeurology, Charite-Universitatsmedizin Berlin. Co-Investigators: Benjamin Blankertz, PhD, Department of Computer Science, Technische Universitat Berlin; Tom Mitchell, PhD, Machine Learning Department, Carnegie Mellon University.
Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved.
The first major goal of this work is to learn how certain brain regions (olfactory system, hippocampus, and cerebellum) learn very complex stimuli that employ a combinatorial code to identify stimuli as points in a high-dimensional space. For example, the simple fruit fly olfactory system uses the firing rates of 50 different types of odorant receptors to identify each odor by placing it at a point in a SO-dimensional space.
When we speak, listeners hear us and understand us we speak correctly. But we also hear ourselves, and this auditory feedback affects our ongoing speech: delaying it causes dysfluency; perturbing its pitch or formants induces compensation. Yet we can also speak intelligibly even when we can't hear ourselves. For this reason, most models of speech motor control suppose that during speaking auditory processing is only engaged when auditory feedback is available. In this grant, we propose to investigate a computational model of speaking that represents a major departure from this.
