2015 — 2018 |
Eden, Uri Tzvi (co-PI) [⬀] Frank, Loren M Ganguli, Surya Kepecs, Adam [⬀] Kramer, Mark Alan Machens, Christian Tolosa, Vanessa (co-PI) [⬀] |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Computational and Circuit Mechanisms For Information Transmission in the Brain @ Cold Spring Harbor Laboratory
? DESCRIPTION (provided by applicant): The brain is a massively interconnected network of regions, each of which contains neural circuits that process information related to combinations of sensory, motor and internal variables. Adaptive behavior requires that these regions communicate: sensory and internal information must be evaluated and used to make a decision, which must then be transformed into a motor output. Despite the importance of this question, we know relatively little about the principles of how spiking activity in one region influences activiy in downstream areas, particularly in the context of cognitive operations like decision-making. Here we propose to address this question by focusing on how the ventral striatum (VS), a region critical for motivational control of behavior receives and processes information from two important upstream regions, the orbitofrontal cortex (OFC) and the hippocampus (HP). We have assembled a unique team of scientists with complementary expertise studying the HP (Frank), OFC and VS (Kepecs), using synergistic technologies for large-scale recordings using novel polymer electrodes (Frank/Tolosa) with improved optogenetic identification of projections (Kepecs), and a team of statistical and computational researchers providing complementary analytical expertise in dimensionality reduction (Machens), statistical modeling (Eden/Kramer) and normative models (Ganguli). Our combined expertise will allow us to (1) measure large populations of neurons across the brain regions, (2) identify and (3) manipulate the neurons connecting them in order to (4) test for the first time a range of hypotheses about different modes and circuits for information transmission across regions. Beyond revealing how the OFC, HP and VS communicate during learning and decision-making, our approach will provide new experimental tools and computational methods for systems neuroscience, as well as new insights into the general principles of information transmission across regions.
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0.903 |
2017 — 2018 |
Ganguli, Surya Schnitzer, Mark J [⬀] |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Ensemble Neural Dynamics in the Medial Prefrontal Cortex Underlying Cognitive Flexibility and Reinforcement Learning
Abstract The prefrontal cortex is thought to play a crucial role in cognitive flexibility, in part by updating a person's expectations about the external world and the likely consequences of candidate actions based on the feedback gained from past actions. Deficits in this form of cognition occur in multiple psychiatric conditions in which prefrontal cortex is implicated. Despite much research, the mechanisms by which prefrontal neural circuits contribute to flexible decision-making and switches in cognitive strategy remain unclear. We will examine these issues using reinforcement learning theory, which specifies the optimal strategies for selecting future actions given a subject's past history of actions taken and rewards received. We will first gather the largest set of multi- neuronal recordings ever taken in prefrontal cortex, and then use reinforcement learning theory to analyze the data and deduce the circuit mechanisms by which the prefrontal cortex stores and updates its internal beliefs about the external world and the likely results of future actions. Past studies in behaving animals have found evidence for individual prefrontal cells that, on average, encode information related to cognitive strategy and action selection, but with limited data it has not been possible to identify how prefrontal circuits maintain and update this information over the course of multiple decisions, actions and outcomes. To collect sufficient data and create better models of prefrontal circuits, we will use a miniature microscope enabling us to monitor large neural ensembles in active mice. Our goals are to: (1) Develop and validate an experimental paradigm for imaging the concurrent dynamics of hundreds of prefrontal cells in mice flexibly switching between two different strategies of spatial navigation. Our pilot data show mice can perform the task well, that prefrontal activity is crucial for strategy-switching, and that prefrontal cortex contains cells whose dynamics appear to signal estimates of the optimal strategy. We will verify mice can follow bona fide navigation strategies and not just memorize spatial paths that yield reward. We will also confirm the prefrontal cells stay healthy and have normal activity patterns throughout the multi-day experiment. (2) Use reinforcement learning theory to analyze our large datasets and create neural circuit models of how prefrontal cortex stores and updates its beliefs to guide future actions. Using the theory we will first create observer-actor models of mouse behavior. We will then apply supervised and unsupervised methods of data analysis to assess whether prefrontal neural ensembles encode task-related, abstract variables such as belief and value. Using our observer-actor models and analyses of neural dynamics, we will train recurrent neural network models to solve the strategy-switching task. The resulting circuit models of reinforcement learning will then yield testable predictions about how the mice and prefrontal cells should behave when we modify the task. Overall, our study will address key unanswered questions about prefrontal function and seeks to attain a mechanistic understanding of how prefrontal circuits contribute to flexible decision-making.
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0.954 |
2018 — 2023 |
Chichilnisky, Eduardo Ganguli, Surya Lee, Jin Hyung (co-PI) [⬀] Mcclelland, James (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nrt: Neurotech - Bringing Technology to Neuroscience
Deciphering how the brain works could have untold impacts on medicine, technology, commerce, and our understanding of ourselves. For example, advances in neurotechnology could lead to brain-machine interfaces to overcome sensory impairments and loss of movement due to neurodegenerative disease. Many of the most important advances in neuroscience have required interaction with technical fields such as physics, electrical and chemical engineering, bioengineering, statistics, and computer science, and this will increasingly be the case as the field advances. However, the path for top students from these disciplines to enter the field of neuroscience has always been challenging because they lack the appropriate background and awareness of key questions and technological limitations in the field. This National Science Foundation Research Traineeship (NRT) award to Stanford University will accelerate fundamental developments in neuroscience by attracting promising young talent from these technical disciplines to neuroscience and training them to be leaders in the field. The program will allow students to apply technological developments in diverse fields to the most important problems in neuroscience today and train a new generation of neuroscientists who will bring these technologies to fruition in academia, medicine, and the private sector. The project anticipates training thirty (30) PhD students, including twelve (12) funded trainees, from physics, electrical and chemical engineering, bioengineering, materials science, computer science, and other technical fields.
This traineeship program consists of a novel integrated curriculum of coursework, internship and training experiences, and outreach to achieve its goals. The program will emphasize training for acquiring and analyzing vast data sets, enabling an understanding of nervous system circuitry at a scale that was unimaginable just a few years ago, and connecting the novel data to Stanford's strength in theory, inference from large data sets, and computational modeling. The program will introduce a rigorous multi-year curriculum for trainees, building on their home-discipline training and allowing them to collaborate with each other and with the members of the Neurosciences PhD program. Training will leverage the highly successful Stanford ADVANCE program that supports new PhD students with a special summer program prior to the start of graduate training, and build on it with several approaches customized to this program. The program will be specifically designed to optimize trainee preparation for a career in academia or in a technology industry setting, utilizing internship placements with both startups and established corporations.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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0.954 |
2020 — 2021 |
Clandinin, Thomas Robert [⬀] Ganguli, Surya Murthy, Mala (co-PI) [⬀] Scott, Kristin E (co-PI) [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Population Neural Activity Mediating Sensory Perception Across Modalities
Project Summary: Natural sensory inputs are typically complex, and often combine multiple modalities. Human speech, for example, combines auditory signals with visual cues, such as facial expressions, that inform the interpretation of the spoken words. As individual sensory pathways only provide a partial representation of the sensory information available, selecting the context-appropriate behavioral response to a multimodal stimulus often requires integrating information across modalities. How do neural circuits perform this fundamental computation? Our current understanding of sensory processing is predominantly built upon studies that have focused on single sensory modalities, working into the brain beginning from sensory receptors. As a result, we have a deep understanding of peripheral circuit computations in many different experimental contexts. However, working inward, cell-type by cell-type, has left our understanding of the circuits and computational principles that link sensation to action incomplete. Moreover, experimental strategies that focus exclusively on single sensory modalities cannot, by design, lead to insights into how the unified percepts that guide behavior can be assembled from information emerging in separate sensory processing streams. Here we leverage whole-brain imaging and advanced computational approaches to establish the fruit fly Drosophila as a model system for uncovering fundamental principles underpinning multisensory integration. This proposal has three goals. First, we will optimize whole-brain imaging in this experimental system, and use this technology to comprehensively characterize population dynamics underpinning the sensations of vision, mechanosensation and taste. Second, we will systematically quantify circuit interactions between these sensory modalities and across-animal variability, testing computational models of statistical inference, and identifying the algorithmic bases of multimodal integration. Third, we will link population dynamics to the response properties of single cell-types, providing a powerful path to characterizing circuit and synaptic mechanisms. Taken together, by developing and applying improved methods for large-scale monitoring of neural activity, combined with computational modeling and quantitative analysis, this project will greatly expand our understanding of sensory processing mechanisms across the brain.
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0.954 |
2021 |
Ganguli, Surya Huguenard, John R [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Tracking Pre-Seizure Dynamics to Predict and Control Seizures
Epileptic seizures are unpredictable events that significantly reduce quality of life. Predicting when the next seizure would occur could both prepare persons with epilepsy and their caregivers, and potentially aid in the treatment of seizures. Animal models of epilepsy provide an opportunity to explore the nature of brain activity in the period leading up to seizures. Using both mouse and rat models of generalized absence epilepsy, we have found a specific build up of thalamic neural spiking activity for several seconds before each seizure. This novel electrophysiological signature occurs in the absence of any overt epileptiform EEG activity. We propose to identify the neural circuits that are responsible for pre-seizure activity using high-density multi-channel silicon probes to record broadly across seizure-generating networks in the mouse. We will also measure calcium ion levels, a readout of neural activity, in neuronal cell bodies and their output axons using fluorescent calcium indicators (GCaMPs) and multiphoton microscopy to capture a highly complementary component of pre-seizure activity with high spatial resolution. Neural activity data will be collected together with EEG, locomotion signals, sensory-evoked responses, and pupil diameter to create a comprehensive multimodal stream of pre-seizure activity. This information will be fed into unbiased machine learning approaches to develop predictive algorithms. We will directly test coupling strength within thalamocortical pre-seizure networks by conducting network-level and targeted single-cell recordings in acute brain slices. To determine a specific role of pre- seizure networks in generating seizures, we will test whether chemogenetic or optogenetic silencing of key pre-seizure network elements reduces seizure incidence or severity. Finally, we will test whether we can use seizure-predictive signals to intervene in real-time and prevent seizures before they take hold. Together, these experiments will provide proof of concept for a novel therapeutic approach: targeting the pre-seizure state to improve seizure control.
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0.954 |