
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | May 15, 2014 |
Latest Amendment Date: | July 2, 2016 |
Award Number: | 1443032 |
Award Instrument: | Continuing Grant |
Program Manager: |
Kenneth Whang
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | April 1, 2014 |
End Date: | September 30, 2018 (Estimated) |
Total Intended Award Amount: | $867,960.00 |
Total Awarded Amount to Date: | $867,960.00 |
Funds Obligated to Date: |
FY 2016 = $50,554.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
550 1ST AVE NEW YORK NY US 10016-6402 (212)263-8822 |
Sponsor Congressional District: |
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Primary Place of Performance: |
NY US 10016-6481 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Cross-BIO Activities, CRCNS-Computation Neuroscience, MATHEMATICAL BIOLOGY |
Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Spatial navigation and episodic memory are important for daily activity and survival in rodents and primates. Episodic memory consists of collections of past experiences that occurred at a particular time and space, expressed in the form of sequences of temporal or spatial events. Spatial (topographical or topological) representation of the environment is pivotal for navigation. The hippocampus plays a significant role in both spatial representations and episodic memory. However, it remains unclear how the spikes of hippocampal neurons might be used by downstream structures in order to reconstruct the spatial environment without the a priori information of the place receptive fields. Little is known how the hippocampal neuronal representation might be affected by experimental manipulation. Furthermore, cortico-hippocampal interplay and communications are critical for memory consolidation, but many questions about their temporal coordination during sleep remains unresolved. This project proposes a collaborative proposal for studying the neural representation of population codes in rodent hippocampal-cortical circuits. The investigators and collaborators at MGH, MIT and Boston University will integrate innovative computational and experimental approaches to explore the neural codes during various spatial navigation and spatial/temporal memory tasks as well as during post-behavior sleep---as sleep is critical to hippocampal-dependent memory consolidation. Notably, due to the lack of measured behavior, it remains a great challenge to analyze or interpret sleep-associated hippocampal or cortical spike data.
The important questions central to this project are: how do hippocampal (or hippocampal-cortical) neuronal representations vary with respect to species (rat vs. mouse), animal (healthy vs. diseased), experience (novel vs. familiar), environment (one vs. two-dimensional), behavioral state (awake vs. sleep), and task (active vs. passive navigation; spatial working memory vs. temporal sequence memory). The investigators will simultaneously record ensemble spike activity from two or multiple areas of the rodent brain (hippocampus, primary visual cortex, prefrontal cortex, and retrosplenial cortex) under different experimental conditions, and will decipher the population codes using a coherent statistical framework. In light of Bayesian inference (variational Bayes or nonparametric Bayes), innovative unsupervised or semi-supervised learning approaches are developed for mining and visualizing sparse (in terms of both sample size and low firing rate) neuronal ensemble spike data.
The outcome of this investigation will improve the understanding of neural mechanisms of hippocampal (or hippocampal-cortical) population coding and its implications in learning, sleep and memory. The derived findings will shed light on the links between the variability of neural responses and the animal behavior (or other external factors), and will provide further insight into memory dysfunction (such as in Alzheimer's disease). Furthermore, this project has broader impacts in developing efficient algorithms to decipher neuronal population spike activity during behavior or sleep, as well as in discovering invariant topological representation of population codes in other cortical areas. In addition to the scientific significance, this proposal bears an educational component for training researchers on advanced quantitative skills in ensemble spike data analysis as well as for disseminating scientific resources (by sharing data and software) to a broad neuroscience community.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
NSF-CRCNS Project Outcome Report
PI: Zhe Chen
(10/01/2013 – 09/30/2018)
Intellectual Merit and Broader Impacts
This CRCNS project integrates collaborative effort and interdisciplinary techniques (rodent behavior, electrophysiology, population-decoding, computational statistics and machine learning) to uncover neural representations of population codes in the rodent hippocampal-neocortical circuits.
First, this project has provided great opportunities to train graduate/undergraduate students and postdoctoral fellows in the area of neurophysiology, computational neuroscience, statistics and biomedical engineering. Specifically, one female undergraduate student conducted research under this grant support. Second, this project has enhanced research collaborations between computational and experimental neuroscientists at the NYU, MIT, Baylor College of Medicine and NERF (Belgium). The two PIs (Dr. Zhe Chen and Dr. Matthew A. Wilson) are the members of the NSF-funded MIT Center for Brain, Minds and Machines (co-PI: M.A. Wilson), which foster new research collaborations across many institutions. Third, the project has promoted open-source software and data sharing. Finally, this project has promoted outreach activity through the annual Brain Awareness Week and Brain Day to increase the public awareness of our research.
Research outcome
Dissecting the hippocampal-neocortical circuit mechanisms requires us to simultaneously record the neural activity (including ensemble spikes and local field potentials) from rodent hippocampal-neocortical circuits during various wakeful behaviors and sleep. The development of large-scale rodent electrophysiology has enabled us to examine the neuronal population codes, and called for the need for developing innovative statistical tools to analyze these data efficiently.
Uncovering latent structures of population codes from the hippocampal-neocortical circuits is a great challenge in computational neuroscience. This question is particularly important while analyzing sleep-associated neural data in the complete absence of behavior. We have proposed a new unsupervised analysis paradigm (“memory first, content later”) for examining the content question of hippocampal-neocortical population codes, and tested our methods using rodent hippocampal-neocortical recordings during both spatial navigation and sleep. Our proposed method has also a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks (such as the nonhuman primate motor cortex during chewing behaviors).
Detection of memory replay candidates and assessment of their statistical significance is an active research topic in neuroscience. We have developed a state-of-the-art graphics processing unit (GPU)-powered decoding system for ultrafast reconstruction of spatial positions from rodent’s unsorted spatiotemporal spiking patterns, during run behavior, quiet wakefulness or sleep. Our system achieves ultrafast or real-time decoding speed (~fraction of millisecond per spike) and scalability up to thousands of channels. For the first time, by accommodating parallel shuffling in real time (latency <15 ms), our approach enables assessment of the statistical significance of online-decoded “memory replay” candidates during quiet wakefulness or sleep. Our system supports the decoding of spatial content or content-triggered experimental manipulation in closed-loop neuroscience experiments.
Sleep spindles have been implicated in memory consolidation and synaptic plasticity processes during NREM sleep. We have developed a novel deep learning strategy (SpindleNet) to detect sleep spindles from human EEG or rodent LFP recordings, based on a single EEG/LFP channel. Compared to other automatic spindle detection methods, our method is well suited for online closed-loop experiments. SpindleNet achieves superior detection accuracy and speed (latency 150-350 ms) and retains good performance under low EEG/LFP sampling frequencies and low signal-to-noise ratios; it also has good generalization across sleep datasets from various subject groups of different ages (children/young adult/elderly) and species (human/rat).
In summary, this NSF-funded project has produced innovative methods/algorithms and important technology advances that may accommodate the speed or scalability of future closed-loop neuroscience experiments.
Last Modified: 10/01/2018
Modified by: Zhe Chen
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