Award Abstract # 1443032
CRCNS: Computational Approaches to Uncover Neural Representation of Population Codes in Rodent Hippocampal-Cortical Circuits

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: NEW YORK UNIVERSITY
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 2013 = $817,405.00
FY 2016 = $50,554.00
History of Investigator:
  • Zhe Chen (Principal Investigator)
    Zhe.Chen@nyulangone.org
Recipient Sponsored Research Office: New York University Medical Center
550 1ST AVE
NEW YORK
NY  US  10016-6402
(212)263-8822
Sponsor Congressional District: 12
Primary Place of Performance: New York University Medical Center
NY  US  10016-6481
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): M5SZJ6VHUHN8
Parent UEI:
NSF Program(s): Cross-BIO Activities,
CRCNS-Computation Neuroscience,
MATHEMATICAL BIOLOGY
Primary Program Source: 01001314DB NSF RESEARCH & RELATED ACTIVIT
01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7327
Program Element Code(s): 727500, 732700, 733400
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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(Showing: 1 - 10 of 33)
Agarwal R, Chen Z, Kloosterman F, Wilson MA, Sarma SV "A novel nonparametric approach for neural encoding and decoding models of multimodal receptive fields" Neural Computation , v.28 , 2016 , p.1356
Agarwal R, Chen Z, Kloosterman F, Wilson MA, Sarma SV. "Neural encoding models of complex receptive fields: a comparison of nonparametric and parametric approaches." Proc. 50th Annual Conf. Information Sciences and Systems (CISS) , 2016 , p.562
Agarwal R, Chen Z, Sarma SV "Novel nonparametric maximum likelihood estimator for probability density functions" IEEE Transactions on Pattern Analysis & Machine Intelligence , v.39 , 2017 , p.1294
Chen Z "Unfolding representations of trajectory coding in neuronal population spike activity" Proc. 51st Conf. Information Science and Systems , 2017
Chen Z. "A primer on neural signal processing." IEEE Circuits and Systems Magazine , v.17 , 2016 , p.33
Chen, Z. and Gomperts, S. N. and Yamamoto, J. and Wilson, M. A. "Neural representation of spatial topology in the rodent hippocampus" Neural Computation , v.26 , 2014 , p.1--39
Chen Z, Gomperts SN, Yamamoto J, Wilson MA "Neural representation of spatial topology in the rodent hippocampus" Neural Computation , v.26 , 2014 , p.1
Chen Z, Grosmark A, Penagos H, Wilson MA "Uncovering representations of sleep-associated hippocampal ensemble spike activity" Scientific Reports , v.6 , 2016 , p.32193
Chen Z, Hu S, Zhang Q, Wang J. "Quickest detection for abrupt changes in neural ensemble spiking activity using model-based and model-free approaches" Proc. IEEE EMBS Neural Engineering Conference , 2017
Chen Z, Linderman SW, Wilson MA. "Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes." Proc. IEEE Machine Learning for Signal Processing (MLSP) , 2016
Chen Z, Liu S, Iriarte-Diaz J, Hatsopoulos NG, Ross CF, Takahashi K "Latent variable models for uncovering motor cortical ensemble dynamics" Proc. 51st Asilomar Conf. Signals, Systems, and Computers , 2017
(Showing: 1 - 10 of 33)

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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