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Chen ZS, Pesaran B. Improving scalability in systems neuroscience. Neuron 2021; 109:1776-1790. [PMID: 33831347 PMCID: PMC8178195 DOI: 10.1016/j.neuron.2021.03.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA; Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA.
| | - Bijan Pesaran
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neurology, New York University School of Medicine, New York, NY 10016, USA.
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Dai W, Lv L, Lu J, Hou H, Yan Q, Alam FE, Li Y, Zeng X, Yu J, Wei Q, Xu X, Wu J, Jiang N, Du S, Sun R, Xu J, Wong CP, Lin CT. A Paper-Like Inorganic Thermal Interface Material Composed of Hierarchically Structured Graphene/Silicon Carbide Nanorods. ACS NANO 2019; 13:1547-1554. [PMID: 30726676 DOI: 10.1021/acsnano.8b07337] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
With the increasing integration of devices in electronics fabrication, there are growing demands for thermal interface materials (TIMs) with high through-plane thermal conductivity for efficiently solving thermal management issues. Graphene-based papers consisting of a layer-by-layer stacked architecture have been commercially used as lateral heat spreaders; however, they lack in-depth studies on their TIM applications due to the low through-plane thermal conductivity (<6 W m-1 K-1). In this study, a graphene hybrid paper (GHP) was fabricated by the intercalation of silicon source and the in situ growth of SiC nanorods between graphene sheets based on the carbothermal reduction reaction. Due to the formation of covalent C-Si bonding at the graphene-SiC interface, the GHP possesses a superior through-plane thermal conductivity of 10.9 W m-1 K-1 and can be up to 17.6 W m-1 K-1 under packaging conditions at 75 psi. Compared with the current graphene-based papers, our GHP has the highest through-plane thermal conductivity value. In the TIM performance test, the cooling efficiency of the GHP achieves significant improvement compared to that of state-of-the-art thermal pads. Our GHP with characteristic structure is of great promise as an inorganic TIM for the highly efficient removal of heat from electronic devices.
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Affiliation(s)
- Wen Dai
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , PR China
| | | | | | | | | | - Fakhr E Alam
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , PR China
| | | | | | - Jinhong Yu
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , PR China
| | - Qiuping Wei
- State Key Laboratory of Powder Metallurgy, School of Materials Science and Engineering , Central South University , Changsha 410083 , PR China
| | - Xiangfan Xu
- Center for Phononics and Thermal Energy Science School of Physics Science and Engineering , Tongji University , Shanghai 200092 , China
| | - Jianbo Wu
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering , Shanghai Jiao Tong University , Shanghai , 200240 , PR China
| | - Nan Jiang
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , PR China
| | | | | | - Jianbin Xu
- Department of Electronics Engineering , The Chinese University of Hong Kong , Shatin , New Territories , Hong Kong 999077 , China
| | - Ching-Ping Wong
- School of Materials Science and Engineering , Georgia Institute of Technology , Atlanta , Georgia 30332 , United States
| | - Cheng-Te Lin
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , PR China
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Wang RM, Thakur CS, van Schaik A. An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator. Front Neurosci 2018; 12:213. [PMID: 29692702 PMCID: PMC5902707 DOI: 10.3389/fnins.2018.00213] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 03/16/2018] [Indexed: 11/13/2022] Open
Abstract
This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.
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Affiliation(s)
- Runchun M Wang
- The MARCS Institute, University of Western Sydney, Sydney, NSW, Australia
| | - Chetan S Thakur
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - André van Schaik
- The MARCS Institute, University of Western Sydney, Sydney, NSW, Australia
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Wang RM, Hamilton TJ, Tapson JC, van Schaik A. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks. Front Neurosci 2015; 9:180. [PMID: 26041985 PMCID: PMC4438254 DOI: 10.3389/fnins.2015.00180] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 05/06/2015] [Indexed: 11/24/2022] Open
Abstract
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform.
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Affiliation(s)
- Runchun M Wang
- The MARCS Institute, University of Western Sydney Sydney, NSW, Australia
| | - Tara J Hamilton
- The MARCS Institute, University of Western Sydney Sydney, NSW, Australia
| | - Jonathan C Tapson
- The MARCS Institute, University of Western Sydney Sydney, NSW, Australia
| | - André van Schaik
- The MARCS Institute, University of Western Sydney Sydney, NSW, Australia
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