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Meng J, Ahamed T, Yu B, Hung W, EI Mouridi S, Wang Z, Zhang Y, Wen Q, Boulin T, Gao S, Zhen M. A tonically active master neuron modulates mutually exclusive motor states at two timescales. SCIENCE ADVANCES 2024; 10:eadk0002. [PMID: 38598630 PMCID: PMC11006214 DOI: 10.1126/sciadv.adk0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/07/2024] [Indexed: 04/12/2024]
Abstract
Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.
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Affiliation(s)
- Jun Meng
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Tosif Ahamed
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Bin Yu
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wesley Hung
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Sonia EI Mouridi
- University Claude Bernard Lyon 1, MeLiS, CNRS UMR 5284, INSERM U1314, 69008 Lyon, France
| | - Zezhen Wang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yongning Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Quan Wen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Thomas Boulin
- University Claude Bernard Lyon 1, MeLiS, CNRS UMR 5284, INSERM U1314, 69008 Lyon, France
| | - Shangbang Gao
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mei Zhen
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
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Sanaullah, Koravuna S, Rückert U, Jungeblut T. Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. Int J Neural Syst 2023; 33:2350044. [PMID: 37604777 DOI: 10.1142/s0129065723500442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality by building neurons and synapses that mimic the human brain's transmission of electrical signals. However, optimal SNN implementation requires a precise balance of parametric values. To design such ubiquitous neural networks, a graphical tool for visualizing, analyzing, and explaining the internal behavior of spikes is crucial. Although some popular SNN simulators are available, these tools do not allow users to interact with the neural network during simulation. To this end, we have introduced the first runtime interactive simulator, called Runtime Analyzing and Visualization Simulator (RAVSim),a developed to analyze and dynamically visualize the behavior of SNNs, allowing end-users to interact, observe output concentration reactions, and make changes directly during the simulation. In this paper, we present RAVSim with the current implementation of runtime interaction using the LIF neural model with different connectivity schemes, an image classification model using SNNs, and a dataset creation feature. Our main objective is to primarily investigate binary classification using SNNs with RGB images. We created a feed-forward network using the LIF neural model for an image classification algorithm and evaluated it by using RAVSim. The algorithm classifies faces with and without masks, achieving an accuracy of 91.8% using 1000 neurons in a hidden layer, 0.0758 MSE, and an execution time of ∼10[Formula: see text]min on the CPU. The experimental results show that using RAVSim not only increases network design speed but also accelerates user learning capability.
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Affiliation(s)
- Sanaullah
- Department of Engineering and Mathematics, Bielefeld University of Applied Science, Bielefeld, Germany
| | - Shamini Koravuna
- Department of Cognitive Interaction Technology Center, Bielefeld University, Bielefeld, Germany
| | - Ulrich Rückert
- Department of Cognitive Interaction Technology Center, Bielefeld University, Bielefeld, Germany
| | - Thorsten Jungeblut
- Department of Engineering and Mathematics, Bielefeld University of Applied Science, Bielefeld, Germany
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3
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Müller-Cleve SF, Fra V, Khacef L, Pequeño-Zurro A, Klepatsch D, Forno E, Ivanovich DG, Rastogi S, Urgese G, Zenke F, Bartolozzi C. Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Front Neurosci 2022; 16:951164. [PMID: 36440280 PMCID: PMC9695069 DOI: 10.3389/fnins.2022.951164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/19/2022] [Indexed: 03/25/2024] Open
Abstract
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through Braille letter reading. We recorded a new Braille letters dataset based on the capacitive tactile sensors of the iCub robot's fingertip. We then investigated the importance of spatial and temporal information as well as the impact of event-based encoding on spike-based computation. Afterward, we trained and compared feedforward and recurrent Spiking Neural Networks (SNNs) offline using Backpropagation Through Time (BPTT) with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We compared our approach to standard classifiers, in particular to the Long Short-Term Memory (LSTM) deployed on the embedded NVIDIA Jetson GPU, in terms of classification accuracy, power, and energy consumption together with computational delay. Our results show that the LSTM reaches ~97% of accuracy, outperforming the recurrent SNN by ~17% when using continuous frame-based data instead of event-based inputs. However, the recurrent SNN on Loihi with event-based inputs is ~500 times more energy-efficient than the LSTM on Jetson, requiring a total power of only ~30 mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware, and spike-based computing for spatio-temporal pattern recognition at the edge.
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Affiliation(s)
| | - Vittorio Fra
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Lyes Khacef
- Bio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
| | | | - Daniel Klepatsch
- Silicon Austria Labs, Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Graz, Austria
- Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Johannes Kepler University Linz, Graz, Austria
| | - Evelina Forno
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Diego G. Ivanovich
- Silicon Austria Labs, Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Graz, Austria
- Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Johannes Kepler University Linz, Graz, Austria
| | - Shavika Rastogi
- International Centre for Neuromorphic Systems, Western Sydney University, Penrith, NSW, Australia
- Biocomputation Research Group, University of Hertfordshire, Hatfield, United Kingdom
| | - Gianvito Urgese
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Chiara Bartolozzi
- Istituto Italiano di Tecnologia, Event-Driven Perception in Robotics, Genoa, Italy
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Chen BW, Yang SH, Kuo CH, Chen JW, Lo YC, Kuo YT, Lin YC, Chang HC, Lin SH, Yu X, Qu B, Ro SCV, Lai HY, Chen YY. Neuro-Inspired Reinforcement Learning To Improve Trajectory Prediction In Reward-Guided Behavior. Int J Neural Syst 2022; 32:2250038. [DOI: 10.1142/s0129065722500381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Schittler Neves F, Timme M. Decoding complex state space trajectories for neural computing. CHAOS (WOODBURY, N.Y.) 2021; 31:123105. [PMID: 34972334 DOI: 10.1063/5.0053429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
In biological neural circuits as well as in bio-inspired information processing systems, trajectories in high-dimensional state-space encode the solutions to computational tasks performed by complex dynamical systems. Due to the high state-space dimensionality and the number of possible encoding trajectories rapidly growing with input signal dimension, decoding these trajectories constitutes a major challenge on its own, in particular, as exponentially growing (space or time) requirements for decoding would render the original computational paradigm inefficient. Here, we suggest an approach to overcome this problem. We propose an efficient decoding scheme for trajectories emerging in spiking neural circuits that exhibit linear scaling with input signal dimensionality. We focus on the dynamics near a sequence of unstable saddle states that naturally emerge in a range of physical systems and provide a novel paradigm for analog computing, for instance, in the form of heteroclinic computing. Identifying simple measures of coordinated activity (synchrony) that are commonly applicable to all trajectories representing the same percept, we design robust readouts whose sizes and time requirements increase only linearly with the system size. These results move the conceptual boundary so far hindering the implementation of heteroclinic computing in hardware and may also catalyze efficient decoding strategies in spiking neural networks in general.
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Affiliation(s)
- Fabio Schittler Neves
- Center for Advancing Electronics Dresden (CFAED) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
| | - Marc Timme
- Center for Advancing Electronics Dresden (CFAED) and Institute for Theoretical Physics, TU Dresden, 01062 Dresden, Germany
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6
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Yan Y, Burgess N, Bicanski A. A model of head direction and landmark coding in complex environments. PLoS Comput Biol 2021; 17:e1009434. [PMID: 34570749 PMCID: PMC8496825 DOI: 10.1371/journal.pcbi.1009434] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 10/07/2021] [Accepted: 09/08/2021] [Indexed: 01/27/2023] Open
Abstract
Environmental information is required to stabilize estimates of head direction (HD) based on angular path integration. However, it is unclear how this happens in real-world (visually complex) environments. We present a computational model of how visual feedback can stabilize HD information in environments that contain multiple cues of varying stability and directional specificity. We show how combinations of feature-specific visual inputs can generate a stable unimodal landmark bearing signal, even in the presence of multiple cues and ambiguous directional specificity. This signal is associated with the retrosplenial HD signal (inherited from thalamic HD cells) and conveys feedback to the subcortical HD circuitry. The model predicts neurons with a unimodal encoding of the egocentric orientation of the array of landmarks, rather than any one particular landmark. The relationship between these abstract landmark bearing neurons and head direction cells is reminiscent of the relationship between place cells and grid cells. Their unimodal encoding is formed from visual inputs via a modified version of Oja's Subspace Algorithm. The rule allows the landmark bearing signal to disconnect from directionally unstable or ephemeral cues, incorporate newly added stable cues, support orientation across many different environments (high memory capacity), and is consistent with recent empirical findings on bidirectional HD firing reported in the retrosplenial cortex. Our account of visual feedback for HD stabilization provides a novel perspective on neural mechanisms of spatial navigation within richer sensory environments, and makes experimentally testable predictions.
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Affiliation(s)
- Yijia Yan
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Neil Burgess
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Andrej Bicanski
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- School of Psychology, Newcastle University, Newcastle upon Tyne, United Kingdom
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Toomey E, Segall K, Castellani M, Colangelo M, Lynch N, Berggren KK. Superconducting Nanowire Spiking Element for Neural Networks. NANO LETTERS 2020; 20:8059-8066. [PMID: 32965119 DOI: 10.1021/acs.nanolett.0c03057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ∼10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.
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Affiliation(s)
- E Toomey
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - K Segall
- Department of Physics and Astronomy, Colgate University, Hamilton, New York 13346, United States
| | - M Castellani
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - M Colangelo
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - N Lynch
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - K K Berggren
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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Huyck CR, Vergani AA. Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. J Comput Neurosci 2020; 48:299-316. [PMID: 32715350 DOI: 10.1007/s10827-020-00758-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 11/27/2022]
Abstract
Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; however, these bumps can be merged by overcoming the repeller dynamics. A simple associative memory can include these bump attractors, allowing the use of continuous variables in these memories, and these associations can be learned by Hebbian rules. These simulations are related to biological networks, showing that this is a step toward a more complete neural cognitive associative memory.
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Bolding KA, Franks KM. Recurrent cortical circuits implement concentration-invariant odor coding. Science 2018; 361:361/6407/eaat6904. [PMID: 30213885 DOI: 10.1126/science.aat6904] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 08/03/2018] [Indexed: 12/28/2022]
Abstract
Animals rely on olfaction to find food, attract mates, and avoid predators. To support these behaviors, they must be able to identify odors across different odorant concentrations. The neural circuit operations that implement this concentration invariance remain unclear. We found that despite concentration-dependence in the olfactory bulb (OB), representations of odor identity were preserved downstream, in the piriform cortex (PCx). The OB cells responding earliest after inhalation drove robust responses in sparse subsets of PCx neurons. Recurrent collateral connections broadcast their activation across the PCx, recruiting global feedback inhibition that rapidly truncated and suppressed cortical activity for the remainder of the sniff, discounting the impact of slower, concentration-dependent OB inputs. Eliminating recurrent collateral output amplified PCx odor responses rendered the cortex steeply concentration-dependent and abolished concentration-invariant identity decoding.
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Affiliation(s)
- Kevin A Bolding
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Kevin M Franks
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA.
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Burylko O, Kazanovich Y, Borisyuk R. Winner-take-all in a phase oscillator system with adaptation. Sci Rep 2018; 8:416. [PMID: 29323149 PMCID: PMC5765106 DOI: 10.1038/s41598-017-18666-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 12/15/2017] [Indexed: 11/09/2022] Open
Abstract
We consider a system of generalized phase oscillators with a central element and radial connections. In contrast to conventional phase oscillators of the Kuramoto type, the dynamic variables in our system include not only the phase of each oscillator but also the natural frequency of the central oscillator, and the connection strengths from the peripheral oscillators to the central oscillator. With appropriate parameter values the system demonstrates winner-take-all behavior in terms of the competition between peripheral oscillators for the synchronization with the central oscillator. Conditions for the winner-take-all regime are derived for stationary and non-stationary types of system dynamics. Bifurcation analysis of the transition from stationary to non-stationary winner-take-all dynamics is presented. A new bifurcation type called a Saddle Node on Invariant Torus (SNIT) bifurcation was observed and is described in detail. Computer simulations of the system allow an optimal choice of parameters for winner-take-all implementation.
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Affiliation(s)
- Oleksandr Burylko
- Institute of Mathematics, National Academy of Sciences of Ukraine, Tereshchenkivska 3, 01601, Kyiv, Ukraine.
| | - Yakov Kazanovich
- Institute of Mathematical Problems of Biology, The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290, Pushchino, Russia
| | - Roman Borisyuk
- Institute of Mathematical Problems of Biology, The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290, Pushchino, Russia.,School of Computing and Mathematics, Plymouth University, PL4 8AA, Plymouth, United Kingdom
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Dasgupta S, Stevens CF, Navlakha S. A neural algorithm for a fundamental computing problem. Science 2017; 358:793-796. [DOI: 10.1126/science.aam9868] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 09/25/2017] [Indexed: 12/18/2022]
Abstract
Similarity search—for example, identifying similar images in a database or similar documents on the web—is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem.
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Affiliation(s)
- Sanjoy Dasgupta
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Charles F. Stevens
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA, USA
| | - Saket Navlakha
- Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
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