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Internal state dynamics shape brainwide activity and foraging behaviour 期刊论文
NATURE, 2020, 577 (7789) : 239-+
作者:  Marques, Joao C.;  Li, Meng;  Schaak, Diane;  Robson, Drew N.;  Li, Jennifer M.
收藏  |  浏览/下载:5/0  |  提交时间:2020/07/03

The brain has persistent internal states that can modulate every aspect of an animal'  s mental experience(1-4). In complex tasks such as foraging, the internal state is dynamic(5-8). Caenorhabditis elegans alternate between local search and global dispersal(5). Rodents and primates exhibit trade-offs between exploitation and exploration(6,7). However, fundamental questions remain about how persistent states are maintained in the brain, which upstream networks drive state transitions and how state-encoding neurons exert neuromodulatory effects on sensory perception and decision-making to govern appropriate behaviour. Here, using tracking microscopy to monitor whole-brain neuronal activity at cellular resolution in freely moving zebrafish larvae(9), we show that zebrafish spontaneously alternate between two persistent internal states during foraging for live prey (Paramecia). In the exploitation state, the animal inhibits locomotion and promotes hunting, generating small, localized trajectories. In the exploration state, the animal promotes locomotion and suppresses hunting, generating long-ranging trajectories that enhance spatial dispersion. We uncover a dorsal raphe subpopulation with persistent activity that robustly encodes the exploitation state. The exploitation-state-encoding neurons, together with a multimodal trigger network that is associated with state transitions, form a stochastically activated nonlinear dynamical system. The activity of this oscillatory network correlates with a global retuning of sensorimotor transformations during foraging that leads to marked changes in both the motivation to hunt for prey and the accuracy of motor sequences during hunting. This work reveals an important hidden variable that shapes the temporal structure of motivation and decision-making.


  
Spin-cooling of the motion of a trapped diamond 期刊论文
NATURE, 2020
作者:  Auer, Thomas O.;  Khallaf, Mohammed A.;  Silbering, Ana F.;  Zappia, Giovanna;  Ellis, Kaitlyn;  Alvarez-Ocana, Raquel;  Arguello, J. Roman;  Hansson, Bill S.;  Jefferis, Gregory S. X. E.;  Caron, Sophie J. C.;  Knaden, Markus;  Benton, Richard
收藏  |  浏览/下载:15/0  |  提交时间:2020/07/03

Coupling the spins of many nitrogen-vacancy centres in a trapped diamond to its orientation produces a spin-dependent torque and spin-cooling of the motion of the diamond.


Observing and controlling macroscopic quantum systems has long been a driving force in quantum physics research. In particular, strong coupling between individual quantum systems and mechanical oscillators is being actively studied(1-3). Whereas both read-out of mechanical motion using coherent control of spin systems(4-9) and single-spin read-out using pristine oscillators have been demonstrated(10,11), temperature control of the motion of a macroscopic object using long-lived electronic spins has not been reported. Here we observe a spin-dependent torque and spin-cooling of the motion of a trapped microdiamond. Using a combination of microwave and laser excitation enables the spins of nitrogen-vacancy centres to act on the diamond orientation and to cool the diamond libration via a dynamical back-action. Furthermore, by driving the system in the nonlinear regime, we demonstrate bistability and self-sustained coherent oscillations stimulated by spin-mechanical coupling, which offers the prospect of spin-driven generation of non-classical states of motion. Such a levitating diamond-held in position by electric field gradients under vacuum-can operate as a '  compass'  with controlled dissipation and has potential use in high-precision torque sensing(12-14), emulation of the spin-boson problem(15) and probing of quantum phase transitions(16). In the single-spin limit(17) and using ultrapure nanoscale diamonds, it could allow quantum non-demolition read-out of the spin of nitrogen-vacancy centres at ambient conditions, deterministic entanglement between distant individual spins(18) and matter-wave interferometry(16,19,20).


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).