wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our networks contain leaky integrate-and-fire neurons (positive dissipation) as well as neurons with inverted sign of the leak current (negative dissipation) . We first show that such networkscan exhibit a balanced state of irregular , asynchronous spiking activity, if the neurons with negative dissipation are endowed with a voltage-dependent input cutoff, which may be interpreted as a simple conductance-based implementation of their synapses.
Revised:  Our networks contain leaky integrate-and-fire neurons that generalize irregularly spiking non-chaotic LIF neuron networks. Endowed with simple conductance-based synapses for XIF neurons, our networks can generate a balanced state of irregular , asynchronous spiking activity, if the neurons with negative dissipation are endowed with a voltage-dependent input cutoff, which may be interpreted as a simple conductance-based implementation of their synapses.
Edit intent: meaning-changed
Original:  We first show that such networkscan exhibit a balanced state of irregular , asynchronous spiking activity, if the neurons with negative dissipation are endowed with a voltage-dependent input cutoff, which may be interpreted as a simple conductance-based implementation of their synapses. We compute the voltage probability distributions and self-consistent firing rates for both types of neurons for finite size spike impacts assuming that inputs are Poissonian.
Revised:  We first show that such networkscan exhibit a balanced state of irregular asynchronous spiking as well. We determine the voltage probability distributions and self-consistent firing rates for both types of neurons for finite size spike impacts assuming that inputs are Poissonian.
Edit intent: clarity
Original:  We compute the voltage probability distributions and self-consistent firing rates for both types of neurons for finite size spike impacts assuming that inputs are Poissonian. The balanced state exists in a variety of neural network models and its dynamical properties such as chaoticity vary considerably. Our models generalize networks of leaky integrate-and-fire neurons where the dynamics are non-chaotic despite their irregularity. We show that switching the sign of the dissipation of a single neuron renders the entire network dynamics unstable and chaotic as indicated by a positive largest Lyapunov exponent. To further characterize the dynamics we consider the full spectrum of Lyapunov exponents and the related covariant Lyapunov vectors (CLVs) , which indicate the directions in which infinitesimal perturbations grow or shrink with a rate given by the Lyapunov exponents .
Revised:  We compute the voltage probability distributions and self-consistent firing rates for both types of neurons for finite size spike impacts . Further, we compute the full spectrum of Lyapunov exponents and the related covariant Lyapunov vectors (CLVs) , which indicate the directions in which infinitesimal perturbations grow or shrink with a rate given by the Lyapunov exponents .
Edit intent: coherence
Original:  To further characterize the dynamics we consider the full spectrum of Lyapunov exponents and the related covariant Lyapunov vectors (CLVs) , which indicate the directions in which infinitesimal perturbations grow or shrink with a rate given by the Lyapunov exponents .
Revised:  To further characterize the dynamics we consider the full spectrum of Lyapunov exponents (LEs) and the covariant Lyapunov vectors (CLVs) , which indicate the directions in which infinitesimal perturbations grow or shrink with a rate given by the Lyapunov exponents .
Edit intent: clarity
Original:  To further characterize the dynamics we consider the full spectrum of Lyapunov exponents and the related covariant Lyapunov vectors (CLVs) , which indicate the directions in which infinitesimal perturbations grow or shrink with a rate given by the Lyapunov exponents .
Revised:  To further characterize the dynamics we consider the full spectrum of Lyapunov exponents and the related covariant Lyapunov vectors (CLVs) specifying the corresponding perturbation directions .
Edit intent: clarity
Original:  We find that for each .. .
Revised:  We find that there is approximately one positive LE for each XIF neuron. This indicates in particular that a single XIF neuron renders the network dynamics chaotic. A simple mean-field approach, which can be justified by properties of the CLVs, explains the finding. As an application, we propose a spike-based computing scheme where our networks serve as computational reservoirs and their different stability properties yield different computational capabilities .
Edit intent: meaning-changed
Original: A distinguishing feature of living matter is its ability to control force-generating structures that accomplish tasks such as chromosome segregation, intracellular transport, and cell movement. To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised: A distinguishing feature of living matter is its ability to control force-generating , "active" molecules that create and manipulate non-equilibrium structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: clarity
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields that span up to millimeter length scales ] ] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity 1-3] ] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ]. Experimental active matter systems of biological or synthetic molecules are capable of URLanizing into structures ] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] 4,5] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] and generating global flows ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] 6-9] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] ]. However, these experimental systems lack the spatiotemporal control found in cells, limiting their utility for studying non-equilibrium phenomena and bioinspired engineering . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] ] . Here, we uncover non-equilibrium phenomena and principles by optically controlling structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] ] . Here, we control micron-scale structures and fluid flow in an engineered system of purified proteins .
Edit intent: meaning-changed
Original:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ]  ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of purified proteins .
Revised:  To generate and manipulate these micron-scale structures and force fields , cells create spatiotemporal patterns of molecular-scale activity ] ] ] . Here, we control micron-scale structures and fluid flow by manipulating molecular-scale protein activity set by dynamic optical boundaries in an engineered system of active biomolecules .
Edit intent: clarity
Original:  By composing these basic operations, we  create microtubule networks that can span several hundred microns in length and contract at speeds up to an order of magnitude faster than the speed of an individual motor.
Revised:  By composing these basic operations, we are able to create microtubule networks that can span several hundred microns in length and contract at speeds up to an order of magnitude faster than the speed of an individual motor.
Edit intent: clarity
Original: Despite the remarkable similarities between deep neural networks ( DNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks suggests that considerable differences still exist between the two systems.
Revised: Despite the remarkable similarities between convolutional neural networks ( DNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks suggests that considerable differences still exist between the two systems.
Edit intent: meaning-changed
Original: Despite the remarkable similarities between deep neural networks ( DNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks suggests that considerable differences still exist between the two systems.
Revised: Despite the remarkable similarities between deep neural networks ( CNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks suggests that considerable differences still exist between the two systems.
Edit intent: meaning-changed
Original: Despite the remarkable similarities between deep neural networks ( DNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks suggests that considerable differences still exist between the two systems.
Revised: Despite the remarkable similarities between deep neural networks ( DNN ) and the human brain as shown in previous studies, the fact that DNNs still fall behind humans in many visual tasks , indicating that there still exist considerable differences between the two systems.
Edit intent: clarity
Original:  To probe their dissimilarities , we leverage adversarial noise (AN) and adversarial interference (AI) images that yield distinct recognition performance in a prototypical DNN (AlexNet) and human vision.
Revised:  Here , we leverage adversarial noise (AN) and adversarial interference (AI) images that yield distinct recognition performance in a prototypical DNN (AlexNet) and human vision.
Edit intent: coherence
Original:  To probe their dissimilarities , we leverage adversarial noise (AN) and adversarial interference (AI) images that yield distinct recognition performance in a prototypical DNN (AlexNet) and human vision. The evoked activity by regular (RE) and adversarial images in both systems is thoroughly compared.
Revised:  To probe their dissimilarities , we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. Humans can successfully recognize AI images as corresponding categories but perceive AN images as meaningless noise. In contrast, CNNs can correctly recognize AN images but mistakenly classify AI images into wrong categories with surprisingly high confidence. We use functional magnetic resonance imaging to measure brain activity evoked by regular and adversarial images in both systems is thoroughly compared.
Edit intent: meaning-changed
Original:  The evoked activity by regular (RE) and adversarial images in both systems is thoroughly compared. We find that representational similarity between RE and adversarial images in the human brain resembles their perceptual similarity .
Revised:  The evoked activity by regular (RE) and adversarial images in the human brain, and compare it to the activity of artificial neurons in a prototypical CNN-AlexNet. In the human brain, we find that the representational similarity between RE and adversarial images in the human brain resembles their perceptual similarity .
Edit intent: meaning-changed
Original:  We find that representational similarity between RE and adversarial images in the human brain resembles their perceptual similarity . However, such representation-perception association is disrupted in the DNN. Especially, the representational similarity between RE and AN images idiosyncratically increases from low- to high-level layers .
Revised:  We find that representational similarity between RE and adversarial images in the human brain resembles their perceptual similarity in all early visual areas. In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for perceptual similarity .
Edit intent: meaning-changed
Original:  Taken together, our results provide a complementary perspective on the comparison between DNNs and the human brain , and highlight the need to characterize their differences to further bridge artificial and human intelligence research .
Revised:  Taken together, our results provide a complementary perspective on the comparison between DNNs and the human brain and AlexNet in the representation-perception relation suggest that future CNNs should emulate both behavior and the internal neural presentations of the human brain .
Edit intent: meaning-changed
Original:  The DM maximizes his or her continuation value under a worst-case model which lies within a nonparametric neighborhood of a benchmark model  .
Revised:  The DM maximizes his or her continuation value under a worst-case model which lies within a nonparametric neighborhood of a benchmark model that he or she fears is misspecified by a set of models. Decisions are evaluated under a "worst-case" model that delivers the lowest utility among all models in this set .
Edit intent: meaning-changed
Original:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of  recursion.
Revised:  To do so, new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of recursion.
Edit intent: coherence
Original:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of  recursion.
Revised:  To do so, we derive new fixed-point results are derived for monotone, convex operators that act on a Banach space of " thin-tailed " functions that arises naturally from the structure of recursion.
Edit intent: clarity
Original:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of  recursion.
Revised:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of thin-tailed " functions that arises naturally from the structure of recursion.
Edit intent: fluency
Original:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of  recursion.
Revised:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed functions arising naturally from the structure of recursion.
Edit intent: coherence
Original:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of  recursion.
Revised:  To do so, we derive new fixed-point results which use monotonicity and convexity of the value function recursion and which are embedded within a Banach space of " thin-tailed " functions that arises naturally from the structure of the continuation value recursion.
Edit intent: meaning-changed
Original:  The fixed-point results are quite general and are also applied to models where the DM learns about a hidden state and Rust-type dynamic discrete choice models . A perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model.
Revised:  The fixed-point results are quite general ; applications to models with learning and Rust-type dynamic discrete choice models . A perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model.
Edit intent: clarity
Original:  The fixed-point results are quite general and are also applied to models where the DM learns about a hidden state and Rust-type dynamic discrete choice models . A perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model.
Revised:  The fixed-point results are quite general and are also applied to models where the DM learns about a hidden state and Rust-type dynamic discrete choice models are also discussed. For estimation, a perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model.
Edit intent: meaning-changed
Original:  A robust consumption-investment problem is studied as an empirical application and some connections are drawn with the literature on macroeconomic uncertainty.
Revised:  The result also allows convergence rates of estimators to be characterized. An empirical application studies an endowment economy where the DM's benchmark model may be interpreted as an aggregate of experts' forecasting models. The application reveals time-variation in the way the DM pessimistically distorts benchmark probabilities. Consequences for asset pricing are explored and connections are drawn with the literature on macroeconomic uncertainty.
Edit intent: meaning-changed