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Quantitative Biology

New submissions

[ total of 16 entries: 1-16 ]
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New submissions for Fri, 23 Feb 18

[1]  arXiv:1802.07834 [pdf, other]
Title: Learning to Gather without Communication
Comments: Preliminary version, presented at the 5th Biological Distributed Algorithms Workshop. Washington D.C, July 28th, 2017
Subjects: Populations and Evolution (q-bio.PE); Distributed, Parallel, and Cluster Computing (cs.DC); Learning (cs.LG); Multiagent Systems (cs.MA); Machine Learning (stat.ML)

A standard belief on emerging collective behavior is that it emerges from simple individual rules. Most of the mathematical research on such collective behavior starts from imperative individual rules, like always go to the center. But how could an (optimal) individual rule emerge during a short period within the group lifetime, especially if communication is not available. We argue that such rules can actually emerge in a group in a short span of time via collective (multi-agent) reinforcement learning, i.e learning via rewards and punishments. We consider the gathering problem: several agents (social animals, swarming robots...) must gather around a same position, which is not determined in advance. They must do so without communication on their planned decision, just by looking at the position of other agents. We present the first experimental evidence that a gathering behavior can be learned without communication in a partially observable environment. The learned behavior has the same properties as a self-stabilizing distributed algorithm, as processes can gather from any initial state (and thus tolerate any transient failure). Besides, we show that it is possible to tolerate the brutal loss of up to 90\% of agents without significant impact on the behavior.

[2]  arXiv:1802.07875 [pdf, ps, other]
Title: The stationary distribution of a Wright-Fisher diffusion model with general small mutation rates
Comments: 14 pages, 0 figures
Subjects: Populations and Evolution (q-bio.PE)

The stationary distribution of a sample taken from a Wright-Fisher diffusion with general small mutation rates is found using a coalescent approach. The approximation is equivalent to having at most one mutation in the coalescent tree to the first order in the rates. The approach is different from Burden and Tang [1, 2] who use a probability flux argument to obtain the same results from a forward diffusion generator equation. The solution has interest because the solution is not known when rates are not small. An analogous solution is found for the configuration of alleles in a general exchangeable binary coalescent tree. In particular an explicit solution is found for a pure birth process tree when individuals reproduce at rate {\lambda}.

[3]  arXiv:1802.07905 [pdf, other]
Title: Closed-loop control of a modular neuromorphic biohybrid
Subjects: Neurons and Cognition (q-bio.NC)

Neural networks modularity is a major challenge for the development of control circuits of neural activity. Under physiological limitations, the accessible regions for external stimulation are possibly different from the functionally relevant ones, requiring complex indirect control designs. Moreover, control over one region might affect activity of other downstream networks, once sparse connections exist. We address these questions by developing a hybrid device of a cortical culture functionally integrated with a biomimetic hardware neural network. This design enables the study of modular networks controllability, while connectivity is well-defined and key features of cortical networks are accessible. Using a closed-loop control to monitor the activity of the coupled hybrid, we show that both modules are congruently modified, in the macroscopic as well as the microscopic activity levels. Control impacts efficiently the activity on both sides whether the control circuit is an indirect series one, or implemented independently only on one of the modules. Hence, these results present global functional impacts of a local control intervention. Overall, this strategy provides an experimental access to the controllability of neural activity irregularities, when embedded in a modular organization.

[4]  arXiv:1802.07914 [pdf, other]
Title: Synthesizing a Clock Signal with Reactions---Part I: Duty Cycle Implementation Based on Gears
Authors: Chuan Zhang (1 and 2 and 3), Lulu Ge (1 and 2 and 3), Xiaohu You (2) ((1) Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS), (2) National Mobile Communications Research Laboratory, (3) Quantum Information Center, Southeast University, China)
Subjects: Molecular Networks (q-bio.MN); Chemical Physics (physics.chem-ph)

Timing is of fundamental importance in biology and our life. Borrowing ideas from mechanism, we map our clock signals onto a gear system, in pursuit of better depiction of a clock signal implemented with chemical reaction networks (CRNs). On a chassis of gear theory, more quantitative descriptions are offered for our method. Inspired by gears, our work to synthesize a tunable clock signal could be divided into two parts. Part I, this paper, mainly focuses on the implementation of clock signals with three types of duty cycles, namely $1/2$, $1/N$ ($N > 2$), and $M/N$. Part II devotes itself in addressing frequency alteration issues of clock signals. \textcolor{black}{Guaranteed by existing literature, the experimental chassis can be taken care of by DNA strand displacement reactions, which lay a solid foundation for the physical implementation of nearly arbitrary CRNs.

[5]  arXiv:1802.07922 [pdf, other]
Title: Synthesizing a Clock Signal with Reactions---Part II: Frequency Alteration Based on Gears
Authors: Chuan Zhang (1 and 2 and 3), Lulu Ge (1 and 2 and 3), Xiaohu You (2) ((1) Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS), (2) National Mobile Communications Research Laboratory, (3) Quantum Information Center, Southeast University, China)
Subjects: Molecular Networks (q-bio.MN); Chemical Physics (physics.chem-ph)

On a chassis of gear model, we have offered a quantitative description for our method to synthesize a chemical clock signal with various duty cycles in Part I. As Part II of the study, this paper devotes itself in proposing a design methodology to handle frequency alteration issues for the chemical clock, including both frequency division and frequency multiplication. Several interesting examples are provided for a better explanation of our contribution. All the simulation results verify and validate the correctness and efficiency of our proposal.

[6]  arXiv:1802.08024 [pdf]
Title: Chemical Heredity as Group Selection at the Molecular Level
Comments: Keywords: Composomes, Evolution, Cooperation, Price equation, Group selection, Compositional information, Population dynamics
Subjects: Populations and Evolution (q-bio.PE)

Many examples of cooperation exist in biology. In chemical systems however, which can sometimes be quite complex, we do not appear to observe intricate cooperative interactions. A key question for the origin of life, is then how can molecular cooperation first arise in an abiotic system prior to the emergence of biological replication. We postulate that selection at the molecular level is a driving force behind the complexification of chemical systems, particularly during the origins of life. In the theory of multilevel selection the two selective forces are: within-group and between-group, where the former tends to favor "selfish" replication of individuals and the latter favor cooperation between individuals enhancing the replication of the group as a whole. These forces can be quantified using the Price equation, which is a standard tool used in evolutionary biology to quantify evolutionary change. Our central claim is that replication and heredity in chemical systems are subject to selection, and quantifiable using the multilevel Price equation. We demonstrate this using the Graded Autocatalysis Replication Domain computer model, describing simple protocell composed out of molecules and its replication, which respectively analogue to the group and the individuals. In contrast to previous treatments of this model, we treat the lipid molecules themselves as replicating individuals and the protocells they form as groups of individuals. Our goal is to demonstrate how evolutionary biology tools and concepts can be applied in chemistry and we suggest that molecular cooperation may arise as a result of group selection. Further, the biological relation of parent-progeny is proposed to be analogue to the reactant-product relation in chemistry, thus allowing for tools from evolutionary biology to be applied to chemistry and would deepen the connection between chemistry and biology.

Cross-lists for Fri, 23 Feb 18

[7]  arXiv:1802.08195 (cross-list from cs.LG) [pdf, other]
Title: Adversarial Examples that Fool both Human and Computer Vision
Subjects: Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.

[8]  arXiv:1802.08217 (cross-list from cs.NE) [pdf, ps, other]
Title: A new model for Cerebellar computation
Authors: Reza Moazzezi
Comments: 5 pages
Subjects: Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)

The standard state space model is widely believed to account for the cerebellar computation in motor adaptation tasks [1]. Here we show that several recent experiments [2-4] where the visual feedback is irrelevant to the motor response challenge the standard model. Furthermore, we propose a new model that accounts for the the results presented in [2-4]. According to this new model, learning and forgetting are coupled and are error size dependent. We also show that under reasonable assumptions, our proposed model is the only model that accounts for both the classical adaptation paradigm as well as the recent experiments [2-4].

Replacements for Fri, 23 Feb 18

[9]  arXiv:1701.07879 (replaced) [pdf]
Title: A Radically New Theory of how the Brain Represents and Computes with Probabilities
Authors: Gerard Rinkus
Comments: 33 pages, 10 figures - Sec. explaining single cell tuning fns as artifacts of embedding SDRs in superposition removed (for future paper) - Clarified that a given SDR code represents the whole likelihood distribution over stored hypotheses at a coarsely-ranked level of fidelity (Submitted for review)
Subjects: Neurons and Cognition (q-bio.NC); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
[10]  arXiv:1705.01188 (replaced) [pdf, other]
Title: Dynamics of Virus and Immune Response in Multi-Epitope Network
Comments: Revised preprint, to appear in Journal of Mathematical Biology
Subjects: Populations and Evolution (q-bio.PE); Dynamical Systems (math.DS)
[11]  arXiv:1710.11305 (replaced) [pdf, other]
Title: A probabilistic cellular automata model for the dynamics of a population driven by logistic growth and weak Allee effect
Comments: 19 pages, 6 figures, 55 references. Accepted for publication in J. Phys. A: Math. Theor. (IoP)
Subjects: Statistical Mechanics (cond-mat.stat-mech); Cellular Automata and Lattice Gases (nlin.CG); Populations and Evolution (q-bio.PE)
[12]  arXiv:1711.08198 (replaced) [pdf, other]
Title: A multiobjective deep learning approach for predictive classification in Neuroblastoma
Comments: NIPS ML4H workshop 2017 & MAQC 2018
Subjects: Quantitative Methods (q-bio.QM); Learning (cs.LG)
[13]  arXiv:1711.09588 (replaced) [pdf, other]
Title: A model of non-Gaussian diffusion in heterogeneous media
Subjects: Biological Physics (physics.bio-ph); Cell Behavior (q-bio.CB)
[14]  arXiv:1712.08369 (replaced) [html]
Title: Nobel Symposium 162 - Microfluidics
Comments: Nobel Symposium 162, Stockholm, Sweden
Subjects: Fluid Dynamics (physics.flu-dyn); Other Quantitative Biology (q-bio.OT)
[15]  arXiv:1801.01823 (replaced) [pdf, other]
Title: Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Subjects: Neurons and Cognition (q-bio.NC); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[16]  arXiv:1801.03268 (replaced) [pdf]
Title: Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
Comments: Although some prognostic indicators and models have been proposed for disorders of consciousness, each single method when used alone carries risks of false prediction. Song et al. report that a model combining resting state functional MRI with clinical characteristics provided accurate, robust, and interpretable prognostications. 52 pages, 1 table, 7 figures
Subjects: Neurons and Cognition (q-bio.NC)
[ total of 16 entries: 1-16 ]
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