| 1. |
- Pearce, Neil E, et al.
(författare)
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IARC Monographs : 40 Years of Evaluating Carcinogenic Hazards to Humans
- 2015
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Ingår i: Journal of Environmental Health Perspectives. - : Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 123:6, s. 507-514
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Recently the International Agency for Research on Cancer (IARC) Programme for the Evaluation of Carcinogenic Risks to Humans has been criticized for several of its evaluations, and also the approach used to perform these evaluations. Some critics have claimed that IARC Working Groups' failures to recognize study weaknesses and biases of Working Group members have led to inappropriate classification of a number of agents as carcinogenic to humans.OBJECTIVES: The authors of this paper are scientists from various disciplines relevant to the identification and hazard evaluation of human carcinogens. We have examined here criticisms of the IARC classification process to determine the validity of these concerns. We review the history of IARC evaluations and describe how the IARC evaluations are performed.DISCUSSION: We conclude that these recent criticisms are unconvincing. The procedures employed by IARC to assemble Working Groups of scientists from the various discipline and the techniques followed to review the literature and perform hazard assessment of various agents provide a balanced evaluation and an appropriate indication of the weight of the evidence. Some disagreement by individual scientists to some evaluations is not evidence of process failure. The review process has been modified over time and will undoubtedly be altered in the future to improve the process. Any process can in theory be improved, and we would support continued review and improvement of the IARC processes. This does not mean, however, that the current procedures are flawed.CONCLUSIONS: The IARC Monographs have made, and continue to make, major contributions to the scientific underpinning for societal actions to improve the public's health.
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| 2. |
- Battistin, Claudia, et al.
(författare)
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Belief propagation and replicas for inference and learning in a kinetic Ising model with hidden spins
- 2015
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Ingår i: Journal of Statistical Mechanics. - 1742-5468 .- 1742-5468.
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Tidskriftsartikel (refereegranskat)abstract
- We propose a new algorithm for inferring the state of hidden spins and reconstructing the connections in a synchronous kinetic Ising model, given the observed history. Focusing on the case in which the hidden spins are conditionally independent of each other given the state of observable spins, we show that calculating the likelihood of the data can be simplified by introducing a set of replicated auxiliary spins. Belief propagation (BP) and susceptibility propagation (SusP) can then be used to infer the states of hidden variables and to learn the couplings. We study the convergence and performance of this algorithm for networks with both Gaussian-distributed and binary bonds. We also study how the algorithm behaves as the fraction of hidden nodes and the amount of data are changed, showing that it outperforms the Thouless-Anderson-Palmer (TAP) equations for reconstructing the connections.
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| 3. |
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| 4. |
- Hertz, John A., et al.
(författare)
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Ising model for inferring network structure from spike data
- 2013
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Ingår i: Principle of Neural Coding. - Boca/Raton : CRC Press. - 9781439853306 - 9781439853313 ; , s. 527-546
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Bokkapitel (refereegranskat)abstract
- Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.
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| 5. |
- Hertz, John A., et al.
(författare)
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Path integral methods for the dynamics of stochastic and disordered systems
- 2017
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Ingår i: Journal of Physics A. - : IOP Publishing. - 1751-8113 .- 1751-8121. ; 50:3
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Forskningsöversikt (refereegranskat)abstract
- We review some of the techniques used to study the dynamics of disordered systems subject to both quenched and fast (thermal) noise. Starting from the Martin-Siggia-Rose/Janssen-De Dominicis-Peliti path integral formalism for a single variable stochastic dynamics, we provide a pedagogical survey of the perturbative, i.e. diagrammatic, approach to dynamics and how this formalism can be used for studying soft spin models. We review the supersymmetric formulation of the Langevin dynamics of these models and discuss the physical implications of the supersymmetry. We also describe the key steps involved in studying the disorder-averaged dynamics. Finally, we discuss the path integral approach for the case of hard Ising spins and review some recent developments in the dynamics of such kinetic Ising models.
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| 6. |
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| 7. |
- Hertz, John, et al.
(författare)
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Ising models for inferring network structure from spike data
- 2013
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Ingår i: Principles of Neural Coding. - : CRC Press. ; , s. 527-546
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Now that we can record the spike trains of large numbers of neurons simultaneously, we have a chance, for the first time in the history of neuroscience, to start to understand how networks of neurons work. But how are we to proceed, once we have such data? In this chapter, we will review some ideas we have been developing. The reader will recognize that we are only describing the very first steps in a long journey. But we hope that they will help point the way toward real progress some time in the not-too-distant future.
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| 8. |
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| 9. |
- Hertz, John, et al.
(författare)
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Stochastic activation in a genetic switch model
- 2018
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Ingår i: Physical review. E. - 2470-0045 .- 2470-0053. ; 98:5
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Tidskriftsartikel (refereegranskat)abstract
- We study a biological autoregulation process, involving a protein that enhances its own transcription, in a parameter region where bistability would be present in the absence of fluctuations. We calculate the rate of fluctuation-induced rare transitions between locally stable states using a path integral formulation and Master and Chapman-Kolmogorov equations. As in simpler models for rare transitions, the rate has the form of the exponential of a quantity S-0 (a barrier) multiplied by a prefactor eta. We calculate S-0 and eta first in the bursting limit (where the ratio gamma of the protein and mRNA lifetimes is very large). In this limit, the calculation can be done almost entirely analytically, and the results are in good agreement with simulations. For finite gamma numerical calculations are generally required. However, S-0 can be calculated analytically to first order in 1/gamma, and the result agrees well with the full numerical calculation for all gamma > 1. Employing a method used previously on other problems, we find we can account qualitatively for the way the prefactor eta varies with gamma, but its value is 15-20% higher than that inferred from simulations.
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| 10. |
- Jovanovic, Stojan
(författare)
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Correlations of Higher Order in Networks of Spiking Neurons
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The topic of this dissertation is the study of the emergence of higher-order correlations in recurrentlyconnected populations of brain cells.Neurons have been experimentally shown to form vast networks in the brain. In these networks, eachbrain cell communicates with tens of thousands of its neighbors by sending out and receiving electricalsignals, known as action potentials or spikes. The effect of a single action potential can propagate throughthe network and cause additional spikes to be generated. Thus, the connectivity of the neuronal networkgreatly influences the network's spiking dynamics. However, while the methods of action potentialgeneration are very well studied, many dynamical features of neuronal networks are still only vaguelyunderstood.The reasons for this mostly have to do with the difficulties of keeping track of the collective, non-linearbehavior of hundreds of millions of brain cells. Even when one focuses on small groups of neurons, all butthe most trivial questions about coordinated activity remain unanswered, due to the combinatorialexplosion that arises in all questions of this sort. In theoretical neuroscience one often needs to resort tomathematical models that try to explain the most important dynamical phenomena while abstractingaway many of the morphological features of real neurons.On the other hand, advances in experimental methods are making simultaneous recording of largeneuronal populations possible. Datasets consisting of collective spike trains of thousands of neurons arebecoming available. With these new developments comes the possibility of finally understanding the wayin which connectivity gives rise to the many interesting dynamical aspects of spiking networks.The main research question, addressed in this thesis, is how connectivity between neurons influences thedegree of synchrony between their respective spike trains. Using a linear model of spiking neurondynamics, we show that there is a mathematical relationship between the network's connectivity and theso-called higher-order cumulants, which quantify beyond-chance-level coordinated activity of groups ofneurons. Our equations describe the specific connectivity patterns that give rise to higher-ordercorrelations. In addition, we explore the special case of correlations of third-order and find that, in large,regular networks, it is the presence of a single subtree that is responsible for third-order synchrony.In summary, the results presented in this dissertation advance our understanding of how higher-ordercorrelations between spike trains of neurons are affected by certain patterns in synaptic connectivity.Our hope is that a better understanding of such complicated neuronal dynamics can lead to a consistenttheory of the network's functional properties.
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