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| 2. |
- 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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| 3. |
- 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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| 5. |
- Stemann Larsen, Pernille, et al.
(författare)
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Pregnancy and Birth Cohort Resources in Europe: a Large Opportunity for Aetiological Child Health Research
- 2013
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Ingår i: Paediatric and Perinatal Epidemiology. - : Wiley-Blackwell. - 0269-5022 .- 1365-3016. ; 27:4, s. 393-414
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Forskningsöversikt (refereegranskat)abstract
- Background During the past 25 years, many pregnancy and birth cohorts have been established. Each cohort provides unique opportunities for examining associations of early-life exposures with child development and health. However, to fully exploit the large amount of available resources and to facilitate cross-cohort collaboration, it is necessary to have accessible information on each cohort and its individual characteristics. The aim of this work was to provide an overview of European pregnancy and birth cohorts registered in a freely accessible database located at http://www.birthcohorts.net. Methods European pregnancy and birth cohorts initiated in 1980 or later with at least 300 mother-child pairs enrolled during pregnancy or at birth, and with postnatal data, were eligible for inclusion. Eligible cohorts were invited to provide information on the data and biological samples collected, as well as the timing of data collection. Results In total, 70 cohorts were identified. Of these, 56 fulfilled the inclusion criteria encompassing a total of more than 500000 live-born European children. The cohorts represented 19 countries with the majority of cohorts located in Northern and Western Europe. Some cohorts were general with multiple aims, whilst others focused on specific health or exposure-related research questions. Conclusion This work demonstrates a great potential for cross-cohort collaboration addressing important aspects of child health. The web site, http://www.birthcohorts.net, proved to be a useful tool for accessing information on European pregnancy and birth cohorts and their characteristics.
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| 6. |
- Tyrcha, Joanna, et al.
(författare)
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NETWORK INFERENCE WITH HIDDEN UNITS
- 2014
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Ingår i: Mathematical Biosciences and Engineering. - : American Institute of Mathematical Sciences (AIMS). - 1547-1063 .- 1551-0018. ; 11:1, s. 149-165
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Tidskriftsartikel (refereegranskat)abstract
- We derive learning rules for finding the connections between units in stochastic dynamical networks from the recorded history of a visible subset of the units. We consider two models. In both of them, the visible units are binary and stochastic. In one model the hidden units are continuous-valued, with sigmoidal activation functions, and in the other they are binary and stochastic like the visible ones. We derive exact learning rules for both cases. For the stochastic case, performing the exact calculation requires, in general, repeated summations over an number of configurations that grows exponentially with the size of the system and the data length, which is not feasible for large systems. We derive a mean field theory, based on a factorized ansatz for the distribution of hidden-unit states, which offers an attractive alternative for large systems. We present the results of some numerical calculations that illustrate key features of the two models and, for the stochastic case, the exact and approximate calculations.
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| 7. |
- Tyrcha, Joanna, et al.
(författare)
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The effect of nonstationarity on models inferred from neural data
- 2013
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Ingår i: Journal of Statistical Mechanics. - 1742-5468.
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Tidskriftsartikel (refereegranskat)abstract
- Neurons subject to a common nonstationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interaction between neurons. Here we show that these two situations can be distinguished with machine learning techniques, provided that the data are rich enough. In order to do this, we study the problem of inferring a kinetic Ising model, stationary or nonstationary, from the available data. We apply the inference procedure to two data sets: one from salamander retinal ganglion cells and the other from a realistic computational cortical network model. We show that many aspects of the concerted activity of the salamander retinal neurons can be traced simply to the external input. A model of non-interacting neurons subject to a nonstationary external field outperforms a model with stationary input with couplings between neurons, even accounting for the differences in the number of model parameters. When couplings are added to the nonstationary model, for the retinal data, little is gained: the inferred couplings are generally not significant. Likewise, the distribution of the sizes of sets of neurons that spike simultaneously and the frequency of spike patterns as a function of their rank (Zipf plots) are well explained by an independent-neuron model with time-dependent external input, and adding connections to such a model does not offer significant improvement. For the cortical model data, robust couplings, well correlated with the real connections, can be inferred using the nonstationary model. Adding connections to this model slightly improves the agreement with the data for the probability of synchronous spikes but hardly affects the Zipf plot.
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| 8. |
- Zeng, Hong Li, et al.
(författare)
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L-1 regularization for reconstruction of a non-equilibrium Ising model
- 2014
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Ingår i: Physica Scripta. - : IOP Publishing. - 0031-8949 .- 1402-4896. ; 89:10, s. 105002-
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Tidskriftsartikel (refereegranskat)abstract
- The couplings in a sparse asymmetric, asynchronous Ising network are reconstructed using an exact learning algorithm. L-1 regularization is used to remove the spurious weak connections that would otherwise be found by simply maximizing the log likelihood of a finite data set. In order to see how L-1 regularization works in detail, we perform the calculation in several ways including (1) by iterative minimization of a cost function equal to minus the log likelihood of the data plus an L-1 penalty term, and (2) an approximate scheme based on a quadratic expansion of the cost function around its minimum. In these schemes, we track how connections are pruned as the strength of the L-1 penalty is increased from zero to large values. The performance of the methods for various coupling strengths is quantified using receiver operating characteristic curves, showing that increasing the coupling strength improves reconstruction quality.
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| 9. |
- Zeng, Hong-Li, et al.
(författare)
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Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates
- 2013
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Ingår i: Physical Review Letters. - 0031-9007 .- 1079-7114. ; 110:21, s. 210601-
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Tidskriftsartikel (refereegranskat)abstract
- We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases: one in which we know both the spin history and the update times and one in which we know only the spin history. For the first case, we show that one can average over all possible choices of update times to obtain a learning rule that depends only on spin correlations and can also be derived from the equations of motion for the correlations. For the second case, the same rule can be derived within a further decoupling approximation. We study all methods numerically for fully asymmetric Sherrington-Kirkpatrick models, varying the data length, system size, temperature, and external field. Good convergence is observed in accordance with the theoretical expectations.
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