Change search
Refine search result
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Battistin, C.
    et al.
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Niels Bohr Institute, Denmark .
    Tyrcha, J.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Centre for Neural Computation, Norway.
    Belief propagation and replicas for inference and learning in a kinetic Ising model with hidden spins2015In: Journal of Statistical Mechanics: Theory and Experiment, ISSN 1742-5468, E-ISSN 1742-5468, no 5, article id P05021Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Hertz, John A.
    et al.
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Sollich, Peter
    Path integral methods for the dynamics of stochastic and disordered systems2017In: JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, ISSN 1751-8113, Vol. 50, no 3, article id 033001Article, review/survey (Refereed)
    Abstract [en]

    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.

  • 3.
    Hertz, John
    et al.
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Tyrcha, J.
    Ising models for inferring network structure from spike data2013In: Principles of Neural Coding, CRC Press , 2013, p. 527-546Chapter in book (Other academic)
    Abstract [en]

    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. 

  • 4.
    John, Hertz
    et al.
    KTH.
    Roudi, Yasser
    Thorning, Andreas
    Tyrcha, Joanna
    Aurell, Erik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Zeng, Hong-Li
    Inferring network connectivity using kinetic Ising models2010Conference paper (Refereed)
  • 5.
    Jovanovic, Stojan
    et al.
    KTH, School of Computer Science and Communication (CSC).
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Rotter, Stefan
    Cumulants of Hawkes point processes2015In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, no 4, article id 042802Article in journal (Refereed)
    Abstract [en]

    We derive explicit, closed-form expressions for the cumulant densities of a multivariate, self-exciting Hawkes point process, generalizing a result of Hawkes in his earlier work on the covariance density and Bartlett spectrum of such processes. To do this, we represent the Hawkes process in terms of a Poisson cluster process and show how the cumulant density formulas can be derived by enumerating all possible "family trees," representing complex interactions between point events. We also consider the problem of computing the integrated cumulants, characterizing the average measure of correlated activity between events of different types, and derive the relevant equations.

  • 6.
    Roudi, Yasser
    et al.
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Dunn, B.
    Hertz, John A.
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Multi-neuronal activity and functional connectivity in cell assemblies2015In: Current Opinion in Neurobiology, ISSN 0959-4388, E-ISSN 1873-6882, Vol. 32, p. 38-44Article in journal (Refereed)
    Abstract [en]

    Our ability to collect large amounts of data from many cells has been paralleled by the development of powerful statistical models for extracting information from this data. Here we discuss how the activity of cell assemblies can be analyzed using these models, focusing on the generalized linear models and the maximum entropy models and describing a number of recent studies that employ these tools for analyzing multi-neuronal activity. We show results from simulations comparing inferred functional connectivity, pairwise correlations and the real synaptic connections in simulated networks demonstrating the power of statistical models in inferring functional connectivity. Further development of network reconstruction techniques based on statistical models should lead to more powerful methods of understanding functional anatomy of cell assemblies.

  • 7. Tyrcha, Joanna
    et al.
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Network Inference With Hidden Units2014In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 11, no 1, p. 149-165Article in journal (Refereed)
    Abstract [en]

    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.

  • 8. Tyrcha, Joanna
    et al.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Marsili, Matteo
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    The effect of nonstationarity on models inferred from neural data2013In: Journal of Statistical Mechanics: Theory and Experiment, ISSN 1742-5468, E-ISSN 1742-5468, p. P03005-Article in journal (Refereed)
    Abstract [en]

    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.

  • 9. Zeng, Hong Li
    et al.
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    L-1 regularization for reconstruction of a non-equilibrium Ising model2014In: Physica Scripta, ISSN 0031-8949, E-ISSN 1402-4896, Vol. 89, no 10, p. 105002-Article in journal (Refereed)
    Abstract [en]

    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.

  • 10. Zeng, Hong-Li
    et al.
    Alava, Mikko
    Aurell, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hertz, John
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Roudi, Yasser
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates2013In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 110, no 21, p. 210601-Article in journal (Refereed)
    Abstract [en]

    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.

1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf