Network Inference with Hidden Units

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

Mean Field Theory For Non-Equilibrium Network Reconstruction

There has been recent progress on the problem of inferring the structure of interactions in complex networks when they are in stationary states satisfying detailed balance, but little has been done for non-equilibrium systems. Here we introduce an approach to this problem, considering, as an example, the question of recovering the interactions in an asymmetrically-coupled, synchronously-updated Sherrington-Kirkpatrick model. We derive an exact iterative inversion algorithm and develop efficient approximations based on dynamical mean-field and Thouless-Anderson-Palmer equations that express the interactions in terms of equal-time and one time step-delayed correlation functions.Comment: new version, accepted in PRL. For the Supp. Mat. (ref. 11), please contact the author

Cumulants of Hawkes point processes

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.Comment: 11 pages, 4 figure

Ising Models for Inferring Network Structure From Spike Data

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.Comment: To appear in "Principles of Neural Coding", edited by Stefano Panzeri and Rodrigo Quian Quirog

“The Heighe Worthynesse of Love”: Visions of Perception, Convention, and Contradiction in Chaucer’s Troilus and Criseyde

This thesis examines three images associated with the manuscripts and early printed editions of Chaucer’s Troilus and Criseyde which I have dubbed “Prostrate Troilus,” “Pandarus as Messenger,” and “Criseyde in the Garden.” These images are artifacts of contemporary textual interpretation that “read” Chaucer’s text and the tale of Troilus. They each illustrate the way in which Troilus, Pandarus, and Criseyde “read” images, gestures, symbols, and speeches within the narrative, and they show how these characters are constrained and influenced by their individual primary modes of perception. Troilus reads but does not analyze. Pandarus actively reads his own meanings into messages. Criseyde’s reading is reflective. Ultimately, the different interpretive strategies that Chaucer explores in Troilus mirror those of Chaucer’s readers

Statistical physics of pairwise probability models

Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.Comment: 25 pages, 3 figure