Algorithms for identification and categorization

The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is essential for some of the processing of information in the brain.Comment: 6 pages, 5 figure

Stereo-EEG exploration in the insula/operculum in paediatric patients with refractory epilepsy

PURPOSE: Failure to recognise involvement of the insula / opercula (I/O) region is associated with poor outcome in epilepsy surgery. Recognition is challenging due to high connectivity with adjacent structures resulting in variable and misleading semiology, often subjective and therefore likely to be underreported by children. In this study we explored prevalence and characteristics of I/O involvement in paediatric patients undergoing sEEG exploration. METHOD: We retrospectively included all consecutive patients undergoing sEEG at our centre between 11/2014 and 01/2018 with at least three contacts within I/O and excluded those with undetermined seizure onset zone (SOZ) by sEEG. We divided patients into three groups: 1) SOZ in I/O, 2) spread to I/O and 3) no I/O involvement. We compared pre-invasive characteristics, sEEG results, surgery and outcome for each group. RESULTS: 29 of all 53 consecutive patients had an identified SOZ by sEEG and at least three contacts within the I/O and were included. 41% had I/O SOZ, 38% had I/O spread and 21% had no I/O involvement. Insula associated symptoms described in adult literature were not statistically different between the three groups. Complications due to sEEG were low (2 of 53 patients). Following I/O surgery, 63% were seizure free while an additional 26% of patients achieved seizure reduction. Postoperative deficits were seen in 75% of the patients but completely resolved in all but one patient. CONCLUSIONS: Our data suggest an important role of the I/O region with frequent onset or propagation to the I/O region (at least 64% of all 53 sEEG cases). Semiology appears less specific than in adults. Insula depth electrode insertion is safe with subsequent good surgical outcomes albeit common transient deficits

The feasibility and added value of mapping music during awake craniotomy:A systematic review

The value of mapping musical function during awake craniotomy is unclear. Hence, this systematic review was conducted to examine the feasibility and added value of music mapping in patients undergoing awake craniotomy. An extensive search, on 26 March 2021, in four electronic databases (Medline, Embase, Web of Science and Cochrane CENTRAL register of trials), using synonyms of the words “Awake Craniotomy” and “Music Performance,” was conducted. Patients performing music while undergoing awake craniotomy were independently included by two reviewers. This search resulted in 10 studies and 14 patients. Intra‐operative mapping of musical function was successful in 13 out of 14 patients. Isolated music disruption, defined as disruption during music tasks with intact language/speech and/or motor functions, was identified in two patients in the right superior temporal gyrus, one patient in the right and one patient in the left middle frontal gyrus and one patient in the left medial temporal gyrus. Pre‐operative functional MRI confirmed these localizations in three patients. Assessment of post‐operative musical function, only conducted in seven patients by means of standardized (57%) and non‐standardized (43%) tools, report no loss of musical function. With these results, we conclude that mapping music is feasible during awake craniotomy. Moreover, we identified certain brain regions relevant for music production and detected no decline during follow‐up, suggesting an added value of mapping musicality during awake craniotomy. A systematic approach to map musicality should be implemented, to improve current knowledge on the added value of mapping musicality during awake craniotomy

Replica symmetry breaking in the `small world' spin glass

We apply the cavity method to a spin glass model on a `small world' lattice, a random bond graph super-imposed upon a 1-dimensional ferromagnetic ring. We show the correspondence with a replicated transfer matrix approach, up to the level of one step replica symmetry breaking (1RSB). Using the scheme developed by M\'ezard & Parisi for the Bethe lattice, we evaluate observables for a model with fixed connectivity and $\pm J$ long range bonds. Our results agree with numerical simulations significantly better than the replica symmetric (RS) theory.Comment: 21 pages, 3 figure

Cycle-based Cluster Variational Method for Direct and Inverse Inference

We elaborate on the idea that loop corrections to belief propagation could be dealt with in a systematic way on pairwise Markov random fields, by using the elements of a cycle basis to define region in a generalized belief propagation setting. The region graph is specified in such a way as to avoid dual loops as much as possible, by discarding redundant Lagrange multipliers, in order to facilitate the convergence, while avoiding instabilities associated to minimal factor graph construction. We end up with a two-level algorithm, where a belief propagation algorithm is run alternatively at the level of each cycle and at the inter-region level. The inverse problem of finding the couplings of a Markov random field from empirical covariances can be addressed region wise. It turns out that this can be done efficiently in particular in the Ising context, where fixed point equations can be derived along with a one-parameter log likelihood function to minimize. Numerical experiments confirm the effectiveness of these considerations both for the direct and inverse MRF inference.Comment: 47 pages, 16 figure

The Supersymmetric Particle Spectrum

We examine the spectrum of supersymmetric particles predicted by grand unified theoretical (GUT) models where the electroweak symmetry breaking is accomplished radiatively. We evolve the soft supersymmetry breaking parameters according to the renormalization group equations (RGE). The minimization of the Higgs potential is conveniently described by means of tadpole diagrams. We present complete one-loop expressions for these minimization conditions, including contributions from the matter and the gauge sectors. We concentrate on the low $\tan \beta$ fixed point region (that provides a natural explanation of a large top quark mass) for which we find solutions to the RGE satisfying both experimental bounds and fine-tuning criteria. We also find that the constraint from the consideration of the lightest supersymmetric particle as the dark matter of the universe is accommodated in much of parameter space where the lightest neutralino is predominantly gaugino. The supersymmetric mass spectrum displays correlations that are model-independent over much of the GUT parameter space.Comment: 62 pages + 10 PS figures included (uuencoded), MAD/PH/80

The Variational Garrote

In this paper, we present a new variational method for sparse regression using $L_0$ regularization. The variational parameters appear in the approximate model in a way that is similar to Breiman's Garrote model. We refer to this method as the variational Garrote (VG). We show that the combination of the variational approximation and $L_0$ regularization has the effect of making the problem effectively of maximal rank even when the number of samples is small compared to the number of variables. The VG is compared numerically with the Lasso method, ridge regression and the recently introduced paired mean field method (PMF) (M. Titsias & M. L\'azaro-Gredilla., NIPS 2012). Numerical results show that the VG and PMF yield more accurate predictions and more accurately reconstruct the true model than the other methods. It is shown that the VG finds correct solutions when the Lasso solution is inconsistent due to large input correlations. Globally, VG is significantly faster than PMF and tends to perform better as the problems become denser and in problems with strongly correlated inputs. The naive implementation of the VG scales cubic with the number of features. By introducing Lagrange multipliers we obtain a dual formulation of the problem that scales cubic in the number of samples, but close to linear in the number of features.Comment: 26 pages, 11 figure

Optimal control as a graphical model inference problem

We reformulate a class of non-linear stochastic optimal control problems introduced by Todorov (2007) as a Kullback-Leibler (KL) minimization problem. As a result, the optimal control computation reduces to an inference computation and approximate inference methods can be applied to efficiently compute approximate optimal controls. We show how this KL control theory contains the path integral control method as a special case. We provide an example of a block stacking task and a multi-agent cooperative game where we demonstrate how approximate inference can be successfully applied to instances that are too complex for exact computation. We discuss the relation of the KL control approach to other inference approaches to control.Comment: 26 pages, 12 Figures; Machine Learning Journal (2012