Measuring information-transfer delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics

Role of the medial part of the intraparietal sulcus in implementing movement direction

The contribution of the posterior parietal cortex (PPC) to visually guided movements has been originally inferred from observations made in patients suffering from optic ataxia. Subsequent electrophysiological studies in monkeys and functional imaging data in humans have corroborated the key role played by the PPC in sensorimotor transformations underlying goal-directed movements, although the exact contribution of this structure remains debated. Here, we used transcranial magnetic stimulation (TMS) to interfere transiently with the function of the left or right medial part of the intraparietal sulcus (mIPS) in healthy volunteers performing visually guided movements with the right hand. We found that a "virtual lesion" of either mIPS increased the scattering in initial movement direction (DIR), leading to longer trajectory and prolonged movement time, but only when TMS was delivered 100-160 ms before movement onset and for movements directed toward contralateral targets. Control experiments showed that deficits in DIR consequent to mIPS virtual lesions resulted from an inappropriate implementation of the motor command underlying the forthcoming movement and not from an inaccurate computation of the target localization. The present study indicates that mIPS plays a causal role in implementing specifically the direction vector of visually guided movements toward objects situated in the contralateral hemifield

Phase shifts of synchronized oscillators and the systolic/diastolic blood pressure relation

We study the phase-synchronization properties of systolic and diastolic arterial pressure in healthy subjects. We find that delays in the oscillatory components of the time series depend on the frequency bands that are considered, in particular we find a change of sign in the phase shift going from the Very Low Frequency band to the High Frequency band. This behavior should reflect a collective behavior of a system of nonlinear interacting elementary oscillators. We prove that some models describing such systems, e.g. the Winfree and the Kuramoto models offer a clue to this phenomenon. For these theoretical models there is a linear relationship between phase shifts and the difference of natural frequencies of oscillators and a change of sign in the phase shift naturally emerges.Comment: 8 figures, 9 page

Auditory Spectrum-Based Pitched Instrument Onset Detection

In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitch-based features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%