Vasile V. Moca

Membrane Resonance Enables Stable and Robust Gamma Oscillations

Neuronal mechanisms underlying beta/gamma oscillations (20–80 Hz) are not completely understood. Here, we show that in vivo beta/gamma oscillations in the cat visual cortex sometimes exhibit remarkably stable frequency even when inputs fluctuate dramatically. Enhanced frequency stability is associated with stronger oscillations measured in individual units and larger power in the local field potential. Simulations of neuronal circuitry demonstrate that membrane properties of inhibitory interneurons strongly determine the characteristics of emergent oscillations. Exploration of networks containing either integrator or resonator inhibitory interneurons revealed that: (i) Resonance, as opposed to integration, promotes robust oscillations with large power and stable frequency via a mechanism called RING (Resonance INduced Gamma); resonance favors synchronization by reducing phase delays between interneurons and imposes bounds on oscillation cycle duration; (ii) Stability of frequency and robustness of the oscillation also depend on the relative timing of excitatory and inhibitory volleys within the oscillation cycle; (iii) RING can reproduce characteristics of both Pyramidal INterneuron Gamma (PING) and INterneuron Gamma (ING), transcending such classifications; (iv) In RING, robust gamma oscillations are promoted by slow but are impaired by fast inputs. Results suggest that interneuronal membrane resonance can be an important ingredient for generation of robust gamma oscillations having stable frequency.

Visual Exploration and Object Recognition by Lattice Deformation

Mechanisms of explicit object recognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called “Dots”), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating object recognition under a naturalistic setting – free visual exploration – enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for object recognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand.

EEG under anesthesia-Feature extraction with TESPAR

We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five DOA classes assessed by human experts whose judgements were based on EEG mid-latency auditory evoked potentials (MLAEPs) and clinical observations. We found that our system closely mimicked the behavior of the human expert, thus proving the utility of the method. Further analyses on the features extracted by our technique indicated that information related to DOA is mostly distributed across frequency bands and that the presence of high frequencies (> 80 Hz), which reflect mostly muscle activity, is beneficial for DOA detection.

Properties of multivariate data investigated by fractal dimensionality.

Elaborated data-mining techniques are widely available today. Nevertheless, many non-linear relations among variables remain undiscovered in multi-dimensional datasets. To address this issue we propose a method based on the concept of fractal dimension that explores the structure of multivariate data and apply the method to simulated data, as well as to local field potentials recorded from cat visual cortex. We find that with changes in the analysis scale, the dimensionality of the data often changes, indicating first that the data are not simple fractals with one unique dimension and second, that, at a certain scale, important changes in the geometric structure of the data may occur. The method can be used as a data-mining tool but also as a method for testing a models fit to the data. We achieve the latter by comparing the dimensionality of the original data to the dimensionality of the data reconstructed from a models description of the data (here using the general linear model). The method provides indispensable help in estimating the complexity of non-linear relationships within multivariate datasets.

Emergence of beta/gamma oscillations: ING, PING, and what about RING?

Background Oscillatory activity in high-beta and gamma bands (20-80Hz) is known to play an important role in cortical processing being linked to cognitive processes and behavior. Beta/gamma oscillations are thought to emerge in local cortical circuits via two mechanisms: the interaction between excitatory principal cells and inhibitory interneurons – the pyramidal-interneuron gamma (PING) [1], and in networks of coupled inhibitory interneurons under tonic excitation – the interneuronal gamma (ING) [2]. Experimental evidence underlines the important role of inhibitory interneurons and especially of the fast spiking (FS) interneurons [3,4]. We show in simulation that an important property of FS neurons, namely the membrane resonance (frequency preference), represents an additional mechanism – the resonance induced gamma (RING), i.e. modulation of oscillatory discharge by resonance. RING promotes frequency stability and enables oscillations in purely excitatory networks.

Probing the visual system with visual hypotheses

Introduction The ability of our visual system to categorize objects remains, to date, a challenging field of research. Most theories fall short at explaining how our visual system is able to find consistent visual solutions even under occluded conditions, to infer illusory shapes stemming from Gestalt rules, or to construct multiple interpretations in case of multi-stable perceptions. An alternative theory is that visual recognition is an inference process relying on exploration of multiple hypotheses, and may include highdimensional attractors as solutions to visual problems. Testing this theory requires the development of specialized stimulation techniques, allowing one to control the amount of information that is delivered to the visual system.

Discriminating legitimate oscillations from broadband transients

Neural oscillations are one of the most prominent characteristics of brain activity. Unfortunately, quantification of oscillations is not always straightforward. For spiking activity the pulsed nature of the binary signal makes spectral methods difficult to apply [1]. Counterintuitively, difficulties arise also for the case of continuous data (such as the local field potentials - LFPs, or the electroencephalogram - EEG) because their estimated spectra can be contaminated by broadband transient (BT) noise. Notably, muscle and ocular artifacts are known to produce BTs [2] that overlap with the gamma band (30-80 Hz), which is particularly relevant for information processing and seems to be correlated to conscious states. To address the issue of BTs in EEG, independent component analysis (ICA) has been employed for artifact treatment. However ICA is suitable only for multichannel recordings and may not be always satisfactory [3]. Eye-tracking data has been shown to help ICA cope better with eye artifacts [4]. Here we intend to explore an alternative approach to separating legitimate oscillations from BTs. This approach is based on previous work on oscillations in binary spiking data, where we have proposed a method relying on the spectrum of the autocorrelation function (ACF), namely the oscillation score (OS) [1]. OS takes into account particularities of ACF in order to isolate the truly periodic components which are then quantified by spectral analysis of the modified ACF. Most notably, the OS removes the central peak of the ACF, whose presence is not a characteristic of oscillatory activity. The central peak is very large, usually narrow and contaminates the ACF spectrum with broadband noise. After removing the central peak, the OS is capable to correctly quantify the strength of oscillations eliminating broadband contamination and overcoming difficulties associated with other methods [1]. For continuous signals like EEG or LFP, one should note that non-periodic spike-like transients (e.g., microsaccadic artifacts) should influence only the central peak of the ACF. Following the same idea as in OS, the influence of BTs could be mitigated by careful manipulation of the central peak. One legitimate question is whether the ACF central peak should be removed as in the case of OS, rescaled, or left intact. Here, we show that an extremely large central peak indicates BTs and that its width reflects the width of the transients. By considering the envelope of the ACF computed on the side-lobes, we show how to correctly handle the central peak (rescaling) to remove the influence of non-periodic transients with minimal distortion to the ACFs spectrum. According to the Wiener-Khinchin theorem, the Fourier transform of the ACF is the power spectrum of the signal. Therefore, we show that by applying an appropriate correction to the ACF central peak one is able to estimate the power spectrum of the signal while minimizing the influence of BTs. The method presented here and the OS demonstrate that mixed correlation-spectral approaches can offer a unified framework for robust quantification of oscillations in both continuous (LFP, EEG) and discrete spiking data.