Sunita Mandon

Switching Neuronal Inputs by Differential Modulations of Gamma-Band Phase-Coherence

Receptive fields (RFs) of cortical sensory neurons increase in size along consecutive processing stages. When multiple stimuli are present in a large visual RF, a neuron typically responds to an attended stimulus as if only that stimulus were present. However, the mechanism by which a neuron selectively responds to a subset of its inputs while discarding all others is unknown. Here, we show that neurons can switch between subsets of their afferent inputs by highly specific modulations of interareal gamma-band phase-coherence (PC). We measured local field potentials, single- and multi-unit activity in two male macaque monkeys (Macaca mulatta) performing an attention task. Two small stimuli were placed on a screen; the stimuli were driving separate local V1 populations, while both were driving the same local V4 population. In each trial, we cued one of the two stimuli to be attended. We found that gamma-band PC of the local V4 population with multiple subpopulations of its V1 input was differentially modulated. It was high with the input subpopulation representing the attended stimulus, while simultaneously it was very low between the same V4 population and the other input-providing subpopulation representing the irrelevant stimulus. These differential modulations, which depend on stimulus relevance, were also found in the locking of spikes from V4 neurons to the gamma-band oscillations of the V1 input subpopulations. This rapid, highly specific interareal locking provides neurons with a powerful dynamic routing mechanism to select and process only the currently relevant signals.

Rapid contour integration in macaque monkeys

Integration of oriented elements into a contour has been investigated extensively in human psychophysics whereas electrophysiological experiments exploring the neuronal mechanism of contour integration were most often done with macaque monkeys. To bridge the gap between human psychophysics and physiology we estimated spatial and temporal constraints of contour integration in two macaque monkeys. Our results show that contour integration in monkeys depends in a similar way on element distance and alignment between contour path and contour elements as in human subjects. The grouping process was surprisingly fast: In a backward masking experiment we show that a stimulus duration of 30-60 ms is sufficient to perceive a contour and to identify its shape.

Optimality of human contour integration.

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy.

A Multi-Channel, Flex-Rigid ECoG Microelectrode Array for Visual Cortical Interfacing

High-density electrocortical (ECoG) microelectrode arrays are promising signal-acquisition platforms for brain-computer interfaces envisioned, e.g., as high-performance communication solutions for paralyzed persons. We propose a multi-channel microelectrode array capable of recording ECoG field potentials with high spatial resolution. The proposed array is of a 150 mm2 total recording area; it has 124 circular electrodes (100, 300 and 500 μm in diameter) situated on the edges of concentric hexagons (min. 0.8 mm interdistance) and a skull-facing reference electrode (2.5 mm2 surface area). The array is processed as a free-standing device to enable monolithic integration of a rigid interposer, designed for soldering of fine-pitch SMD-connectors on a minimal assembly area. Electrochemical characterization revealed distinct impedance spectral bands for the 100, 300 and 500 μm-type electrodes, and for the arrays own reference. Epidural recordings from the primary visual cortex (V1) of an awake Rhesus macaque showed natural electrophysiological signals and clear responses to standard visual stimulation. The ECoG electrodes of larger surface area recorded signals with greater spectral power in the gamma band, while the skull-facing reference electrode provided higher average gamma power spectral density (γPSD) than the common average referencing technique.

Development of a fully implantable recording system for ECoG signals

This paper presents a fully implantable neural recording system for the simultaneous recording of 128 channels. The electrocorticography (ECoG) signals are sensed with 128 gold electrodes embedded in a 10 µm thick polyimide foil. The signals are picked up by eight amplifier array ICs and digitized with a resolution of 16 bit at 10 kHz. The digitized measurement data is processed in a reconfigurable digital ASIC, which is fabricated in a 0.35 µm CMOS technology and occupies an area of 2.8×2.8mm2. After data reduction, the measurement data is fed into a transceiver IC, which transmits the data with up to 495 kbit/s to a base station, using an RF loop antenna on a flexible PCB. The power consumption of 84mW is delivered via inductive coupling from the base station.

Toward High Performance, Weakly Invasive Brain Computer Interfaces Using Selective Visual Attention

Brain–computer interfaces have been proposed as a solution for paralyzed persons to communicate and interact with their environment. However, the neural signals used for controlling such prostheses are often noisy and unreliable, resulting in a low performance of real-world applications. Here we propose neural signatures of selective visual attention in epidural recordings as a fast, reliable, and high-performance control signal for brain prostheses. We recorded epidural field potentials with chronically implanted electrode arrays from two macaque monkeys engaged in a shape-tracking task. For single trials, we classified the direction of attention to one of two visual stimuli based on spectral amplitude, coherence, and phase difference in time windows fixed relative to stimulus onset. Classification performances reached up to 99.9%, and the information about attentional states could be transferred at rates exceeding 580 bits/min. Good classification can already be achieved in time windows as short as 200 ms. The classification performance changed dynamically over the trial and modulated with the tasks varying demands for attention. For all three signal features, the information about the direction of attention was contained in the γ-band. The most informative feature was spectral amplitude. Together, these findings establish a novel paradigm for constructing brain prostheses as, for example, virtual spelling boards, promising a major gain in performance and robustness for human brain–computer interfaces.

How ideal do macaque monkeys integrate contours

Abstract In this contribution, we present a joint study combining psychophysical investigations with macaque monkeys and probabilistic modelling of contour integration. We study the detection of S- and U-shaped contours of aligned Gabor patches among a set of unaligned distracters. This approach allows to compare quantitatively the performance of monkeys with the performance predicted by a probabilistic model. We find that the performance of the animals is very high, and we show that these experimental results can satisfactorially be explained by the structure of long-ranging horizontal interactions in V1.

Attention Configures Synchronization Within Local Neuronal Networks for Processing of the Behaviorally Relevant Stimulus

The need for fast and dynamic processing of relevant information imposes high demands onto the flexibility and efficiency of the nervous system. A good example for such flexibility is the attention-dependent selection of relevant sensory information. Studies investigating attentional modulations of neuronal responses to simultaneously arriving input showed that neurons respond, as if only the attended stimulus would be present within their receptive fields (RF). However, attention also improves neuronal representation and behavioral performance, when only one stimulus is present. Thus, attention serves for selecting relevant input and changes the neuronal processing of signals representing selected stimuli, ultimately leading to a more efficient behavioral performance. Here, we tested the hypothesis that attention configures the strength of functional coupling between a local neuronal networks neurons specifically for effective processing of signals representing attended stimuli. This coupling is measured as the strength of γ-synchronization between these neurons. The hypothesis predicts that the pattern of synchronization in local networks should depend on which stimulus is attended. Furthermore, we expect this pattern to be similar for the attended stimulus presented alone or together with irrelevant stimuli in the RF. To test these predictions, we recorded spiking-activity and local field potentials (LFP) with closely spaced electrodes in area V4 of monkeys performing a demanding attention task. Our results show that the γ-band phase coherence (γ-PhC) between spiking-activity and the LFP, as well as the spiking-activity of two groups of neurons, strongly depended on which of the two stimuli in the RF was attended. The γ-PhC was almost identical for the attended stimulus presented either alone or together with a distractor. The functional relevance of dynamic γ-band synchronization is further supported by the observation of strongly degraded γ-PhC before behavioral errors, while firing rates were barely affected. These qualitatively different results point toward a failure of attention-dependent top-down mechanisms to correctly synchronize the local neuronal network in V4, even though this network receives the correctly selected input. These findings support the idea of a flexible, demand-dependent dynamic configuration of local neuronal networks, for performing different functions, even on the same sensory input.

Can reduced contour detection performance in the periphery be explained by larger integration fields

Human contour integration performance decreases [1,2] with eccentricity, though less so for contours with good gestalt properties [3]. However, the cause is still not well understood. On the one hand, there is reduced visual acuity in the periphery due to cortical magnification. The same area of the visual field is mapped to a larger area of cortical surface close to the fovea than in the periphery. On the other hand, there is visual crowding. Elements that are clearly recognizable when shown in isolation are harder to recognize when surrounded by similar objects. The critical spacing for crowding has shown to be approximately half the eccentricity [4]. So crowding could be caused by larger integration fields in the periphery that span at least half the eccentricity.

Phase differences in local field potentials from macaque monkey area V4 predict attentional state in single trials with 99.6% accuracy

Coherent oscillations and synchronous activity are suggested to play an important role in selective processing and dynamic routing of information across the primary visual cortical areas. In this contribution we show that phase coherency between distant recording sites allows to distinguish almost perfectly between two attentional states in a behavioral task, thus giving strong quantitative support for a functional role of oscillatory neural dynamics.