Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity

Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of ESA-based RF-detection still hold true when using an iterative procedure for determining the optimal spike threshold for each MUA individually. Moreover, selectivity measures for ESA-based RFs were quite compatible with MUA-based RFs. Regarding RF size, ESA delivered larger RFs than thresholded MUA, but size difference was consistent over all SNR fractions. Regarding orientation selectivity, ESA delivered more sites with significant orientation-dependent responses but with somewhat lower orientation indexes than MUA. However, preferred orientations were similar for both signal types. The results suggest that ESA is a powerful signal for applications requiring automated, fast, and reliable response detection, as e.g., brain-computer interfaces and neuroprosthetics, due to its high sensitivity and its independence from user-dependent intervention. Because the full information of the spiking activity is preserved, ESA also constitutes a valuable alternative for offline analysis of data with limited SNR

Federated Electronic Practical Resources using PILAR as VISIR Integrated Tool

Practical training is a pillar in technical education. Traditionally, these benefits have been acquired through hands-on laboratory sessions. However, at present, the educational models trend to rely on distance education tools either totally (e-learning, m-learning, etc.) or partially (b-learning). To provide practical training in those educational scenarios is challenging. Remote laboratories --real laboratories, working on real systems and under real conditions, controlled remotely-- can play a fundamental role. Nevertheless, remote laboratories not only provide advantages, but disadvantages of both environments involved in the process: real laboratories and remote communications. Furthermore, remote laboratories add new limitations due to constructive constraints. VISIR (Virtual Instruments System In Reality) is a remote laboratory on top of the state of the art for wiring and measuring electrical and electronics circuits, but VISIR system has his own particular restrictions like any other remote lab. In this context, PILAR (Platform Integration of Laboratories based on the Architecture of visiR) Erasmus Plus project development aims for a federation of five of the existing VISIR nodes in Europe: Blekinge Institute of Technology (BTH), Spanish University for Distance Education (UNED), University of Deusto (UDEUSTO), Carinthia University of Applied Sciences (CUAS), School of Engineering of Polytechnic of Porto (ISEP). This paper describes the benefits that PILAR project will provide to the consortium, and how these physical constraints of the VISIR system can be compensated through the federation, after one year and a half of the project development and having the first draft of the federation and weebsite running.info:eu-repo/semantics/publishedVersio

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

2006 Special Issue How do we model attention-dependent signal routing?

One fundamental problem of neuronal information processing is posed by the enormous divergence and convergence of synaptic connections in and between many brain structures. This suggests that neurons receive not only signals which are relevant for their present computational task, but are often exposed to a large amount of unrelated, arbitrary signals which disturb ongoing information processing. This is particularly evident in vision when viewing natural, densely cluttered scenes. Neurons in higher visual areas with large receptive fields receive signals caused by a variety of different stimuli at the same time. Nevertheless they are capable of restricting their processing largely to the attended stimulus (Reynolds, Chelazzi, & Desimone, 1999). Thus, selective processing of the behaviorally relevant stimulus often occurs while large parts of a neuron’s synaptic input is expected to carry signals which reflect arbitrarily different stimuli. These unrelated signals can not be considered to behave like stationary and independent noise signals which might be discarded by simple subtraction. They are caused by real stimuli, and therefore they appear and disappear, increase and shrink, correlate and de-correlate in the same non-stationary manner as the signals for the relevant stimulus. Given the evidence for spatio-temporal structure in ongoing activity (Arieli, Sterkin, Grinvald, & Aertsen, 1996), even synaptic inputs carrying no stimulus information do not conform to typical assumptions of stationarity and independence. Since the relevant stimulus may cover only a small part of the classical receptive field, the number of irrelevant signals may easily outnumber the relevant signals by an order of magnitude and more. Therefore attention and other cognitive processes require a mechanism capable of reducing strongly and selectively the influence of large parts of the synaptic input to a neuron and to increase the effectiveness for a potentially small part carrying the computationally relevant signals. Modeling studies have suggested different solutions to control signal routing. Models based on gating neuron

Demonstration of Intracortical Chronic Recording and Acute Microstimulation Using Novel Floating Neural Probes

This paper presents long-term stable multichannel recording of neural activity using novel intracortical floating probes implanted chronically in rat cortex. The novel flexible probe design approach allows recording of action potentials for at least 38 days after implantation. Furthermore the capability of the PEDOT: PSS coated microelectrodes for electrical stimulation is characterized in vitro and in an acute in vivo experiment. The in vitro results show a charge injection capacity of 2 mC/cm2 and the in vivo results demonstrate reproducible response of the neural network to charge injection up to 1 mC/cm2. The optical inspection of the explanted neural probe reveals sufficient stability of the PEDOT: PSS microelectrode coating for the acute microstimulation experiment. These preliminary results indicate the capability for long-term stable microstimulation

Silicon-Based Microfabrication of Free-Floating Neural Probes and Insertion Tool for Chronic Applications

Bidirectional neural interfaces for multi-channel, high-density recording and electrical stimulation of neural activity in the central nervous system are fundamental tools for neuroscience and medical applications. Especially for clinical use, these electrical interfaces must be stable over several years, which is still a major challenge due to the foreign body response of neural tissue. A feasible solution to reduce this inflammatory response is to enable a free-floating implantation of high-density, silicon-based neural probes to avoid mechanical coupling between the skull and the cortex during brain micromotion. This paper presents our latest development of a reproducible microfabrication process, which allows a monolithic integration of a highly-flexible, polyimide-based cable with a silicon-stiffened neural probe at a high resolution of 1 µm. For a precise and complete insertion of the free-floating probes into the cortex, a new silicon-based, vacuum-actuated insertion tool is presented, which can be attached to commercially available electrode drives. To reduce the electrode impedance and enable safe and stable microstimulation an additional coating with the electrical conductive polymer PEDOT:PSS is used. The long-term stability of the presented free-floating neural probes is demonstrated in vitro and in vivo. The promising results suggest the feasibility of these neural probes for chronic applications

Signal transfer of visual stimuli to V4 occurs in gamma-rhythmic, pulsed information packages

Summary Selective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing by synchronization was proposed: neural populations sending attended signals align their gamma-rhythmic activities with receiving populations, such that spikes from the senders arrive at excitability peaks of the receivers, enhancing signal transfer. Conversely, the non-attended signals arrive unaligned to the receiver’s oscillation, reducing signal transfer. Therefore, visual signals should be transferred through periodically pulsed information packages, resulting in a modulation of the stimulus content within the receiver’s activity by its gamma phase and amplitude. To test this prediction, we quantified gamma phase-specific stimulus content within neural activity from area V4 of macaques performing a visual attention task. For the attended stimulus we find enhanced stimulus content reaching its maximum near excitability peaks, with effect magnitude increasing with oscillation amplitude, establishing a functional link between selective processing and gamma activity