Single-electrode Recordings Research Articles

Linear and Nonlinear Behaviors of the Photoreceptor Coupled Network.

Photoreceptors are electrically coupled to one another, and the spatiotemporal properties of electrical synapses in a two-dimensional retinal network are still not well studied, because of the limitation of the single electrode or pair recording techniques which do not allow simultaneously measuring responses of multiple photoreceptors at various locations in the retina. A multiple electrode recording system is needed. In this study, we investigate the network properties of the two-dimensional rod coupled array of the salamander retina (both sexes were used) by using the newly available multiple patch electrode system that allows simultaneous recordings from up to eight cells and to determine the electrical connectivity among multiple rods. We found direct evidence that voltage signal spread in the rod-rod coupling network in the absence of I h (mediated by HCN channels) is passive and follows the linear cable equation. Under physiological conditions, I h shapes the network signal by progressively shortening the response time-to-peak of distant rods, compensating the time loss of signal traveling from distant rods to bipolar cell somas and facilitating synchronization of rod output signals. Under voltage-clamp conditions, current flow within the coupled rods follows Ohm's law, supporting the idea that nonlinear behaviors of the rod network are dependent on membrane voltage. Rod-rod coupling is largely symmetrical in the 2D array, and voltage-clamp blocking the next neighboring rod largely suppresses rod signal spread into the second neighboring rod, suggesting that indirect coupling pathways play a minor role in rod-rod coupling.

Measuring spatial visual loss in rats by retinotopic mapping of the superior colliculus using a novel multi-electrode array technique

BackgroundThe retinotopic map property of the superior colliculus (SC) is a reliable indicator of visual functional changes in rodents. Electrophysiological mapping of the SC using a single electrode has been employed for measuring visual function in rat and mouse disease models. Single electrode mapping is highly laborious requiring long-term exposure to the SC surface and prolonged anesthetic conditions that can adversely affect the mapping data. New methodTo avoid the above-mentioned issues, we fabricated a fifty-six (56) electrode multi-electrode array (MEA) for rapid and reliable visual functional mapping of the SC. Since SC is a dome-shaped structure, the array was made of electrodes with dissimilar tip lengths to enable simultaneous and uniform penetration of the SC. ResultsSC mapping using the new MEA was conducted in retinal degenerate (RD) Royal College of Surgeons (RCS) rats and rats with focal retinal damage induced by green diode laser. For SC mapping, the MEA was advanced into the SC surface and the visual activities were recorded during full-filed light stimulation of the eye. Based on the morphological examination, the MEA electrodes covered most of the exposed SC area and penetrated the SC surface at a relatively uniform depth. MEA mapping in RCS rats (n=9) demonstrated progressive development of a scotoma in the SC that corresponded to the degree of photoreceptor loss. MEA mapping in the laser damaged rats demonstrated the presence of a scotoma in the SC area that corresponded to the location of retinal laser injury. Comparison with existing methods and conclusionsThe use of MEA for SC mapping is advantageous over single electrode recording by enabling faster recordings and reducing anesthesia time. This study establishes the feasibility of the MEA technique for rapid and efficient SC mapping, particularly advantageous for evaluating therapeutic effects in retinal degenerate rat disease models.

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort’s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

Sleep Power Topography in Children with Attention Deficit Hyperactivity Disorder (ADHD).

Objective: Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG). Method: Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time. Results: Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time. Conclusions: These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.

Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network

In this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) architecture, which has emerged as a general and effective model for capturing long-term temporal dependencies with good generalization performance. In this way, training the network with the data recorded from one rat could lead to estimating the bladder status of different rats. We combined modeling of spiking and local field potential (LFP) activity into a unified framework to estimate the pressure and volume of the bladder. Moreover, we investigated the effect of two-electrode recording on decoding performance. The results show that the two-electrode recordings significantly improve the decoding performance compared to single-electrode recordings. The proposed framework could estimate bladder pressure and volume with an average normalized root-mean-squared (NRMS) error of 14.9 ± 4.8% and 19.7 ± 4.7% and a correlation coefficient (CC) of 83.2 ± 3.2% and 74.2 ± 6.2%, respectively. This work represents a promising approach to the real-time estimation of bladder pressure/volume in the closed-loop control of bladder function using functional electrical stimulation.

Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates.

For years, neurophysiological studies of the cerebral cortical mechanisms of voluntary motor control were limited to single-electrode recordings of the activity of one or a few neurons at a time. This approach was supported by the widely accepted belief that single neurons were the fundamental computational units of the brain (the “neuron doctrine”). Experiments were guided by motor-control models that proposed that the motor system attempted to plan and control specific parameters of a desired action, such as the direction, speed or causal forces of a reaching movement in specific coordinate frameworks, and that assumed that the controlled parameters would be expressed in the task-related activity of single neurons. The advent of chronically implanted multi-electrode arrays about 20 years ago permitted the simultaneous recording of the activity of many neurons. This greatly enhanced the ability to study neural control mechanisms at the population level. It has also shifted the focus of the analysis of neural activity from quantifying single-neuron correlates with different movement parameters to probing the structure of multi-neuron activity patterns to identify the emergent computational properties of cortical neural circuits. In particular, recent advances in “dimension reduction” algorithms have attempted to identify specific covariance patterns in multi-neuron activity which are presumed to reflect the underlying computational processes by which neural circuits convert the intention to perform a particular movement into the required causal descending motor commands. These analyses have led to many new perspectives and insights on how cortical motor circuits covertly plan and prepare to initiate a movement without causing muscle contractions, transition from preparation to overt execution of the desired movement, generate muscle-centered motor output commands, and learn new motor skills. Progress is also being made to import optical-imaging and optogenetic toolboxes from rodents to non-human primates to overcome some technical limitations of multi-electrode recording technology.

Three dimensional microenvironments on multi-electrode arrays produce neuronal networks that function like the brain

Event Abstract Back to Event Three dimensional microenvironments on multi-electrode arrays produce neuronal networks that function like the brain Justin L. Bourke1, 2, 3*, Anita Quigley1, 2, 3, 4, Cathal O'Connell2, 3, Jeremy Crook2, 4, Gordon Wallace2, 4, Mark Cook1, 2, 3 and Robert Kapsa1, 2, 3, 4 1 University of Melbourne, Department of Clinical Neurosciences, St Vincent's Hospital, Australia 2 Australian Research Council Centre of Excellence for Electromaterials Science, Australia 3 St Vincent's Hospital (Melbourne), Department of Medicine, Australia 4 University of Wollongong, Intelligent Polymer Research Institute, Australia Introduction: Engineered 3D constructs for cell culture mimic in vivo conditions better than traditional 2D substrates, resulting in controlled cellular growth patterns[1] and improved cell function[2-5]. More critically from a tissue engineering perspective, such 3D substrates may improve intercellular interactions and thus whole-tissue and organoid functionality. Previous studies have generated pseudo-3D neuronal networks using beads to separate neuronal positioning[6], with MEA recordings providing insight into changes in function from such substrate designs[7-8]. Functional network differences exist between these bead-based substrate networks and traditional 2D neuronal networking, although the authors point out the limited inter-layer connectivity, with 7 times more neuronal interconnectivity within layers than between due to neurites extending only on bead surfaces. We have developed a true 3D networking model based on collagen where neurite positioning and connectivity is not limited by the substrate. We used microelectrode arrays to investigate functional differences between neuronal networks formed in 2D and 3D, and determined that 3D cultures produce neuronal network function more akin to networks of the intact brain. Methods: Embryonic rat hippocampal neurons were cultured in 2D and in 3D collagen gels upon microelectrode arrays (Multichannel Systems). Cultures were maintained for 56 days, with electrophysiological recordings taken every 7 days. Neuronal cell function at each electrode and network activity between electrodes were compared between 2D and 3D cultures. Results and Discussion: Neural activation originated from spontaneously active pace-making regions within both 2D and 3D cultures. In 2D, activity flowed directly across the planar cultures resulting in a short repetitive and predictable spatial and temporal bursting pattern, with one burst at each electrode per activation of the pace-making region (Fig 1a). In contrast, the 3D constructs produced regular oscillatory activity throughout the higher order neural networks, resulting in spiking patterns of increased burst duration (Fig 1b), a pattern that more closely mimics network activity within the intact brain. Synchronous activation was observed throughout both 2D and 3D networks, indicating network connectivity throughout the cultures. Previous bead-based materials produce network functionality with both short interval regular spiking and longer duration bursts, a mix of network function observed in 2D and true 3D collagen networks observed within this study. The results observed illustrate the importance of a true 3D neuronal network microenvironment to producing network function akin to the in vivo state. Conclusions: Mimicking the natural microenvironment in cell culture applications is critical to achieving more natural single-cell function. In addition, characterisation of intercellular interactions within such tissue engineered constructs is critical to understanding whole-tissue and organoid level functionality. From a neural construct perspective, we have shown that neural network formation and function in three-dimensions mimics network function of the intact brain more accurately than conventional two-dimensional cultures. Measurement of neural network function at the whole-tissue level in three-dimensional neural cultures will optimise outcomes for network studies and allow for investigation of mechanisms behind neural network formation and neural networking disorders such as epilepsy and schizophrenia. Paired with neurons derived from induced pluripotent stem cells from patients, the findings here will facilitate a personalised medicine approach to drug efficacy studies for individuals suffering epilepsy and schizophrenia. This presentation: This presentation will concentrate on the yet to be published methods to generate such 3D networks, analysis of the networking activity, and work currently underway in applying such networks to a clinical model for drug selection for patients suffering from epilepsy. Figure 1: Typical single electrode recordings in (Ai) 2D and (bi) 3D cultures (adapted from Bourke et al [5]). Synchronicity within raster plots of spike times across multiple electrode in (Aii) 2D and (Bii) 3D cultures indicate fully connected neural networks in both culture conditions. Note the fast spiking patterns in 2D cultures and slower, highly consistent bursting patterns in 3D networks. Figure 1 Acknowledgements The authors wish to acknowledge financial support from the Australian Research Council (ARC) Project CE140100012, the ARC Centre of Excellence for Electromaterials Science, and National Health and Medical Research Council project grant 1062569.

Intraoperative definition of bottom-of-sulcus dysplasia using intraoperative ultrasound and single depth electrode recording – A technical note

Bottom of sulcus dysplasias (BOSDs) are localized focal cortical dysplasias (FCDs) centred on the bottom of a sulcus that can be highly epileptogenic, but difficult to delineate intraoperatively. We report on a patient with refractory epilepsy due to a BOSD, successfully resected with the aid of a multimodal surgical approach using neuronavigation based on MRI and PET, intraoperative ultrasound (iUS) and electrocorticography (ECoG) using depth electrodes. The lesion could be visualized on iUS showing an increase in echogenicity at the grey-white matter junction. IUS demonstrated the position of the depth electrode in relation to the lesion. Depth electrode recording showed almost continuous spiking. Thus, intraoperative imaging and electrophysiology helped confirm the exact location of the lesion. Post-resection ultrasound demonstrated the extent of the resection and depth electrode recording did not show any epileptiform activity. Thus, both techniques helped assess completeness of resection. The patient has been seizure free since surgery. Using a multimodal approach including iUS and ECoG is a helpful adjunct in surgery for BOSD and may improve seizure outcome.

Mixed Neural Network Approach for Temporal Sleep Stage Classification.

This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electro-oculogram. Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Dynamic population codes of multiplexed stimulus features in primate area MT.

The middle-temporal area (MT) of primate visual cortex is critical in the analysis of visual motion. Single-unit studies suggest that the response dynamics of neurons within area MT depend on stimulus features, but how these dynamics emerge at the population level, and how feature representations interact, is not clear. Here, we used multivariate classification analysis to study how stimulus features are represented in the spiking activity of populations of neurons in area MT of marmoset monkey. Using representational similarity analysis we distinguished the emerging representations of moving grating and dot field stimuli. We show that representations of stimulus orientation, spatial frequency, and speed are evident near the onset of the population response, while the representation of stimulus direction is slower to emerge and sustained throughout the stimulus-evoked response. We further found a spatiotemporal asymmetry in the emergence of direction representations. Representations for high spatial frequencies and low temporal frequencies are initially orientation dependent, while those for high temporal frequencies and low spatial frequencies are more sensitive to motion direction. Our analyses reveal a complex interplay of feature representations in area MT population response that may explain the stimulus-dependent dynamics of motion vision.NEW & NOTEWORTHY Simultaneous multielectrode recordings can measure population-level codes that previously were only inferred from single-electrode recordings. However, many multielectrode recordings are analyzed using univariate single-electrode analysis approaches, which fail to fully utilize the population-level information. Here, we overcome these limitations by applying multivariate pattern classification analysis and representational similarity analysis to large-scale recordings from middle-temporal area (MT) in marmoset monkeys. Our analyses reveal a dynamic interplay of feature representations in area MT population response.

Ultrasonic Neuromodulation: Evidence from Neuron to Animal Model

Neurostimulation is among the fundamental ways of neuroscience to probe the neural mechanisms from molecular to behavioral levels. The cutting-edge discovery of ultrasound as a non-invasive neurostimulation tool, has made possible for the effective amelioration of multiple functional brain diseases such as Parkinson disease, epilepsy and major depression. In this study, we highlight the up-to-date research on the application of the ultrasonic radiation force in the stimulation of isolated neuron, mice and monkeys with the underlying mechanism and the challenges of ultrasonic neuromodulation discussed. A high-frequency neurostimulation chip based on surface acoustic waves (SAWs) was designed and fabricated for the stimulation of isolated neuron. The responses of HEK cells, and neuron under the stimulation of the chip were recorded by the Ca2+ imaging and patch clamp. Confocal imaging of fluo-4-AM fluorescence was conducted on the neuron cells with the neurostimulation chip. The whole-cell voltage clamp was used to record the changes in membrane current in response to stimulation with the chip. Portable and wearable systems incorporating high voltage waveform generator (0.5-5MHz) were designed for free-moving small animal studies. In addition, a wearable 256-element 2D dual-curvature focused ultrasound phased-array transducer and MRI compatible ultrasonic neuromodulation system were developed for primate studies. Rhesus monkeys were trained to perform perceptual tasks with and without intermittent ultrasonic stimulation to specific cerebral cortex. In vivo single or multiple neuron electrode recording and eye tracking were conducted during task performance, allowing an evaluation of the direct linkage between physiology and behavior. Time-lapsed series of confocal images were obtained from neuron cells loaded with fluo-4-AM. Ca2+ transients were triggered in response to the stimulation by the high-frequency neurostimulation chip indicated by the increase in fluo-4 fluorescence intensity. Alterations in the membrane currents in response to high-frequency neurostimulation were recorded for HEK cells and neuron, respectively. Electromyography and local field potential signals were successfully recorded, demonstrating that the ultrasound neuromodulation can induce neuronal activity and evoke motor behaviors of animals. These results manifested that the interaction between ultrasonic radiation force and the protein of cell membranes may lead to the opening of mechanosensitive ion channels, thus triggering effective neuromodulation at both cellular and behavioral levels.

Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach.

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the “TFFO” (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

Brain Mechanism Responsible for "the Treatment of Orofacial Diseases by Acupuncture at Hegu Acupoint"

treatment of orofacial diseases by acupuncture at (LI4), as a conclusion of ancient physicians before more than 1000 years, exhibits effect of acupuncture at point on orofacial diseases, such as toothache and facial paralysis. However, how to correlate and face and its biological mechanism still remain elusive. Here we investigated relationship of somatosensory cortex areas representing face and hands using single electrode and electrode array recording in macaco monkeys. We found that in 3b area, neurons receiving signals from are neighboring to those areas receiving signals from orofacial receptive field separately; convergent neurons of and lower lip were also found. The somatopographic map based on electrophysiological recordings showed that those units are neighboring to each other but not overlapping in 3b area. Three months after ligations of median nerve and musculospiral nerve, plasticity could be found in neurons adjacent in thumb-orofacial regions in 3b area of cortex; region which was response to orofacial stimulation was significantly enlarged to depth of 1-2 mm in cerebral cortex of thumb, suggesting functional reorganization in cortex of thumb. Therefore, the treatment of orofacial diseases by acupuncture at Hegu exhibits adjacent representing areas receiving signals from and face in 3b somatosensory cortex separately, and functional reorganization as representing area invasion may occur under pathological conditions.

Efficient gradient computation for dynamical models

Data assimilation is a fundamental issue that arises across many scales in neuroscience — ranging from the study of single neurons using single electrode recordings to the interaction of thousands of neurons using fMRI. Data assimilation involves inverting a generative model that can not only explain observed data but also generate predictions. Typically, the model is inverted or fitted using conventional tools of (convex) optimization that invariably extremise some functional — norms, minimum descriptive length, variational free energy, etc. Generally, optimisation rests on evaluating the local gradients of the functional to be optimized. In this paper, we compare three different gradient estimation techniques that could be used for extremising any functional in time — (i) finite differences, (ii) forward sensitivities and a method based on (iii) the adjoint of the dynamical system. We demonstrate that the first-order gradients of a dynamical system, linear or non-linear, can be computed most efficiently using the adjoint method. This is particularly true for systems where the number of parameters is greater than the number of states. For such systems, integrating several sensitivity equations – as required with forward sensitivities – proves to be most expensive, while finite-difference approximations have an intermediate efficiency. In the context of neuroimaging, adjoint based inversion of dynamical causal models (DCMs) can, in principle, enable the study of models with large numbers of nodes and parameters.

Monkey neuronal assemblies predict (across objects) human fMRI and behavior

Complex systems (e.g. for object recognition) are classically explored via hierarchical computational models that are derived from principles of primary sensory cortex. Biological guidance for higher stages of these models remains poor, and existing approaches cannot bridge this gap. Single-electrode recordings do not reveal how neurons act as part of local assemblies, e.g. populations of neurons spanning ~500 μm grouped by tuning covariation and coincident spiking. Functional MRI is an indirect measure that blurs across multiple assemblies within each mm[sup]3[/sup] voxel. This problem of spatial scale is compounded by investigator-driven biases in stimulus manipulation, making it virtually impossible to find specific neuronal assemblies and circuits underlying the same complex feature computations across animals and species, or even to confirm feature-specific results across studies. We measured spiking assemblies in inferior temporal cortex, the last stage of the macaque visual object pathway, by combining precise mapping via multi-electrode arrays with precise measurement of assembly tuning covariation and coincident spiking. The assemblies were tuned to complex features in stimulus space, and these ‘key features’ enabled identification of the same feature assembly in another monkey via optical imaging. These monkey complex features also accurately predicted, across objects and in humans, fMRI patterns in lateral occipital complex and perception of a complex visual illusion. Altogether, this preliminary evidence suggests that a common library of complex visual object features, with common organizing principles and computations, is shared across individuals and across species. Meeting abstract presented at VSS 2013

Transient activation of MT neurons to stimulus velocity changes: experiments and modelling

Transient activation of MT neurons to stimulus velocity changes: experiments and modelling

Parvalbumin deficiency affects burst discharges but not network properties of the RTN.

Parvalbumin deficiency affects burst discharges but not network properties of the RTN.

Response properties and receptive field organization of collision-sensitive neurons in the optic tectum of bullfrog, Rana catesbeiana

Many studies have reported that animals will display collision avoidance behavior when the size of retinal image of an object reaches a threshold. The present study aimed to investigate the neural correlates underlying the frog collision avoidance behavior. Different types of visual stimuli simulating the retinal image of an approaching or a recessing object were generated by a computer and presented to the right eye of frog. A multielectrode array was used to examine the activity of collision-sensitive neurons, and single electrode recordings were employed to quantify visual parameter(s) of the frog collision-sensitive neurons. The multielectrode array revealed that 40 neurons in the optic tectum showed selective responsiveness to objects approaching on a direct collision course. The response profiles of these collision-sensitive neurons were similar to those of lobula giant movement detector (LGMD) in the locust or to those of neurons in the pigeon. However, the receptive field (RF) size of the frog neurons [(18.5+/-3.8) degrees, n=33)] was smaller than those of collision-sensitive neurons of the locust and the pigeon. Multielectrode recordings also showed that the collision-sensitive neurons were activated only when the focus of expansion of a looming retinal image was located within the center of its RF. There was a linear relationship between the parameter l/v (l denotes half-size of the object, v denotes approaching velocity) and time-to-collision (time difference between the peak of the neuronal activity and the predictive collision) in 16 collision-sensitive neurons. Theoretical consideration showed that the peak firing rate always occurred at a fixed delay of (60.1 +/- 39.5) ms (n=16) after the object had reached a constant angular size of (14.8 +/- 3.4) degrees (n=16) on the retina. The results may help clarify the mechanisms underlying the collision avoidance behavior in bullfrog.

Patterns recognition in the ECoG data of auditory evoked response

We study the evoked potential of the local neuronal circuits of the left temporal lobe surrounding the cortex in response to two types of simple stimuli. We wish to gain deeper insight into the systems-level functional and behavioral consequences of sensation of a tone that includes a cooperative population of neurons in electrocorticogram (ECoG) recordings. Available experimental data does not have the necessary flexibility and the rich variety of parameter adjustments in the single-electrode recordings needed in order to make the data amenable to conventional analysis. These constraints and potential imperfections in data pose new challenges in the mathematical, computational and statistical aspects of data analysis and modeling. We use the experimental setting called the odd-ball paradigm, to collect intracranial electrophysiological recordings using an 8 × 8 grid. The evoked response potentials that are then analyzed using algorithms that utilize methods of information theory. The extracted patterns of response are used to elucidate cortical substrates of auditory attention and decision-making. While data analysis was performed for the cases of attention as well as inattention, the analysis used the data from inattention for statistical purposes to contrast with data for selective attention. This approach was useful to fine-tune optimization parameters, and to exploit a sharper computational rendering of the Principle of Economy of Resources. The main results presented here are: (1) a computational-mathematical methodology to study cortical response to brief tones in the presence of attention; (2) an application of (1) to neurobiology that provides an algorithm to estimate the transmission time of an stimulus from cochlea until the cortex (approx. 50–60 msec).

Multisite Recording of Extracellular Potentials Produced by Microchannel-Confined Neurons In-Vitro

Towards establishing electrical interfaces with patterned in vitro neurons, we have previously described the fabrication of hybrid elastomer-glass devices polymer-on-multielectrode array technology and obtained single-electrode recordings of extracellular potentials from confined neurons (Claverol-Tinturé et al., 2005). Here, we demonstrate the feasibility of spatially localized multisite recordings from individual microchannel-guided neurites extending from microwell-confined somas with good signal-to-noise ratios (20 dB) and spike magnitudes of up to 300 microV. Single-cell current source density (scCSD) analysis of the spatio-temporal patterns of membrane currents along individual processes is illustrated.