Single-unit Spike Research Articles

Response properties of neurons in the cat’s putamen during auditory discrimination

The striatum integrates diverse convergent input and plays a critical role in the goal-directed behaviors. To date, the auditory functions of striatum are less studied. Recently, it was demonstrated that auditory cortico-striatal projections influence behavioral performance during a frequency discrimination task. To reveal the functions of striatal neurons in auditory discrimination, we recorded the single-unit spike activities in the putamen (dorsal striatum) of free-moving cats while performing a Go/No-go task to discriminate the sounds with different modulation rates (12.5Hz vs. 50Hz) or envelopes (damped vs. ramped). We found that the putamen neurons can be broadly divided into four groups according to their contributions to sound discrimination. First, 40% of neurons showed vigorous responses synchronized to the sound envelope, and could precisely discriminate different sounds. Second, 18% of neurons showed a high preference of ramped to damped sounds, but no preference for modulation rate. They could only discriminate the change of sound envelope. Third, 27% of neurons rapidly adapted to the sound stimuli, had no ability of sound discrimination. Fourth, 15% of neurons discriminated the sounds dependent on the reward-prediction. Comparing to passively listening condition, the activities of putamen neurons were significantly enhanced by the engagement of the auditory tasks, but not modulated by the cat’s behavioral choice. The coexistence of multiple types of neurons suggests that the putamen is involved in the transformation from auditory representation to stimulus-reward association.

Fidelity of frequency and phase entrainment of circuit-level spike activity during DBS.

High-frequency stimulation is known to entrain spike activity downstream and upstream of several clinical deep brain stimulation (DBS) targets, including the cerebellar-receiving area of thalamus (VPLo), subthalamic nucleus (STN), and globus pallidus (GP). Less understood are the fidelity of entrainment to each stimulus pulse, whether entrainment patterns are stationary over time, and how responses differ among DBS targets. In this study, three rhesus macaques were implanted with a single DBS lead in VPLo, STN, or GP. Single-unit spike activity was recorded in the resting state in motor cortex during VPLo DBS, in GP during STN DBS, and in STN and pallidal-receiving area of motor thalamus (VLo) during GP DBS. VPLo DBS induced time-locked spike activity in 25% (n = 15/61) of motor cortex cells, with entrained cells following 7.5 ± 7.4% of delivered pulses. STN DBS entrained spike activity in 26% (n = 8/27) of GP cells, which yielded time-locked spike activity for 8.7 ± 8.4% of stimulus pulses. GP DBS entrained 67% (n = 14/21) of STN cells and 32% (n = 19/59) of VLo cells, which showed a higher fraction of pulses effectively inhibiting spike activity (82.0 ± 9.6% and 86.1 ± 16.6%, respectively). Latency of phase-locked spike activity increased over time in motor cortex (58%, VPLo DBS) and to a lesser extent in GP (25%, STN DBS). In contrast, the initial inhibitory phase observed in VLo and STN during GP DBS remained stable following stimulation onset. Together, these data suggest that circuit-level entrainment is low-pass filtered during high-frequency stimulation, most notably for glutamatergic pathways. Moreover, phase entrainment is not stationary or consistent at the circuit level for all DBS targets.

Quantifying spike train oscillations: biases, distortions and solutions.

Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols.

Movement-related discharge in the macaque globus pallidus during high-frequency stimulation of the subthalamic nucleus.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) has largely replaced ablative therapies for Parkinson's disease. Because of the similar efficacies of the two treatments, it has been proposed that DBS acts by creating an "informational lesion," whereby pathologic neuronal firing patterns are replaced by low-entropy, stimulus-entrained firing patterns. The informational lesion hypothesis, in its current form, states that DBS blocks the transmission of all information from the basal ganglia, including both pathologic firing patterns and normal, task-related modulations in activity. We tested this prediction in two healthy rhesus macaques by recording single-unit spiking activity from the globus pallidus (232 neurons) while the animals completed choice reaction time reaching movements with and without STN-DBS. Despite strong effects of DBS on the activity of most pallidal cells, reach-related modulations in firing rate were equally prevalent in the DBS-on and DBS-off states. This remained true even when the analysis was restricted to cells affected significantly by DBS. In addition, the overall form and timing of perimovement modulations in firing rate were preserved between DBS-on and DBS-off states in the majority of neurons (66%). Active movement and DBS had largely additive effects on the firing rate of most neurons, indicating an orthogonal relationship in which both inputs contribute independently to the overall firing rate of pallidal neurons. These findings suggest that STN-DBS does not act as an indiscriminate informational lesion but rather as a filter that permits task-related modulations in activity while, presumably, eliminating the pathological firing associated with parkinsonism.

Task‐related changes in sensorimotor integration influence the common synaptic input to motor neurones

The purpose of this investigation was to understand how visual information, when used to guide muscle activity, influences the frequency content of the neural drive to muscles and the gain of afferent feedback. Subjects maintained static, isometric contractions of the tibialis anterior muscle by matching a visual display of their ankle dorsiflexion force to a target set at 10% of their maximum voluntary contraction level. Two visual feedback conditions were studied. The first was a high-sensitivity feedback, in which small changes in force were of large on-screen visual magnitude. The second was a low-sensitivity feedback, in which the on-screen scaling of feedback was reduced by a factor of 10, making small force fluctuations difficult to perceive. Force tremor and Hoffmann reflex (H-reflex) amplitudes were compared between the two conditions, as well as coherence among single motor unit spike trains derived from high-density EMG recordings. The high-sensitivity feedback condition was associated with lower error, larger force tremor (4-12 Hz) and larger H-reflex amplitudes relative to the low-sensitivity feedback condition. In addition, the use of high-sensitivity feedback was associated with lower 1-5 Hz coherence among pairs of motor units, but larger coherence at high frequencies (6-12, approx. 20, >30 Hz). Alteration of visual feedback influences nearly the entire frequency spectrum of common input to motor neurones, as well the gain of afferent feedback. We speculate that task-related modulation of afferent feedback could be the origin of many of the observed changes in the neural drive to muscles.

Behavioral Modulation of Neural Encoding of Click-Trains in the Primary and Nonprimary Auditory Cortex of Cats

Neural representation of acoustic stimuli in the mammal auditory cortex (AC) has been extensively studied using anesthetized or awake nonbehaving animals. Recently, several studies have shown that active engagement in an auditory behavioral task can substantially change the neuron response properties compared with when animals were passively listening to the same sounds; however, these studies mainly investigated the effect of behavioral state on the primary auditory cortex and the reported effects were inconsistent. Here, we examined the single-unit spike activities in both the primary and nonprimary areas along the dorsal-to-ventral direction of the cat's AC, when the cat was actively discriminating click-trains at different repetition rates and when it was passively listening to the same stimuli. We found that the changes due to task engagement were heterogeneous in the primary AC; some neurons showed significant increases in driven firing rate, others showed decreases. But in the nonprimary AC, task engagement predominantly enhanced the neural responses, resulting in a substantial improvement of the neural discriminability of click-trains. Additionally, our results revealed that neural responses synchronizing to click-trains gradually decreased along the dorsal-to-ventral direction of cat AC, while nonsynchronizing responses remained less changed. The present study provides new insights into the hierarchical organization of AC along the dorsal-to-ventral direction and highlights the importance of using behavioral animals to investigate the later stages of cortical processing.

Long term, stable brain machine interface performance using local field potentials and multiunit spikes

Objective. Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor. Approach. We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor. Main results. Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements. Significance. Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling

We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

While monkeys perform a task alternating between behavioral adaptation --relying on feedback monitoring and memory of previous choices-- and repetition of previous actions, firing rates in dorsal Anterior Cingulate Cortex (dACC) modulate with cognitive control levels [1]. Further, it has been hypothesized that dorsolateral Prefrontal Cortex (dlPFC) could make use of dACC signals to take the adapted decision [2]. However, little is known about whether dACC spike timing may participate to behavioral adaptation signaling, and how multiple unit activities may be jointly decoded by dlPFC. We tested the hypothesis that temporal (or spatiotemporal) pattern matching mediate information transmission by dACC. We used spike-train metrics [3] to decode dACC activity recorded in a problem solving task [1] (336 cells, 2 monkeys). We computed a similarity measure between every spike train and the activity patterns in different task epochs for classification. When computing the similarity, we explored different degrees of (i) timing sensitivity and (ii) distinction between spikes from different neurons, which led to different classification performances. We found that timing sensitivity could improve behavioral adaptation vs. repetition classification of single unit spike trains. Optimal decoding occurred when accounting for spike times at a resolution <= 200 ms. Furthermore, spike-train metrics decoding of unitary discharges was related to the monkeys' response time. A downstream neural decoder could exploit this temporal information through coincidence detection determined by synaptic and membrane time constants. In addition, when decoding two units jointly, we found that each pair had a specific optimal distinction degree between spikes coming from the two different neurons. In a realistic neural decoder, the tuning of this distinction degree might occur through non-linear dendritic integration. To further investigate the computational properties of temporal decoding in the context of decision-making, we are implementing a recurrent spiking neural network with connectivity leading to attractor dynamics. In this framework, each decision is mapped to a state in which a corresponding neural subpopulation shows elevated activity, as observed experimentally in dlPFC [4]. In addition, neurons will undergo membrane properties adaptation [5] and short-term plasticity, leading to history-of-choices dependent temporal spike patterns. We will investigate how this temporal-spike-patterns memory of past choices could interact with dACC feedback-specific temporal patterns to lead to adapted decision. The feasibility and putative advantages of spike-timing-dependent-plasticity-based learning of response selectivity to the appropriate dACC-dlPFC temporal correlations will be considered.

Inducing Gamma Oscillations and Precise Spike Synchrony by Operant Conditioning via Brain-Machine Interface

Neural oscillations in the low-gamma range (30-50 Hz) have been implicated in neuronal synchrony, computation, behavior, and cognition. Abnormal low-gamma activity, hypothesized to reflect impaired synchronization, has been evidenced in several brain disorders. Thus, understanding the relations between gamma oscillations, neuronal synchrony and behavior is a major research challenge. We used a brain-machine interface (BMI) to train monkeys to specifically increase low-gamma power in selected sites of motor cortex to move a cursor and obtain a reward. The monkeys learned to robustly generate oscillatory gamma waves, which were accompanied by a dramatic increase of spiking synchrony of highly precise spatiotemporal patterns. The findings link volitional control of LFP oscillations, neuronal synchrony, and the behavioral outcome. Subjects' ability to directly modulate specific patterns of neuronal synchrony provides a powerful approach for understanding neuronal processing in relation to behavior and for the use of BMIs in a clinical setting.

Asymmetric response properties of rapidly adapting mechanoreceptive fibers in the rat glabrous skin

Previous histological and neurophysiological studies have shown that the innervation density of rapidly adapting (RA) mechanoreceptive fibers increases towards the fingertip. Since the psychophysical detection threshold depends on the contribution of several RA fibers, a high innervation density would imply lower thresholds. However, our previous human study showed that psychophysical detection thresholds for the Non-Pacinian I channel mediated by RA fibers do not improve towards the fingertip. By recording single-unit spike activity from rat RA fibers, here we tested the hypothesis that the responsiveness of RA fibers is asymmetric in the proximo-distal axis which may counterbalance the effects of innervation density. RA fibers (n = 32) innervating the digital glabrous skin of rat hind paw were stimulated with 40-Hz sinusoidal mechanical bursts at five different stimulus locations relative to the receptive field (RF) center (two distal, one RF center, two proximal). Different contactor sizes (area: 0.39, 1.63, 2.96 mm2) were used. Rate-intensity functions were constructed based on average firing rates, and the absolute spike threshold and the entrainment threshold were obtained for each RA fiber. Thresholds for proximal stimulus locations were found to be significantly higher than those for distal stimulus locations, which suggests that the mechanical stimulus is transmitted better towards the proximal direction. The effect of contactor size was not significant. Mechanical impedance of the rat digital glabrous skin was further measured and a lumped-parameter model was proposed to interpret the relationship between the asymmetric response properties of RA fibers and the mechanical properties of the skin.

NMDA Receptors Control Cue-Outcome Selectivity and Plasticity of Orbitofrontal Firing Patterns during Associative Stimulus-Reward Learning

Neural activity in orbitofrontal cortex has been linked to flexible representations of stimulus-outcome associations. Such value representations are known to emerge with learning, but the neural mechanisms supporting this phenomenon are not well understood. Here, we provide evidence for a causal role for NMDA receptors (NMDARs) in mediating spike pattern discriminability, neural plasticity, and rhythmic synchronization in relation to evaluative stimulus processing and decision making. Using tetrodes, single-unit spike trains and local field potentials were recorded during local, unilateral perfusion of an NMDAR blocker in rat OFC. In the absence of behavioral effects, NMDAR blockade severely hampered outcome-selective spike pattern formation to olfactory cues, relative to control perfusions. Moreover, NMDAR blockade shifted local rhythmicsynchronization to higher frequencies and degradedits linkage to stimulus-outcome selective coding.These results demonstrate the importance of NMDARs for cue-outcome associative coding in OFC during learning and illustrate how NMDAR blockade disrupts network dynamics.

On the origin of event-related potentials indexing covert attentional selection during visual search: timing of selection by macaque frontal eye field and event-related potentials during pop-out search.

Event-related potentials (ERPs) have provided crucial data concerning the time course of psychological processes, but the neural mechanisms producing ERP components remain poorly understood. This study continues a program of research in which we investigated the neural basis of attention-related ERP components by simultaneously recording intracranially and extracranially from macaque monkeys. Here, we compare the timing of attentional selection by the macaque homologue of the human N2pc component (m-N2pc) with the timing of selection in the frontal eye field (FEF), an attentional-control structure believed to influence posterior visual areas thought to generate the N2pc. We recorded FEF single-unit spiking and local field potentials (LFPs) simultaneously with the m-N2pc in monkeys performing an efficient pop-out search task. We assessed how the timing of attentional selection depends on task demands by direct comparison with a previous study of inefficient search in the same monkeys (e.g., finding a T among Ls). Target selection by FEF spikes, LFPs, and the m-N2pc was earlier during efficient pop-out search rather than during inefficient search. The timing and magnitude of selection in all three signals varied with set size during inefficient but not efficient search. During pop-out search, attentional selection was evident in FEF spiking and LFP before the m-N2pc, following the same sequence observed during inefficient search. These observations are consistent with the hypothesis that feedback from FEF modulates neural activity in posterior regions that appear to generate the m-N2pc even when competition for attention among items in a visual scene is minimal.

Coding of digit displacement by cell spiking and network oscillations in the monkey sensorimotor cortex.

β-Band oscillations occur in motor and somatosensory cortices and muscle activity. Oscillations appear most strongly after movements, suggesting that they may represent or probe the limb's final sensory state. We tested this idea by training two macaque monkeys to perform a finger flexion to one of four displacements, which was then held for 2 s without visual feedback of absolute displacement. Local field potential (LFP) and single unit spiking were recorded from the rostral and caudal primary motor cortex and parietal areas 3a, 3b, 2, and 5. Information theoretic analysis determined how well unit firing rate or the power of LFP oscillations coded finger displacement. All areas encoded significant information about finger displacement after the movement into target, both in β-band (∼20 Hz) oscillatory activity and unit firing rate. On average, the information carried by unit firing was greater (0.07 bits) and peaked earlier (0.73 s after peak velocity) than that by LFP β-oscillations (0.05 bits and 0.95 s). However, there was considerable heterogeneity among units: some cells did not encode maximal information until midway through the holding phase. In 30% of cells, information in rate lagged information in LFP oscillations recorded at the same site. Finger displacement may be represented in the cortex in multiple ways. Coding the digit configuration immediately after a movement probably relies on nonoscillatory feedback, or efference copy. With increasing delay after movement cessation, oscillatory processing may also play a part.

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution.

Influence of unit distance and conduction velocity on the spectra of extracellular action potentials recorded with intrafascicular electrodes

The use of highly selective penetrating electrodes yields multi-unit extracellular action potential (AP) recordings of the nerve fibers in the vicinity of the electrode. Accessing the information carried within the neural data stream further requires discrimination and separation of the multi-unit recording into their constituent multiple single unit spike trains. Shape differences in the single fiber action potentials (SFAPs) are typically used as the criteria for unit separation. The present paper explores the origins of the shape differences through analysis of the SFAP in the frequency domain. We present the derivation and computational model predictions of a method to quantitatively analyse changes in the spectral components of SFAPs with an axially located intrafascicular electrode with non-radially symmetrical sensitivity function. A spatial tissue filter relationship was derived using reciprocity equations in the spatial frequency domain and transformed to time frequency. A three dimensional bioelectrical volume conductor finite element model of a recording electrode residing in a nerve fascicle was developed to explore the potential distribution in the nerve fascicle and further derive the electrode–fiber coupling function in the time-frequency domain. It was found that the spectral distribution of the SFAP was multimodal in nature, similar to empirical reported earlier, and could be predicted by taking the single fiber action currents (SFACs) filtered by the electrode–fiber coupling function. This function manifested itself as a low-pass filter of the SFAC, dependent upon the fiber's location relative to the electrode and conduction velocity. Analysis of the spectral distribution revealed that changes in the landmarks of the distribution could be related to the fiber location and conduction velocity. Moreover, a consistent relationship was found when exploring the distribution of fibers located off the one axis of symmetry.

Preparatory Attention Relies on Dynamic Interactions between Prelimbic Cortex and Anterior Cingulate Cortex

An emerging view of prefrontal cortex (PFC) function is that multiple PFC areas process information in parallel, rather than as distinct modules. Two key functions assigned to the PFC are the regulation of top-down attention and stimulus-guided action. Electrophysiology and lesion studies indicate the involvement of both the anterior cingulate cortex (ACC) and prelimbic cortex (PL) in these functions. Little is known, however, about how these cortical regions interact. We recorded single unit spiking and local field potentials (LFPs) simultaneously in rodents during a sustained attention task and assessed interactions between the ACC and PL by measuring spike-LFP phase synchrony and LFP-LFP phase synchrony between these areas. We demonstrate that the magnitude of synchrony between the ACC and PL, before stimulus onset, predicts the subjects' behavioral choice after the stimulus. Furthermore, neurons switched from a state of beta synchrony during attention to a state of delta synchrony before the instrumental action. Our results indicate that multiple PFC areas interact during attention and that the same neurons may participate in segregated assemblies that support both attention and action.

Dysfunctional Prefrontal Cortical Network Activity and Interactions following Cannabinoid Receptor Activation

Coordinated activity spanning anatomically distributed neuronal networks underpins cognition and mediates limbic-cortical interactions during learning, memory, and decision-making. We used CP55940, a potent agonist of brain cannabinoid receptors known to disrupt coordinated activity in hippocampus, to investigate the roles of network oscillations during hippocampal and medial prefrontal cortical (mPFC) interactions in rats. During quiet wakefulness and rest, CP55940 dose-dependently reduced 0.1-30 Hz local field potential power in CA1 of the hippocampus while concurrently decreasing 30-100 Hz power in mPFC; these contrasting population-level effects were paralleled by differential effects on underlying single-unit activity in the two structures. During decision-making phases of a spatial working memory task, CP5540-induced deficits in hippocampal theta and prefrontal gamma oscillations were observed alongside disrupted theta-frequency coherence between the two structures. These changes in coordinated limbic-cortical network activities correlated with (1) reduced accuracy of task performance, (2) impaired phase-locking of prefrontal single-unit spiking to the local gamma and hippocampal theta rhythms, and (3) impaired task-dependent activity in a subset of mPFC units. In addition to highlighting the importance of CA1-mPFC network oscillations for cognition, these results implicate disrupted theta-frequency coordination of CA1-mPFC activity in the cognitive deficits caused by exogenous activation of brain cannabinoid receptors.

Binding and selective attention increase coherence between distant sites in early visual cortex

We have proposed that the border ownership selectivity that is observed in many neurons in early visual cortex is the result of specific network connectivity (Craft et. al., J. Neurophysiol. 97:4310–26, 2007). According to our model, border ownership cells (B-cells) have reciprocal connections with grouping cells (G-cells) in another level of cortex. A G-cell integrates inputs from B-cells and modulates their firing rates. When a pair of neurons is activated by edges that are part of one figure (the contours are bound), the neurons receive reentrant inputs from the same G-cell. When the contours are part of separate figures, the inputs come from different G-cells. Thus the model predicts that coherence will increase between pairs with bound edges in their receptive fields. Specifically, because pairs that both prefer the bound configuration are connected to the same G-cell, they should display enhanced coherence with binding compared to other pairs. In the model, attention is inserted at a G-cell and propagates back to the B-cells, modulating their firing rates for further processing. Thus as with binding, the model predicts that coherence will increase with attention, especially for pairs that both prefer the bound configuration. We tested these predictions by recording local field potentials and single-unit spiking activity from two electrodes separated by long distances (3–10 mm) in macaque areas V1 and V2. We found that binding increases coherence at around 20 Hz, especially for pairs that both prefer the bound configuration. Selective attention also increases coherence in the same range, though the effect is more subtle. As with binding, the increase in coherence with attention is more pronounced for pairs that both prefer the bound configuration. These results support the network connectivity proposed by the model. The coherence difference (although not gamma) may play a role in coding binding and selective attention.

Developing implantable neuroprosthetics: a new model in pig.

A new model has been established in the domestic pig for neural prosthetic device development and testing. To this end, we report on a complete neural prosthetic developmental system using a wireless sensor as the implant, a pig as the animal model, and a novel data acquisition paradigm for actuator control. A new type of stereotactic frame with clinically-inspired fixations pins that place the pig brain in standard surgical plane was developed and tested with success during the implantation of the microsystem. The microsystem implanted was an ultra-low power (12.5 mW) 16-channel intracortical/epicranial device transmitting broadband (40 kS/s) data over a wireless infrared telemetric link. Pigs were implanted and neural data was collected over a period of 5 weeks, clearly showing single unit spiking activity.