Decreased Beta Power and OFC-STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats.

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input-referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12-30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

Dynamics of Neural Microstates in the VTA-Striatal-Prefrontal Loop during Novelty Exploration in the Rat.

Neural activity at the large-scale population level has been suggested to be consistent with a sequence of brief, quasistable spatial patterns. These "microstates" and their temporal dynamics have been linked to myriad cognitive functions and brain diseases. Most of this research has been performed using EEG, leaving many questions, such as the existence, dynamics, and behavioral relevance of microstates at the level of local field potentials (LFPs), unaddressed. Here, we adapted the standard EEG microstate analysis to triple-area LFP recordings from 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions during novelty exploration. We performed simultaneous recordings from the prefrontal cortex, striatum, and ventral tegmental area in male rats during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring microstates that were stable for ∼60-100 ms. The simultaneous microstate activity across brain regions revealed rhythmic patterns of coactivations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, these rhythmic coactivation patterns across microstates were modulated by behavioral states such as movement and exploration of a novel object. These results support the existence of a functional mesoscopic organization across multiple brain areas and present a possible link of the origin of macroscopic EEG microstates to zero-lag neuronal synchronization within and between brain areas, which is of particular interest to the human research community.SIGNIFICANCE STATEMENT The coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates (i.e., short-lived, recurring spatial patterns of neural activity). We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioral states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

Modeling the Short-Term Dynamics of in Vivo Excitatory Spike Transmission.

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.SIGNIFICANCE STATEMENT Although synaptic dynamics have been extensively studied and modeled using intracellular recordings of postsynaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here, we developed a model that, using only observations of presynaptic and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity (STP) and nonsynaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

EEG microstates as a continuous phenomenon.

In recent years, EEG microstate analysis has gained popularity as a tool to characterize spatio-temporal dynamics of large-scale electrophysiology data. It has been used in a wide range of EEG studies and the discovered microstates have been linked to cognitive function and brain diseases. EEG microstates are assumed to (1) be winner-take-all, meaning that the topography at any given time point is in one state; and (2) discretely transition from one state into another. In this study we investigated these assumptions by taking a geometric perspective of EEG data, treating microstate topographies as basis vectors for a subspace of the original channel space. We found that within- and across-microstate distance distributions were largely overlapping: for the low GFP (Global Field Power) range (lower 15%), individual time points labeled as one microstate are often equidistant to multiple microstate vectors, challenging the winner-take-all assumption. At high global field power, separability of microstates improved, but remained rather weak. Although many GFP peaks (which are the time points used for defining microstates) occur during high GFP ranges, low GFP ranges associated with poor separability also contain GFP peaks. Furthermore, the geometric analysis suggested that microstates and their transitions appear to be more continuous than discrete. The Analysis of rate of change of trajectory in sensor space suggests gradual microstate transitions as opposed to the classical binary view of EEG microstates. Taken together, our findings suggest that EEG microstates are better conceptualized as spatially and temporally continuous, rather than discrete activations of neural populations.

Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks.

Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregular sequences of interspike intervals (ISIs) had a more reliable influence on behavior despite their resemblance to stochastic activity. Similarly, irregular tactile stimulation led to higher rates of behavioral responses. In this study, we identify the mechanisms enabling this sensitivity to stimulus irregularity (SSI) on the neuronal and network levels using simulated spiking neural networks. Matching in vivo experiments, we find that irregular stimulation elicits more detectable network events (bursts) than regular stimulation. Dissecting the stimuli, we identify short ISIs-occurring more frequently in irregular stimulations-as the main drivers of SSI rather than complex irregularity per se. In addition, we find that short-term plasticity modulates SSI. We subsequently eliminate the different mechanisms in turn to assess their role in generating SSI. Removing inhibitory interneurons, we find that SSI is retained, suggesting that SSI is not dependent on inhibition. Removing recurrency, we find that SSI is retained due to the ability of individual neurons to integrate activity over short timescales ("cell memory"). Removing single-neuron dynamics, we find that SSI is retained based on the short-term retention of activity within the recurrent network structure ("network memory"). Finally, using a further simplified probabilistic model, we find that local network structure is not required for SSI. Hence, SSI is identified as a general property that we hypothesize to be ubiquitous in neural networks with different structures and biophysical properties. Irregular sequences contain shorter ISIs, which are the main drivers underlying SSI. The experimentally observed SSI should thus generalize to other systems, suggesting a functional role for irregular activity in cortex.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Saccades create similar mislocalizations in visual and auditory space.

Orienting our eyes to a light, a sound, or a touch occurs effortlessly, despite the fact that sound and touch have to be converted from head- and body-based coordinates to eye-based coordinates to do so. We asked whether the oculomotor representation is also used for localization of sounds even when there is no saccade to the sound source. To address this, we examined whether saccades introduced similar errors of localization judgments for both visual and auditory stimuli. Sixteen subjects indicated the direction of a visual or auditory apparent motion seen or heard between two targets presented either during fixation or straddling a saccade. Compared with the fixation baseline, saccades introduced errors in direction judgments for both visual and auditory stimuli: in both cases, apparent motion judgments were biased in direction of the saccade. These saccade-induced effects across modalities give rise to the possibility of shared, cross-modal location coding for perception and action.

Change Detection in Auditory Textures.

Many natural sounds have spectrotemporal signatures only on a statistical level, e.g. wind, fire or rain. While their local structure is highly variable, the spectrotemporal statistics of these auditory textures can be used for recognition. This suggests the existence of a neural representation of these statistics. To explore their encoding, we investigated the detectability of changes in the spectral statistics in relation to the properties of the change. To achieve precise parameter control, we designed a minimal sound texture--a modified cloud of tones--which retains the central property of auditory textures: solely statistical predictability. Listeners had to rapidly detect a change in the frequency marginal probability of the tone cloud occurring at a random time.The size of change as well as the time available to sample the original statistics were found to correlate positively with performance and negatively with reaction time, suggesting the accumulation of noisy evidence. In summary we quantified dynamic aspects of change detection in statistically defined contexts, and found evidence of integration of statistical information.

Origin of intrinsic irregular firing in cortical interneurons.

Cortical spike trains are highly irregular both during ongoing, spontaneous activity and when driven at high firing rates. There is uncertainty about the source of this irregularity, ranging from intrinsic noise sources in neurons to collective effects in large-scale cortical networks. Cortical interneurons display highly irregular spike times (coefficient of variation of the interspike intervals >1) in response to dc-current injection in vitro. This is in marked contrast to cortical pyramidal cells, which spike highly irregularly in vivo, but regularly in vitro. We show with in vitro recordings and computational models that this is due to the fast activation kinetics of interneuronal K(+) currents. This explanation holds over a wide parameter range and with Gaussian white, power-law, and Ornstein-Uhlenbeck noise. The intrinsically irregular spiking of interneurons could contribute to the irregularity of the cortical network.

Functional connectivity and tuning curves in populations of simultaneously recorded neurons.

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

Putting the tritone paradox into context: insights from neural population decoding and human psychophysics.

The context in which a stimulus occurs can influence its perception. We study contextual effects in audition using the tritone paradox, where a pair of complex (Shepard) tones separated by half an octave can be perceived as ascending or descending. While ambiguous in isolation, they are heard with a clear upward or downward change in pitch, when preceded by spectrally matched biasing sequences. We presented these biased Shepard pairs to awake ferrets and obtained neuronal responses from primary auditory cortex. Using dimensionality reduction from the neural population response, we decode the perceived pitch for each tone. The bias sequence is found to reliably shift the perceived pitch of the tones away from its central frequency. Using human psychophysics, we provide evidence that this shift in pitch is present in active human perception as well. These results are incompatible with the standard absolute distance decoder for Shepard tones, which would have predicted the bias to attract the tones. We propose a relative decoder that takes the stimulus history into account and is consistent with the present and other data sets.

Rodent ultrasonic vocal interaction resolved with millimeter precision using hybrid beamforming.

Ultrasonic vocalizations (USVs) fulfill an important role in communication and navigation in many species. Because of their social and affective significance, rodent USVs are increasingly used as a behavioral measure in neurodevelopmental and neurolinguistic research. Reliably attributing USVs to their emitter during close interactions has emerged as a difficult, key challenge. If addressed, all subsequent analyses gain substantial confidence. We present a hybrid ultrasonic tracking system, Hybrid Vocalization Localizer (HyVL), that synergistically integrates a high-resolution acoustic camera with high-quality ultrasonic microphones. HyVL is the first to achieve millimeter precision (~3.4-4.8 mm, 91% assigned) in localizing USVs, ~3× better than other systems, approaching the physical limits (mouse snout ~10 mm). We analyze mouse courtship interactions and demonstrate that males and females vocalize in starkly different relative spatial positions, and that the fraction of female vocalizations has likely been overestimated previously due to imprecise localization. Further, we find that when two male mice interact with one female, one of the males takes a dominant role in the interaction both in terms of the vocalization rate and the location relative to the female. HyVL substantially improves the precision with which social communication between rodents can be studied. It is also affordable, open-source, easy to set up, can be integrated with existing setups, and reduces the required number of experiments and animals.

Most animals ­ from insects to mammals ­ use vocal sounds to communicate with each other. But not all of these sounds are audible to humans. Frogs, mice and even some primates can produce noises that are ultrasonic, meaning their frequency is so high they cannot be detected by the human ear. These 'ultrasonic vocalizations' are used to relay a variety of signals, including distress, courtship and defense. To understand the role ultrasonic vocalizations play in social interactions, it is important to work out which animal is responsible for emitting the sound. Current methods have a high error rate and often assign vocalizations to the wrong participant, especially if the animals are in close contact with each other. To solve this issue, Sterling et al. developed the hybrid vocalization localizer (HyVL), a system which detects ultrasonic sounds using two different types of microphones. The tool is then able to accurately locate where an ultrasonic vocalization is emitted from within a precision of millimeters. Sterling et al. used their new system to study courtship interactions between two to three mice. The experiments revealed that female courtship vocalizations were substantially rarer than previously reported when two mice were interacting. When three mice were together (one female, two males), Sterling et al. found that one of the male mice typically dominated the conversation. This result was also reflected by the male mouse locating themselves anogenitally to the female, as males tend to vocalize more when in this position. In neuroscience, researchers often measure ultrasonic vocalizations to monitor social interactions between rats and mice. HyVL could provide neuroscientists with a more affordable and easier to use platform for conducting these kinds of experiments, which are important for studying behavior and how the brain develops.

Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding.

Prediction provides key advantages for survival, and cognitive studies have demonstrated that the brain computes multilevel predictions. Evidence for predictions remains elusive at the neuronal level because of the complexity of separating neural activity into predictions and stimulus responses. We overcome this challenge by recording from single neurons from cortical and subcortical auditory regions in anesthetized and awake preparations, during unexpected stimulus omissions interspersed in a regular sequence of tones. We find a subset of neurons that responds reliably to omitted tones. In awake animals, omission responses are similar to anesthetized animals, but larger and more frequent, indicating that the arousal and attentional state levels affect the degree to which predictions are neuronally represented. Omission-sensitive neurons also responded to frequency deviants, with their omission responses getting emphasized in the awake state. Because omission responses occur in the absence of sensory input, they provide solid and empirical evidence for the implementation of a predictive process.

Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity.

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here, we recorded the activity from ∼40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

High-precision spatial analysis of mouse courtship vocalization behavior reveals sex and strain differences.

Mice display a wide repertoire of vocalizations that varies with sex, strain, and context. Especially during social interaction, including sexually motivated dyadic interaction, mice emit sequences of ultrasonic vocalizations (USVs) of high complexity. As animals of both sexes vocalize, a reliable attribution of USVs to their emitter is essential. The state-of-the-art in sound localization for USVs in 2D allows spatial localization at a resolution of multiple centimeters. However, animals interact at closer ranges, e.g. snout-to-snout. Hence, improved algorithms are required to reliably assign USVs. We present a novel algorithm, SLIM (Sound Localization via Intersecting Manifolds), that achieves a 2-3-fold improvement in accuracy (13.1-14.3 mm) using only 4 microphones and extends to many microphones and localization in 3D. This accuracy allows reliable assignment of 84.3% of all USVs in our dataset. We apply SLIM to courtship interactions between adult C57Bl/6J wildtype mice and those carrying a heterozygous Foxp2 variant (R552H). The improved spatial accuracy reveals that vocalization behavior is dependent on the spatial relation between the interacting mice. Female mice vocalized more in close snout-to-snout interaction while male mice vocalized more when the male snout was in close proximity to the female's ano-genital region. Further, we find that the acoustic properties of the ultrasonic vocalizations (duration, Wiener Entropy, and sound level) are dependent on the spatial relation between the interacting mice as well as on the genotype. In conclusion, the improved attribution of vocalizations to their emitters provides a foundation for better understanding social vocal behaviors.

The calyx of Held develops adult-like dynamics and reliability by hearing onset in the mouse in vivo.

The development of the auditory system has received increasing attention since the mechanisms of patterned, spontaneous activity in prehearing mammals were discovered. This early activity originates in the cochlea and is assumed to be of importance for the establishment and refinement of synaptic connections in the auditory system. In the present study we investigate synaptic transmission and its interplay with spontaneous discharges in the developing auditory system. We used the calyx of Held as a model system, where this question can be investigated in vivo over a broad range of ages [postnatal day 8 (P8)-P28]. To precisely quantify the timing and reliability of synaptic transmission, we developed a novel fitting approach which decomposes the extracellularly recorded signal into its presynaptic and postsynaptic components. In prehearing mice, we found signal transmission to be unreliable, with high variability in the transmission delay and in the amplitude of postsynaptic components. These timing and amplitude changes were strongly correlated with the preceding activity. Around hearing onset (P12-P14), the properties of signal transmission converged to the adult-like state which was characterized by high transmission reliability as well as high consistency in timing and amplitude. Although activity-dependent depression was still found in action potentials, EPSP depression no longer played a prominent role. In conclusion, the maturation of synaptic transmission at the calyx of Held seems to be precisely timed to achieve its adult potential by the time acoustically evoked signal processing commences.

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration.

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

The continued need for animals to advance brain research.

Policymakers aim to move toward animal-free alternatives for scientific research and have introduced very strict regulations for animal research. We argue that, for neuroscience research, until viable and translational alternatives become available and the value of these alternatives has been proven, the use of animals should not be compromised.

Early postnatal development of spontaneous and acoustically evoked discharge activity of principal cells of the medial nucleus of the trapezoid body: an in vivo study in mice.

The calyx of Held synapse in the medial nucleus of the trapezoid body of the auditory brainstem has become an established in vitro model to study the development of fast glutamatergic transmission in the mammalian brain. However, we still lack in vivo data at this synapse on the maturation of spontaneous and sound-evoked discharge activity before and during the early phase of acoustically evoked signal processing (i.e., before and after hearing onset). Here we report in vivo single-unit recordings in mice from postnatal day 8 (P8) to P28 with a specific focus on developmental changes around hearing onset (P12). Data were obtained from two mouse strains commonly used in brain slice recordings: CBA/J and C57BL/6J. Spontaneous discharge rates progressively increased from P8 to P13, initially showing bursting patterns and large coefficients of variation (CVs), which changed to more continuous and random discharge activity accompanied by gradual decrease of CV around hearing onset. From P12 on, sound-evoked activity yielded phasic-tonic discharge patterns with discharge rates increasing up to P28. Response thresholds and shapes of tuning curves were adult-like by P14. A gradual shortening in response latencies was observed up to P18. The three-dimensional tonotopic organization of the medial nucleus of the trapezoid body yielded a high-to-low frequency gradient along the mediolateral and dorsoventral but not in the rostrocaudal axes. These data emphasize that models of signal transmission at the calyx of Held based on in vitro data have to take developmental changes in firing rates and response latencies up to the fourth postnatal week into account.

Presynaptic and postsynaptic origin of multicomponent extracellular waveforms at the endbulb of Held-spherical bushy cell synapse.

Extracellular signals from the endbulb of Held-spherical bushy cell (SBC) synapse exhibit up to three component waves ('P', 'A' and 'B'). Signals lacking the third component (B) are frequently observed but as the origin of each of the components is uncertain, interpretation of this lack of B has been controversial: is it a failure to release transmitter or a failure to generate or propagate an action potential? Our aim was to determine the origin of each component. We combined single- and multiunit in vitro methods in Mongolian gerbils and Wistar rats and used pharmacological tools to modulate glutamate receptors or voltage-gated sodium channels. Simultaneous extra- and intracellular recordings from single SBCs demonstrated a presynaptic origin of the P-component, consistent with data obtained with multielectrode array recordings of local field potentials. The later components (A and B) correspond to the excitatory postsynaptic potential (EPSP) and action potential of the SBC, respectively. These results allow a clear interpretation of in vivo extracellular signals. We conclude that action potential failures occurring at the endbulb-SBC synaptic junction largely reflect failures of the EPSP to trigger an action potential and not failures of synaptic transmission. The data provide the basis for future investigation of convergence of excitatory and inhibitory inputs in modulating transmission at a fully functional neuronal system using physiological stimulation.