Feedback Inhibition and Throughput Properties of an Integrate-and-Fire-or-Burst Network Model of Retinogeniculate Transmission

Computational modeling has played an important role in the dissection of the biophysical basis of rhythmic oscillations in thalamus that are associated with sleep and certain forms of epilepsy. In contrast, the dynamic filter properties of thalamic relay nuclei during states of arousal are not well understood. Here we present a modeling and simulation study of the throughput properties of the visually driven dorsal lateral geniculate nucleus (dLGN) in the presence of feedback inhibition from the perigeniculate nucleus (PGN). We employ thalamocortical (TC) and thalamic reticular (RE) versions of a minimal integrate-and-fire-or-burst type model and a one-dimensional, two-layered network architecture. Potassium leakage conductances control the neuromodulatory state of the network and eliminate rhythmic bursting in the presence of spontaneous input (i.e., wake up the network). The aroused dLGN/PGN network model is subsequently stimulated by spatially homogeneous spontaneous retinal input or spatio-temporally patterned input consistent with the activity of X-type retinal ganglion cells during full-field or drifting grating visual stimulation. The throughput properties of this visually-driven dLGN/PGN network model are characterized and quantified as a function of stimulus parameters such as contrast, temporal frequency, and spatial frequency. During low-frequency oscillatory full-field stimulation, feedback inhibition from RE neurons often leads to TC neuron burst responses, while at high frequency tonic responses dominate. Depending on the average rate of stimulation, contrast level, and temporal frequency of modulation, the TC and RE cell bursts may or may not be phase-locked to the visual stimulus. During drifting-grating stimulation, phase-locked bursts often occur for sufficiently high contrast so long as the spatial period of the grating is not small compared to the synaptic footprint length, i.e., the spatial scale of the network connectivity.     

The initiation of bursts in thalamic neurons and the cortical control of thalamic sensitivity

Thalamic neurons generate high-frequency bursts of action potentials when a low-threshold (T-type) calcium current, located in soma and dendrites, becomes activated. Computational models were used to investigate the bursting properties of thalamic relay and reticular neurons. These two types of thalamic cells differ fundamentally in their ability to generate bursts following either excitatory or inhibitory events. Bursts generated with excitatory inputs in relay cells required a high degree of convergence from excitatory inputs, whereas moderate excitation drove burst discharges in reticular neurons from hyperpolarized levels. The opposite holds for inhibitory rebound bursts, which are more difficult to evoke in reticular neurons than in relay cells. The differences between the reticular neurons and thalamocortical neurons were due to different kinetics of the T-current, different electrotonic properties and different distribution patterns of the T-current in the two cell types. These properties enable the cortex to control the sensitivity of the thalamus to inputs and are also important for understanding states such as absence seizures.     

Interactive responses of a thalamic neuron to formalin induced lasting pain in behaving mice

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice.     

Purkyn?'s description of pressure phosphenes and modern neurophysiological studies on the generation of phosphenes by eyeball deformation

(a) When a subject indents one of his eyeballs in total darkness, he immediately perceives light extending slowly across the whole visual field of the indented eye. The appearance and the time course of these pressure or deformation phosphenes are described. (b) With simultaneous binocular indentation of the eyeballs a flickering patterned phosphene is observed. (c) A short history of the research on pressure phosphenes and its consequences for the theories of vision is presented. (d) Purkyn?'s observations of monocular deformation phosphenes are described. He repeatedly noted patterned light structures, which most observers only perceive with simultaneous binocular eyeball deformation. It is suggested that Purkyn?'s deviating observations were caused by amblyopia of one eye. (e) The neurophysiological basis of the monocular pressure phosphenes was investigated by means of microelectrode recordings from single optic tract fibers. The activity of single retinal ganglion cells (on-center, off-center neurons, latency class I [Y-neurons] or latency class II [X-neurons]), was recorded in anaesthetized cats. Eyeball deformation in total darkness led to an activation of the on-center ganglion cells, while the off-center ganglion cells were inhibited. The latency and strength of this activation or inhibition varied considerably between different neurons, but were fairly constant in the same neuron when the eyeball indentation was repeated after a pause of 1-3 min. The latency and strength of neuronal activation or inhibition seemed to be dependent mainly upon the neuron location relative to the point of eyeball indentation. Some on-center neurons also exhibited a short activation at "deformation off". (f) The antagonistic response type of on-center and off-center ganglion cells was also observed when the eyeball was deformed as a hydrostatic open system and the intraocular pressure was kept at 25 mm Hg basic pressure. (g) Dark adaptation up to 45 min affected the deformation responses of retinal neurons only to a small degree, if at all. This corresponds to the observation that deformation phosphenes in a human observer changed little during the course of dark adaptation. (h) We assume that the activation of on-center and inhibition of off-center ganglion cells by eyeball deformation are caused by retinal stretching, which also leads to horizontal cell stretch. Stretching the horizontal cell membrane probably generates an increase in membrane sodium conductivity and a depolarization of the membrane potential. This depolarization of the horizontal cell membrane potential is transmitted either directly or indirectly (via receptor synapses) from the horizontal to the bipolar cells.(ABSTRACT TRUNCATED AT 400 WORDS)     

A multivariate population density model of the dLGN/PGN relay

Using a population density approach we study the dynamics of two interacting collections of integrate-and-fire-or-burst (IFB) neurons representing thalamocortical (TC) cells from the dorsal lateral geniculate nucleus (dLGN) and thalamic reticular (RE) cells from the perigeniculate nucleus (PGN). Each population of neurons is described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. The state variables of each neuron are the membrane potential and the inactivation gating variable of the low-threshold Ca²⁺ current I_T. The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. The population density model is validated by comparing its response to time-varying retinal input to Monte Carlo simulations of the corresponding IFB network composed of 100 to 1000 cells per population. In the absence of retinal input, the population density model exhibits rhythmic bursting similar to the 7 to 14 Hz oscillations associated with slow wave sleep that require feedback inhibition from RE to TC cells. When the TC and RE cell potassium leakage conductances are adjusted to represent cholingergic neuromodulation and arousal of the network, rhythmic bursting of the probability density model may either persists or be eliminated depending on the number of excitatory (TC to RE) or inhibitory (RE to TC) connections made by each presynaptic cell. When the probability density model is stimulated with constant retinal input (10–100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. In this context of dLGN/PGN network modeling, we find the population density approach using 2,500 mesh points and resolving membrane voltage to 0.7 mV is over 30 times more efficient than 1000-cell Monte Carlo simulations. Action Editor: David Golomb     

Reverse spikeology: predicting single spikes.

Neural models that simulate single spike trains can help us understand the basic principles of neural coding in vision. Keat et al. (2001) develop a hybrid model that combines spatiotemporal filtering with nonlinear spike generation. The model does a good job of predicting the responses of single retinal ganglion cells and thalamic relay neurons.     

Spatial and temporal correlations of spike trains in frog retinal ganglion cells

For a neuron, firing activity can be in synchrony with that of others, which results in spatial correlation; on the other hand, spike events within each individual spike train may also correlate with each other, which results in temporal correlation. In order to investigate the relationship between these two phenomena, population neurons’ activities of frog retinal ganglion cells in response to binary pseudo-random checker-board flickering were recorded via a multi-electrode recording system. The spatial correlation index (SCI) and temporal correlation index (TCI) were calculated for the investigated neurons. Statistical results showed that, for a single neuron, the SCI and TCI values were highly related—a neuron with a high SCI value generally had a high TCI value, and these two indices were both associated with burst activities in spike train of the investigated neuron. These results may suggest that spatial and temporal correlations of single neuron’s spiking activities could be mutually modulated; and that burst activities could play a role in the modulation. We also applied models to test the contribution of spatial and temporal correlations for visual information processing. We show that a model considering spatial and temporal correlations could predict spikes more accurately than a model does not include any correlation.     

Co-variation of ionic conductances supports phase maintenance in stomatogastric neurons

Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. Central pattern generators, such as those of the crustacean stomatogastric ganglion (STG), are able to robustly maintain their functionality over a wide range of burst periods. Previous experimental work involving extracellular recordings of the pyloric pattern of the STG has demonstrated that as the burst period varies, the inter-neuronal delays are altered proportionally, resulting in burst phases that are roughly invariant. The question whether spike delays within bursts are also proportional to pyloric period has not been explored in detail. The mechanism by which the pyloric neurons accomplish phase maintenance is currently not obvious. Previous studies suggest that the co-regulation of certain ion channel properties may play a role in governing neuronal activity. Here, we observed in long-term recordings of the pyloric rhythm that spike delays can vary proportionally with burst period, so that spike phase is maintained. We then used a conductance-based model neuron to determine whether co-varying ionic membrane conductances results in neural output that emulates the experimentally observed phenomenon of spike phase maintenance. Next, we utilized a model neuron database to determine whether conductance correlations exist in model neuron populations with highly maintained spike phases. We found that co-varying certain conductances, including the sodium and transient calcium conductance pair, causes the model neuron to maintain a specific spike phase pattern. Results indicate a possible relationship between conductance co-regulation and phase maintenance in STG neurons.     

Synaptic organization and ionic basis of on and off channels in mudpuppy retina. III. A model of ganglion cell receptive field organization based on chloride-free experiments

A chloride-free environment produces selective changes in the retinal network which include a separation of on and off channels. The identification of chloride-sensitive and insensitivie neuronal activity permits identification of some of the connections and intervening polarities of synaptic interactions which are expressed in ganglion cell receptive field organization. These experiments support previous suggestions that surround antagonism is dependent on horizontal cell activity. In addition they suggest a model of the neuronal connections which subserve on-center, off-center, and on-off ganglion cells. Experimental tests of the on-off ganglion cell model favor the idea that this type of ganglion cell receives inhibitory input from amacrine cells and excitatory activation from depolarizing and hyperpolarizing bipolar cells.     

Reliability of spike and burst firing in thalamocortical relay cells

The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between ?80 and ?50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.     

The role of the thalamus in the flow of information to the cortex

The lateral geniculate nucleus is the best understood thalamic relay and serves as a model for all thalamic relays. Only 5-10% of the input to geniculate relay cells derives from the retina, which is the driving input. The rest is modulatory and derives from local inhibitory inputs, descending inputs from layer 6 of the visual cortex, and ascending inputs from the brainstem. These modulatory inputs control many features of retinogeniculate transmission. One such feature is the response mode, burst or tonic, of relay cells, which relates to the attentional demands at the moment. This response mode depends on membrane potential, which is controlled effectively by the modulator inputs. The lateral geniculate nucleus is a first-order relay, because it relays subcortical (i.e. retinal) information to the cortex for the first time. By contrast, the other main thalamic relay of visual information, the pulvinar region, is largely a higher-order relay, since much of it relays information from layer 5 of one cortical area to another. All thalamic relays receive a layer-6 modulatory input from cortex, but higher-order relays in addition receive a layer-5 driver input. Corticocortical processing may involve these corticothalamocortical 're-entry' routes to a far greater extent than previously appreciated. If so, the thalamus sits at an indispensable position for the modulation of messages involved in corticocortical processing.     

Sharpening of directional selectivity from neural output of rabbit retina

The estimation of motion direction from time varying retinal images is a fundamental task of visual systems. Neurons that selectively respond to directional visual motion are found in almost all species. In many of them already in the retina direction selective neurons signal their preferred direction of movement. Scientific evidences suggest that direction selectivity is carried from the retina to higher brain areas. Here we adopt a simple integrate-and-fire neuron model, inspired by recent work of Casti et al. (2008), to investigate how directional selectivity changes in cells postsynaptic to directional selective retinal ganglion cells (DSRGC). Our model analysis shows that directional selectivity in the postsynaptic cells increases over a wide parameter range. The degree of directional selectivity positively correlates with the probability of burst-like firing of presynaptic DSRGCs. Postsynaptic potentials summation and spike threshold act together as a temporal filter upon the input spike train. Prior to the intricacy of neural circuitry between retina and higher brain areas, we suggest that sharpening is a straightforward result of the intrinsic spiking pattern of the DSRGCs combined with the summation of excitatory postsynaptic potentials and the spike threshold in postsynaptic neurons.     

Bursts and isolated spikes code for opposite movement directions in midbrain electrosensory neurons

Directional selectivity, in which neurons respond strongly to an object moving in a given direction but weakly or not at all to the same object moving in the opposite direction, is a crucial computation that is thought to provide a neural correlate of motion perception. However, directional selectivity has been traditionally quantified by using the full spike train, which does not take into account particular action potential patterns. We investigated how different action potential patterns, namely bursts (i.e. packets of action potentials followed by quiescence) and isolated spikes, contribute to movement direction coding in a mathematical model of midbrain electrosensory neurons. We found that bursts and isolated spikes could be selectively elicited when the same object moved in opposite directions. In particular, it was possible to find parameter values for which our model neuron did not display directional selectivity when the full spike train was considered but displayed strong directional selectivity when bursts or isolated spikes were instead considered. Further analysis of our model revealed that an intrinsic burst mechanism based on subthreshold T-type calcium channels was not required to observe parameter regimes for which bursts and isolated spikes code for opposite movement directions. However, this burst mechanism enhanced the range of parameter values for which such regimes were observed. Experimental recordings from midbrain neurons confirmed our modeling prediction that bursts and isolated spikes can indeed code for opposite movement directions. Finally, we quantified the performance of a plausible neural circuit and found that it could respond more or less selectively to isolated spikes for a wide range of parameter values when compared with an interspike interval threshold. Our results thus show for the first time that different action potential patterns can differentially encode movement and that traditional measures of directional selectivity need to be revised in such cases.     

Spike bursts generated by the thermosensitive (cold) neuron from the antennal campaniform sensilla of the ground beetle Platynus assimilis

Responses of the antennal thermosensitive neuron of the ground beetle Platynus assimilis to warming from 20 to 50 °C were measured and analysed. During warming, neurons switched from regular spiking to bursting. ISI analysis showed that the number of spikes in the burst and spike frequency within the burst were temperature dependent and may precisely encode unfavourably or dangerously high temperatures in a graded manner. In contrast, regular spikes of the neuron encode moderate temperatures at 20-30 °C. The threshold temperature of spike bursting varied in different neurons from 25 to 47 °C. As a result, the number of bursting neurons increased with temperature increase. Therefore, in addition to the burst characteristics, the total number of bursting neurons may also contain useful information on external temperature. A relationship between the spike bursts and locomotor activity of the beetles was found which may have importance in behavioural thermoregulation of the species. At 44.4 ± 0.6 °C, first indications of partial paralysis (of the hind legs) were observed. We emphasize, that in contrast to various sensory systems studied, the thermoreceptor neuron of P. assimilis has a stable and continuous burst train, no temporal information is encoded in the timing of the bursts.     

Symmetry between the visual B- and D-systems and equivalence of center and surround: Studies of light increment and decrement in retinal and geniculate neurons of the cat

The dual center surround organization of retinal and geniculate neurons in two antagonistic subsystems B and D, having on-center or off-center receptive fields and signalling brightness or darkness respectively, has been studied by local light increments and decrements. Intensity response functions obtained by the introduction and withdrawal of small center spots either brighter or darker than a common homogeneous field are similar in a given neuron, but the phasic responses are stronger in on-center neurons than in off-center neurons. Center size increments and decrements, however, lead to equal excitations in the B- and D-system, respectively, provided that both luminance steps start from the same level and are of equal size on a linear scale. Decrementing and incrementing the surrounding luminance of the same optimal center spots lead to equal surround responses in the two subsystems if the two luminance steps terminate at the same level. This lateral activation is elicited by light decrement in the B-system and by light increment in the D-system. Center and surround responses within a given subsystem are of comparable amplitude, but generally slightly stronger responses are elicited by optimal center increments (decrements) than by the equivalent surround decrements (increments) which lead to the same spatial contrast for B-(D-) neurons. The symmetry relations between the B- and D-system and the equivalence relations between center and surround in each subsystem hold for retinal and geniculate neurons. The difference between center and surround response latencies is about 9 ms in both subsystems at the retinal and 14 ms at the geniculate level. Stimulus response functions of on- and off-center neurons are unified on the basis of linear relative luminance scales.     

T-type Ca2+ channels and absence epilepsy

Burst firing of the thalamic neurons is driven by the low threshold Ca2+ spike generated by Ca2+ influx through T-type Ca2+ channels when these channels are activated by membrane hyperpolarization due to inhibitory inputs. The major inhibitory inputs to the thalamocortical (TC) neurons are from the GABAergic neurons in the thalamic reticular nucleus. Thalamic burst firings have long been implicated in the pathogenesis of absence epilepsy. The recent progress in genetic approaches has provided with an opportunity to examine this issue at the level of an organism. In this review I describe results primarily obtained from the analysis of the mice deficient for the alpha1G locus which is the predominant gene underlying the low threshold Ca2+ currents in the TC neurons. Current results so far demonstrate the essential role of the thalamocortical bursts in certain forms of absence seizures. Understanding of the pathophysiological mechanisms of absence epilepsy may help develop drugs to control the disease.     

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model

Synaptic information efficacy (SIE) is a statistical measure to quantify the efficacy of a synapse. It measures how much information is gained, on the average, about the output spike train of a postsynaptic neuron if the input spike train is known. It is a particularly appropriate measure for assessing the input–output relationship of neurons receiving dynamic stimuli. Here, we compare the SIE of simulated synaptic inputs measured experimentally in layer 5 cortical pyramidal neurons in vitro with the SIE computed from a minimal model constructed to fit the recorded data. We show that even with a simple model that is far from perfect in predicting the precise timing of the output spikes of the real neuron, the SIE can still be accurately predicted. This arises from the ability of the model to predict output spikes influenced by the input more accurately than those driven by the background current. This indicates that in this context, some spikes may be more important than others. Lastly we demonstrate another aspect where using mutual information could be beneficial in evaluating the quality of a model, by measuring the mutual information between the model’s output and the neuron’s output. The SIE, thus, could be a useful tool for assessing the quality of models of single neurons in preserving input–output relationship, a property that becomes crucial when we start connecting these reduced models to construct complex realistic neuronal networks.     

On the complex dynamics of intracellular ganglion cell light responses in the cat retina

We recorded intracellular responses from cat retinal ganglion cells to sinusoidal flickering lights, and compared the response dynamics with a theoretical model based on coupled nonlinear oscillators. Flicker responses for several different spot sizes were separated in a smooth generator (G) potential and corresponding spike trains. We have previously shown that the G-potential reveals complex, stimulus-dependent, oscillatory behavior in response to sinusoidally flickering lights. Such behavior could be simulated by a modified van der Pol oscillator. In this paper, we extend the model to account for spike generation as well, by including extended Hodgkin-Huxley equations describing local membrane properties. We quantified spike responses by several parameters describing the mean and standard deviation of spike burst duration, timing (phase shift) of bursts, and the number of spikes in a burst. The dependence of these response parameters on stimulus frequency and spot size could be reproduced in great detail by coupling the van der Pol oscillator and Hodgkin-Huxley equations. The model mimics many experimentally observed response patterns, including non-phase-locked irregular oscillations. Our findings suggest that the information in the ganglion cell spike train reflects both intraretinal processing, simulated by the van der Pol oscillator, and local membrane properties described by Hodgkin-Huxley equations. The interplay between these complex processes can be simulated by changing the coupling coefficients between the two oscillators. Our simulations therefore show that irregularities in spike trains, which normally are considered to be noise, may be interpreted as complex oscillations that might carry information.To the memory of Prof. Otto-Joachim Grusser     

Performance limitations of relay neurons

RELAY CELLS ARE PREVALENT THROUGHOUT SENSORY SYSTEMS AND RECEIVE TWO TYPES OF INPUTS: driving and modulating. The driving input contains receptive field properties that must be transmitted while the modulating input alters the specifics of transmission. For example, the visual thalamus contains relay neurons that receive driving inputs from the retina that encode a visual image, and modulating inputs from reticular activating system and layer 6 of visual cortex that control what aspects of the image will be relayed back to visual cortex for perception. What gets relayed depends on several factors such as attentional demands and a subject's goals. In this paper, we analyze a biophysical based model of a relay cell and use systems theoretic tools to construct analytic bounds on how well the cell transmits a driving input as a function of the neuron's electrophysiological properties, the modulating input, and the driving signal parameters. We assume that the modulating input belongs to a class of sinusoidal signals and that the driving input is an irregular train of pulses with inter-pulse intervals obeying an exponential distribution. Our analysis applies to any [Formula: see text] order model as long as the neuron does not spike without a driving input pulse and exhibits a refractory period. Our bounds on relay reliability contain performance obtained through simulation of a second and third order model, and suggest, for instance, that if the frequency of the modulating input increases or the DC offset decreases, then relay increases. Our analysis also shows, for the first time, how the biophysical properties of the neuron (e.g. ion channel dynamics) define the oscillatory patterns needed in the modulating input for appropriately timed relay of sensory information. In our discussion, we describe how our bounds predict experimentally observed neural activity in the basal ganglia in (i) health, (ii) in Parkinson's disease (PD), and (iii) in PD during therapeutic deep brain stimulation. Our bounds also predict different rhythms that emerge in the lateral geniculate nucleus in the thalamus during different attentional states.     

Unitary IPSPs drive precise thalamic spiking in a circuit required for learning

Song learning in birds requires a basal ganglia-thalamo-pallial loop that contains a calyceal GABAergic synapse in the thalamus. Information processing within this circuit is critical for proper song development; however, it is unclear whether activation of the inhibitory output of the basal ganglia structure Area X can drive sustained activity in its thalamic target, the medial portion of the dorsolateral thalamic nucleus (DLM). We show that high-frequency, random activation of this GABAergic synapse can drive precisely timed firing in DLM neurons in brain slices in the absence of excitatory input. Complex IPSP trains, including spike trains recorded in vivo, drive spiking in slices with high reproducibility, even between animals. Using a simple model, we can predict much of DLM's response to natural stimulus trains. These data elucidate basic rules by which thalamic relay neurons translate IPSPs into suprathreshold output and demonstrate extrathalamic GABAergic activation of thalamus.