The control of spiking by synaptic input in striatal and pallidal neurons

The control of spiking by synaptic input in striatal and pallidal neurons Dieter Jaeger Department of Biology, Emory University, Atlanta, GA 30322 Key words: Abstract: rat, slice, whole cell, dynamic current clamp, striatum, globus pallidus Different patterns of simulated synaptic input were applied to striatal and pallidal neurones in vitro using dynamic current clamping. It was found that striatal neurones required a much larger baseline of excitatory inputs than pallidal neurones to allow spiking. Spike rates in pallidal neurones in response to applied synaptic conductances were much higher even when inhibitory input conductances dominated. Repeated applications of inputs with defined short-term correlations demonstrated that the timing of individual spikes can be controlled within 2 ms by specific input patterns both in striatum and GP. The presence of synchronisation in the input led to much increased spike rates even when the mean rate of input was not changed. These results indicate specific modes of synaptic integration in striatal and GP neurones which depend on particular intrinsic voltage-gated conductances. An important role of these mechanisms in network function of the basal ganglia seems likely. 1. INTRODUCTION Every second in a behaving animal, each striatal medium spiny neurone receives thousands of synaptic inputs from cerebral cortex, thalamus, and local interneurons. Neurones in the globus pallidus, in turn, receive a similarly high frequency of inputs from striatum and subthalamic nucleus. The main pathway of information flow is generally believed to travel from cerebral cortex to striatum and on to globus pallidus (Albin et al., 1995). This pathway is excitatory at corticostriatal synapses and inhibitory at striatopallidal synapses. The main question underlying the present work is 1

2 Dieter Jaeger how output spike trains of single neurones in this pathway may be controlled by thousands of synaptic inputs per second. More specifically, our goal was to examine how the temporal precision of individual spikes and the spike rate over longer time intervals can be described as a function of the temporal pattern of excitatory and inhibitory input conductances. To analyse the control of spiking by specific synaptic input patterns, we used the technique of dynamic current clamping in vitro (Sharp et al., 1993). This technique allowed us to apply computer generated conductance patterns to neurones while recording their membrane potential and spike pattern. We recorded from striatal and pallidal neurones to test the hypothesis that the intrinsic properties of neurones in these structures are specialised to allow different modes of spike control by synaptic inputs. 2. METHODS Frontal or sagittal 300 µm thick brain slices were prepared from 15-35 day old male Sprague-Dawley rats. Animals were perfused with a sucrose- Ringer solution under deep anaesthesia before the brain was removed. Whole cell recordings were obtained at 32 C under visual guidance using a 63x water immersion lens. Electrode impedances ranged from 6 to 12 MΩ. The intracellular solution contained (in mm): K-Gluconate 140; NaCl 10; HEPES 10; MgATP 4; NaGTP 0.4; EGTA 0.2; Spermine 0.05. The extracellular solution contained (in mm): NaCl 124; KCl 3; KH 2 PO4 1.2; MgSO 4 1.9; NaHCO 3 26; CaCl 2 2; glucose 20. Endogenous synaptic input in the slices was blocked by adding 40 µm picrotoxin, 10 µm CNQX, and 200 µm AP-5. To add artificial synaptic inputs via the recording electrode, a computer used stored synaptic conductance patterns to calculate the current to be injected on-line at a 10 KHz refresh rate. The equation for the injected synaptic current is: I syn = g syn (V m V rev ), where g is conductance and V rev is the synaptic reversal potential. The reversal potentials for excitation and inhibition were set to 0 and 70 mv, respectively. Each excitatory input induced a conductance with a 0.5 ms rise time and a 1.2 ms decay time constant. The rise and decay time constants for inhibitory inputs were 0.93 and 20 ms, respectively. These values were chosen to match the time course of generic AMPA-type excitatory and GABA A type inhibitory inputs. Currents resulting from many individual EPSCs and IPSCs were added so that the total synaptic input pattern a neurone may receive in vivo is injected via the recording electrode. For details of this method please see (Jaeger and Bower, 1999).

The control of spiking by synaptic input in striatal and pallidal 3 neurons 3. RESULTS To identify the type of recorded neurones, positive and negative current pulses were injected. As shown in previous studies (Kita et. al 1984; Nisenbaum and Wilson, 1995), striatal medium spiny neurones expressed a hyperpolarized resting potential of 70 to 80 mv, and a strong rectification upon injection of hyperpolarizing current (Fig. 1). With depolarising current injection they showed regular spiking. The latency to the first spike was long when the current amplitude was low, which is due to a slowly inactivating K + current (Nisenbaum et al., 1994). Pallidal neurones in contrast showed a more depolarised resting potential around 60 mv, a sag in the response to negative current injection, and fast regular spiking even with low amplitudes of injected positive current (Fig. 1). These properties have been found to identify pallidal projection neurones (Kita and Kitai, 1991). The different response properties to current injection between striatal medium spiny neurones and pallidal projection neurones point out that they express different sets of voltage-gated membrane currents. These currents are gated by voltage changes in the range caused by synaptic input and their (de)activation or (de)inactivation will thus influence the net voltage response to synaptic input. The presence of different such currents in striatal and GP neurones makes it likely that they will respond differently to identical patterns of synaptic input. ST GP 10 mv 10 mv -75 mv -60 mv 200 ms 0 na 0.1 na 0 na 200 ms 0.1 na Figure 1. Current injection pulses applied to medium spiny neurones in striatum (ST) and to neurones in globus pallidus (GP) To apply artificial synaptic conductance patterns with dynamic clamping we constructed a stimulus, which contained the activity of 100 excitatory and 100 inhibitory simulated synapses. Groups of 10 excitatory and 10 inhibitory synapses were coupled to result always in the synchronous

4 Dieter Jaeger activation of 10 synapses (see below for the significance of correlated inputs). Each of the synaptic groups was activated randomly with a mean rate of 80 Hz for excitatory and 30 Hz for inhibitory inputs. This stimulus contained on average 8000 excitatory and 3000 inhibitory inputs per second. Figure 2A shows a 2 s segment of the summed input conductances and Figure 2B illustrates a typical voltage response for striatal and pallidal neurones. The application of ongoing random excitatory and inhibitory input immediately induced an irregular spiking pattern in the recorded neurones, which is otherwise not observed in vitro. The total input conductance levels required to induce realistic spiking in striatum was quite different from that required in GP. Although the same pattern of synaptic input was used for both structures, the amplitude of conductance had to be scaled by different gain factors to induce realistic spike patterns. Medium spiny neurones, though smaller in size than GP neurones, required more inward synaptic current to trigger spiking. GP neurones were found to spike at a higher rate than medium spiny neurones even in the presence of a much lower level of excitatory inputs (Fig. 2A,B). When the same synaptic input pattern was presented to a single neurone several times, we found that a majority of spikes were timed precisely within 2 ms for repeated stimulus presentations (Fig. 2C). This precise control of spike timing via synaptic conductances allows in principle that information in basal ganglia circuits is contained in the absolute timing of individual spikes. To examine how synaptic conductance changes are related to the time of spiking we constructed spike-triggered averages of synaptic current and of excitatory and inhibitory synaptic conductances (Fig. 2D). We found that on average spikes in striatal and GP neurones were preceded by an increase in excitatory input conductance as well as a decrease in inhibitory conductance. Excitation was acting within a shorter period before each spike than inhibition, however, indicating that inhibitory inputs act with a slower time course than excitatory inputs in controlling spike timing. Striatal neurones showed a much higher peak in excitation preceding spike initiation than GP neurones, indicating that a synchronous burst of excitation is a much more important mechanism in inducing a spike in a striatal than a GP neurone. This result is in good agreement with previous evidence that striatal neurones spike preferentially in response to coincident excitatory input from many cortical neurones (Wickens and Wilson, 1998). In contrast, GP neurones are much more sensitive to small excitatory signals, and can be active in the presence of a large inhibitory baseline. These results suggest that the intrinsic response properties of these two types of neurones may be responsible to a large degree for the different spike rates observed in vivo (Jaeger et al., 1994).

The control of spiking by synaptic input in striatal and pallidal 5 neurons A ST GP Synaptic conductances AMPA GABA 5 ns 0 ns 1s 10 mv 2.5 ns B Membrane potential -60 mv C Spike rasters D Spike-triggered averages g AMPA g GABA g GABA I inj. 0 ns 0 na 10 ms 0.1 na 1 ns g AMPA I inj. inward Figure 2. Dynamic current clamping of striatal and GP neurones. A. The black and grey lines represent the summed conductance traces of 100 GABA and 100 AMPA synapses, respectively. The amplitude of excitatory inputs was scaled down to 40% of the striatal input for GP stimulation, and the inhibitory input was scaled up by 20%. C. The rows of black squares denote spike times for subsequent stimulus presentations. D. Spike triggered synaptic conductance and injected current. The positive spike in injected current reflects the sharp change in driving force for synaptic input during an action potential. To examine how correlations in the activity of many inputs affect spiking we constructed a low-correlation and a high-correlation input pattern. The high-correlation pattern consisted of 10 groups of 10 excitatory and

6 Dieter Jaeger inhibitory inputs as described above. The low-correlation input consisted of 100 individually activated excitatory and 100 inhibitory inputs. The resulting conductance traces for the low-correlation pattern had the same mean amplitude as those resulting from high-correlation inputs, but much lower amplitude fluctuations around the mean. Neurones in striatum rarely showed any spiking in response to low-correlation inputs (Fig. 3). Pallidal neurones did spike in response to these inputs, but their spiking frequency was much increased in response to high-correlation inputs (Fig. 3). This finding substantiates the observation that striatal neurones preferentially respond to synchronous excitatory inputs (Wickens and Wilson, 1998). This dependence of spiking on packets of inputs allows for population coding schemes that use the co-ordinated activation of neural assemblies to contain specific information (Aertsen et al., 1996). ST high input correlation low input correlation GP 20 mv Vm 20 mv -60-50 500 ms Figure 3. The effect of input correlation on spiking. Voltage traces (Vm) for low-correlation and high-correlation inputs are superimposed. Note that subthreshold high-frequency fluctuations are more pronounced in the high-correlation condition. The results shown above demonstrate that the temporal structure of spike trains in striatum and in GP can be precisely controlled by synaptic conductances and that correlated packets of inputs are highly effective in controlling spiking. These findings do not deny the presence of rate coding in a traditional sense, i.e. the increase of spike frequency with an increase in the mean excitatory drive. To examine how the presence of correlation and a change of mean drive may interact in the control of spiking we constructed two stimuli that both had the same high correlation in inputs, but one stimulus had an additional constant component in excitatory conductance that equalled 80% of the dynamic component. The addition of constant excitatory conductance did result in an increase of spike frequency from 2 to 8 Hz for the striatal and 10 to 17 Hz for the GP neurone shown in Figure 4. Very similar increases were seen in other neurones recorded with these stimuli. Interestingly, the precise timing of most of the spikes already present before the 80% addition of excitatory conductance was left intact

The control of spiking by synaptic input in striatal and pallidal 7 neurons (spikes noted by asterisks in Fig. 4). Instead of creating a completely new spike pattern, the addition of constant excitatory conductance inserted new spikes in the existing spike train. Therefore, the information transmitted by specific pulses of synchronous inputs can be maintained with different mean rates of spiking. The additional excitatory drive tended to push smaller synchronous events above the threshold needed to trigger a spike. It is important to note that the mean excitatory drive is given by the combined total level of inhibition and excitation. In this situation the modulation of inhibition can be seen as both changing the mean excitatory drive and thus the mean spike rate, as well as setting the threshold for the amplitude of excitatory input packets required to induce a spike. ST GP 20 mv 20 mv 500 ms Figure 4. The effect of added constant excitatory conductance. Responses with added conductance are superimposed on the baseline response and drawn in grey. 4. DISCUSSION An ongoing barrage of excitatory and inhibitory inputs leads to a considerable baseline of synaptic conductances. These conductances define a combined reversal potential, which sets the excitatory drive exerted on the cell. Cells that require a large depolarising current to reach spike threshold such as striatal medium spiny neurones are tuned to respond to input conditions with a large amount of excitation. In contrast, cells that require little or no depolarising input current to trigger spikes such as pallidal projection neurones are tuned to input conditions in which inhibition dominates. The rate in increase of spike rate resulting from increasing excitatory drive is further dependent on intrinsic cell properties such as

8 Dieter Jaeger depolarisation-activated K + currents (Jaeger et al., 1997). Pallidal projection neurones show much higher spike rates in vivo than striatal medium spiny neurones, even though they presumably receive far greater amounts of inhibition. Our results indicate that the intrinsic properties of these neurones are well suited to account for these different spike rates. Beyond the different requirement of pallidal and striatal neurones in the relative mean level of excitatory and input conductances, our results indicate that the correlation between synaptic inputs is important. Medium spiny neurones were particularly tuned to respond to synchronous pulses of many excitatory inputs on top of an already substantial baseline of excitation. This finding with artificial synaptic input in vitro is in good agreement with the subthreshold membrane trajectory and spike pattern found in vivo. Intracellular recordings of medium spiny neurones in vivo indicate the existence of a characteristic up-state, which is produced by a large baseline of excitatory input (Wilson and Groves, 1981; Wilson and Kawaguchi, 1996). Spikes are triggered by additional depolarising pulses above the upstate baseline (Stern et al., 1998; Wickens and Wilson, 1998). The present data indicate that the timing of individual spikes in medium spiny neurones may be controlled with a 2 ms accuracy by realistic synaptic inputs. Inhibitory inputs made a significant contribution to the control of spike rate as well as spike timing. This observation suggests that the activation pattern of fast spiking inhibitory interneurons in striatum may be of great significance in shaping spike responses of medium spiny neurones to cortical input. Inhibition via collaterals from other medium spiny neurones is less likely to be involved, since such inhibition has been found to be weak or absent (Jaeger et al., 1994). Neurones in GP could also spike with a 2 ms accuracy in response to excitatory and inhibitory inputs, but a baseline of excitation was not required for the induction of spiking. The spike rate of GP neurones increased dramatically with small increases in excitation or decreases in inhibition, while individual spikes could still be controlled accurately by specific sets of correlated inputs. In addition, the presence of short-term correlation in the input pattern also led to a pronounced increase in the overall spike rate. Inhibitory and excitatory inputs were in general of equal importance in controlling GP spiking. The method of dynamic current clamping in vitro can reveal how single neurones respond to specific patterns of simulated synaptic inputs. Which of the demonstrated features of controlling spiking are ultimately used to transmit information in basal ganglia circuits remains an exciting area of future research.

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