Distinct Patterns of Striatal Medium Spiny Neuron Activity during the Natural Sleep–Wake Cycle

Electrophysiological properties of MSNs in the anesthetic-free rat

Stable intracellular recordings, of duration 2–34 min, were obtained from 15 striatal neurons located in the projection field of the sensorimotor cortex. Recorded cells displayed a relatively low input resistance (31.9 ± 11.3 MΩ; n = 13 cells) (Fig. 1 A,B,D), a short time constant (7.0 ± 3.3 ms; n = 12 cells), and hyperpolarized membrane potentials regardless of the state of vigilance (extreme value of membrane polarization, −81.7 ± 7.4 mV; range, −74.0 to −100.8 mV; n = 15 cells) (Figs. 1 A,C, 2 A, 3 A,B, 4 A, 7 A). Striatal neurons exhibited, in response to threshold positive current pulses applied from rest, a slow developing ramp depolarization leading to a long-latency spike discharge (Fig. 1 A). Negative current pulses of increasing intensity revealed a pronounced inward rectification evidenced by the damping of the current-induced voltage deflections (Fig. 1 B). Action potentials had an amplitude of 56.5 ± 5.0 mV and a voltage threshold of −51.9 ± 2.3 mV (n = 15 cells). These electrophysiological properties of striatal neurons recorded during the natural sleep–wake cycle are consistent with those previously reported for MSNs recorded in vitro (Nisenbaum et al., 1994; Nisenbaum and Wilson, 1995) and from anesthetized in vivo preparations (Wilson and Kawaguchi, 1996, Wickens and Wilson, 1998; Mahon et al., 2001, 2003b).

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Figure 1.

Electrophysiological properties of MSNs in the anesthetic-free rat. A , Voltage responses of a striatal neuron (top traces) to intracellular injection of positive (single trace) and negative (average response; n = 8) current pulses. Note the slow ramp depolarization (arrow) from −60 mV and the low input resistance (R _in = 22 MΩ). B , Plot of the mean (n = 12) membrane potential change (ΔV) in response to negative current pulses (I _inj.) of increasing intensity. A strong inward rectification was evident for current pulses less than −0.4 nA. C–E , Stability of electrical membrane parameters during active waking. C , Simultaneous recordings, at different times after cell impalement, of neck EMG, cortical surface EEG, and striatal intracellular activity (bottom) in response to repetitive injection (2 Hz) of negative current pulses (0.4 nA). The fast and low amplitude EEG waves together with the phasic increases in muscular tone are characteristic of an active waking. The minimal value of spontaneous membrane potential (dashed line) remained constant throughout the recording session. D , Average (n > 33) voltage responses to current pulses of −0.4 nA obtained at the indicated times after cell impalement. E , DC superimposition of three spontaneous action potentials (top traces) recorded at different times (same color code as in D ) and the corresponding first-order derivative, showing the stability of spike amplitude, duration, and threshold. A and B–E are from two different neurons.

In six MSNs, intracellular records permitted direct comparison of membrane potential fluctuations in the same cell during the sleep–wake cycle. In the remaining cells, recordings were obtained either during wakefulness (n = 6 cells) or SWS (n = 3 cells). Figure 1 C–E shows that stable intracellular recordings could be obtained even during active waking associated with phasic increases in neck muscular tone attributable to postural adjustments. Only cells exhibiting stable membrane potential (Fig. 1 C), input resistance (Fig. 1 C,D), spike waveform, and threshold (Fig. 1 E) throughout the recording session were included in the database.

Intracellular activity of MSNs during wakefulness and drowsiness

Wakefulness was identified by a low-amplitude desynchronized EEG associated with a sustained EMG activity (Timo-Iaria et al., 1970; Gottesmann, 1992) (Fig. 2 A, top). Spectral analysis performed on continuous (10–400 s) EEG records showed a dominant frequency ∼3–5 Hz associated with faster waves, up to 50 Hz (Fig. 2 B, top). The corresponding intracellular striatal activity (recording duration, 8.6 ± 10.0 min; n = 9 cells) was characterized by sporadic depolarizing envelopes, of variable amplitude and duration, on which were superimposed high-frequency small amplitude noise-like fluctuations (Fig. 2 A, top). Eight MSNs fired spontaneously, whereas one cell remained silent for the whole duration of the epoch of wakefulness. This quiescent cell did not show any distinctive electrical membrane properties, and firing could be initiated by current injection (supplemental Fig. 1, available at www.jneurosci.org as supplemental material). This suggests that, as previously reported (Sandstrom and Rebec, 2003), part of the MSN population may be silent in the awake animal. Action potentials were triggered on membrane potential fluctuations (Fig. 2 A, top) with a mean firing rate of 3.3 ± 3.5 Hz (n = 8 cells; range, 0.1–10.8 Hz). ISIs were measured in MSNs whose mean firing rate exceeded 1 Hz. In the typical example shown in Figure 2, the mean ISI was 515.6 ± 624.4 ms with a corresponding coefficient of variation (CV) of 1.21. Across cells, CVs ranged from 1.21 to 1.72 (mean, 1.45 ± 0.21; n = 5 cells). As shown by the autocorrelogram of spike discharge (Fig. 2 A, bottom), the waking state was not associated with any apparent recurrent firing pattern. Consistently, MSNs exhibited positively skewed ISI distribution and the sequential ISI analysis did not reveal any particular internal structure (see Fig. 5 A).

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Figure 2.

Membrane potential fluctuations in MSNs during wakefulness. A , Top, Striatal neuron intracellularly recorded (bottom trace) together with the corresponding EEG and EMG during wakefulness. Note the stability of intracellular recording despite the phasic increases in muscular tone. Bottom, Autocorrelogram of spike discharge for the recording period shown above (bin size, 0.2 s). B , Top, the spectral analysis of the EEG period shown in A , together with the high-amplitude EMG activity, is indicative of wakefulness. Bottom, Cross-correlation between the EEG and intracellular activities shown in A demonstrating the absence of correlation between both signals. C , The membrane potential distribution (bin size, 0.5 mV) calculated from the 20 s recording depicted in A was unimodal with a mean value of −63.5 ± 5.2 mV.

Because cortical inputs provide the main excitatory synaptic drive to MSNs (Wilson, 1993, 1995; Kincaid et al., 1998), we performed cross-correlations of simultaneously recorded cortical EEG and intracellular striatal depolarizations to test for temporal correlations between the two activities. During the waking state, there was no significant peak in the cross-correlograms (Fig. 2 B, bottom), indicating a lack of temporal correlation between the desynchronized EEG and the corresponding intracellular striatal activity. This is consistent with previous findings from anesthetized animals during epochs of cortical desynchronization (Mahon et al., 2001; Kasanetz et al., 2002).

The membrane potential of MSNs during wakefulness, computed from long periods (10–60 s) of continuous recording, typically followed a unimodal, Gaussian-like (r ² > 0.9; n = 9 cells) distribution (Fig. 2 C). The corresponding average membrane potential was −69.4 ± 9.6 mV (range, −87.4 to −58.0 mV; n = 9 cells) with an extreme value of membrane polarization ranging from −94.5 to −74.0 mV (−81.6 ± 7.0 mV; n = 9 cells).

Waking state was occasionally followed by short, 10–20 s, periods of drowsiness (n = 5 periods from 5 cells) (Fig. 3 A,B), which usually preceded the appearance of SWS. Drowsiness was characterized by an attenuation of EMG activity and an increase of the EEG signal power in the low frequency (2–7 Hz) band but without clear dominant frequency (Fig. 3 C, bottom). As shown in Figure 3 D, the transition from wakefulness to drowsiness was associated with a change from a unimodal membrane potential distribution (−62.5 ± 4.1 mV) (Fig. 3 D, top) to a broad (−59.0 ± 7.1 mV), multipeak distribution (Fig. 3 D, bottom).

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Figure 3.

Alteration of MSN membrane potential distribution during drowsiness. A , B , Simultaneous recordings of intracellular striatal activity, EEG and EMG (top traces) during periods of wakefulness ( A ) and drowsiness ( B ). Calibration in B applies to A . C , D , Corresponding power spectra of the EEG ( C ) and membrane potential distributions ( D ). Note the change from a unimodal membrane potential distribution (single Gaussian fit, r ² = 0.97) into a multipeak distribution associated with the transition from wakefulness to drowsiness.

State transitions in MSNs during SWS

SWS periods were characterized by high-amplitude low-frequency EEG waves occasionally intermingled with spindles and a mild muscle activity without phasic contractions (Timo-Iaria et al., 1970; Gottesmann, 1992) (Fig. 4 A, top). EEG spectral analysis indicated a high-power dominant frequency in the 1–3 Hz range associated with reduced power at frequencies >10 Hz (Fig. 4 B, top). Most of the MSNs (n = 7 of 8) recorded during SWS showed periodic transitions in membrane potential between a hyperpolarized and a depolarized state. Transitions to the depolarized state were relatively abrupt, whereas returns to the hyperpolarized state were slower and usually exhibited an exponential-like decay (Fig. 4 A, middle). Depolarized states lasted from 0.1 to 3.0 s and could generate single spikes or a cluster of 2–16 action potentials (Fig. 4 A, top). The mean firing rate in spontaneously active MSNs showed a high cell-to-cell variability (mean, 6.1 ± 7.2 Hz; range, 0.6–16.1 Hz; n = 6 of 8 cells). The mean ISI for the cell illustrated in Figure 4 A was 287.3 ± 512.9 ms (CV, 1.78). Among the four computed cells, CVs ranged from 1.66 to 2.73 (mean, 2.0 ± 0.49). Although there was no significant difference (p > 0.3) between mean firing rates associated with SWS and wakefulness, MSNs displayed during SWS a distinct firing pattern (Fig. 4 A). Consistent with the rhythmic transitions between the quiescent hyperpolarized and suprathreshold depolarized states accompanying SWS, MSNs exhibited a rhythmic brisk firing pattern (Fig. 4 A, bottom) that was reflected in the bimodal distribution of ISIs as well as in the sequential ISI analysis (Fig. 5 B). In contrast to wakefulness, cross-correlation during SWS revealed a strong temporal coherence between intracellular and EEG activity (Fig. 4 B, bottom) at frequencies (∼1 Hz) close to those of the cortical EEG rhythm (Fig. 4 B, top).

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Figure 4.

State transitions in MSNs during SWS. A , Top, Intracellular activity of a striatal neuron (bottom trace) during a period of SWS, together with the corresponding EEG and EMG waves. Middle, DC superimposition (n = 9; left) of truncated records showing spontaneous transitions to the depolarized (red) and hyperpolarized (black) states. Expanded view (right) of two typical examples. Bottom, Autocorrelogram of spike discharge for the intracellular recording shown above (bin size, 0.2 s) illustrating the rhythmic firing pattern of MSNs during SWS. The central peak was truncated for clarity. B , Top, Power spectrum of the EEG record shown in A . Bottom, EEG and intracellular activities exhibited a strong periodic correlation consistent with the optimal frequency of the EEG record (∼1–2 Hz). C , Histogram of membrane potential distribution (bin size, 0.5 mV) calculated for a 46 s recording period including the recording illustrated in A . The histogram could be fitted by a double Gaussian (r ² = 0.97). Inset, In the same cell, the membrane potential fluctuations during a period of wakefulness exhibited a unimodal (bin size, 0.5 mV) distribution around a mean value of −61.6 ± 4.6 mV.

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Figure 5.

Comparison of firing patterns associated with wakefulness and SWS. A , Top, Averaged histogram (n = 5 cells) of the log ISI distribution associated with wakefulness (bin width, 0.1 log ms). Bottom, corresponding sequential analysis of the log ISIs showing the lack of serial dependence between successive intervals. B , Top, Averaged histogram (n = 4 cells) of the log ISIs associated with SWS (bin width, 0.1 log ms). Note the bimodal distribution of the ISIs. Bottom, Corresponding sequential analysis of the log ISIs. Note that firing within depolarized states was represented by a cluster of short intervals that were usually followed and preceded by longer ISIs, which correspond to the duration of hyperpolarized states.

The two states of MSNs polarization during SWS were reflected in the bimodal distributions of membrane potential, computed from continuous records of 10–300 s, and fitted by pairs of Gaussian curves (r ² > 0.95; n = 6 cells) (Fig. 4 C). Average membrane potentials reached during the depolarized and hyperpolarized states were −65.4 ± 5.8 mV (range, −74.5 to −59.4 mV; n = 6 cells) and −73.9 ± 3.5 mV (range, −78.5 to −69.9 mV; n = 6 cells), respectively (Fig. 4 C), corresponding to a voltage difference between the two states of 8.5 ± 3.7 mV. The profile of synaptic activity associated with the two-peak membrane potential distribution was similar to the up and down states classically described in MSNs under urethane and/or ketamine–xylazine anesthesia (Wilson, 1993; Wilson and Kawaguchi, 1996; Stern et al., 1997, 1998; Wickens and Wilson, 1998; Mahon et al., 2001; Tseng et al., 2001). In one cell, with a clearly state-dependent activity, the histogram of membrane potential followed a trimodal distribution with regularly spaced peaks at −72.0, −64.6, and −57.8 mV.

The highest level of membrane polarization during SWS (−83.1 ± 7.8 mV; range, −100.8 to −75.5 mV) as well as the mean value of membrane potential (−70.8 ± 7.4 mV) were similar (p > 0.5 for both parameters) to those measured during the waking state. However, the membrane potential variance during SWS was significantly greater (p < 0.05) than that associated with periods of wakefulness. Figure 4 C shows, for one cell, the conversion of a sleep-associated bimodal membrane potential histogram (Fig. 4 C) into a unimodal distribution during wakefulness (Fig. 4 C, inset). Similar changes occurred in all neurons recorded during both SWS and wakefulness, suggesting that different patterns of spontaneous activity were not cell specific but rather reflected state-dependent changes throughout the MSN population.

Frequency analysis of background synaptic activity

We further characterized the temporal properties of MSN membrane potential fluctuations, presumably of synaptic origin (see Discussion), by performing time–frequency analysis of continuous records from SWS and wakefulness periods. Time–frequency analysis of the intracellular signals, after removing action potentials (Fig. 6 A,B), revealed during both states of vigilance the presence of a mixture of slow and high-frequency components (Fig. 6 B,C). During SWS, transitions to suprathreshold depolarized states were correlated with a transient increase in power over a wide frequency band (up to 200 Hz) (Fig. 6 B). Fast membrane potential fluctuations were also associated with the generation of action potentials during the depolarized state (Fig. 6 A,B). The power of membrane potential fluctuations was much reduced during the quiescent hyperpolarized state (Fig. 6 B,C). In the same manner, random firing during wakefulness was associated with an increase in power at high frequencies (Fig. 6 B), suggesting that in both states of vigilance firing is exceeded by depolarizations resulting from the temporal summation of high-frequency synaptic potentials.

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Figure 6.

Frequency content analysis of striatal membrane potential fluctuations during SWS and wakefulness. A , Superimposition of spontaneous intracellular activity of a striatal neuron before (light blue trace) and after spike removal (black trace) during SWS (left) and wakefulness (right). B , Time–frequency analysis of the down-sampled (1 kHz) intracellular recordings shown in A (same time base). Time–frequency energy density is depicted by color scales for the time–frequency plane. Time–frequency energy density corresponds to the squared modulus of the continuous Gabor Transform computed from 0 to 300 Hz with 3 Hz intervals and a 10 ms Gaussian window. C , Mean Gabor spectrum power density in the 0–300 Hz frequency band for the SWS-associated hyperpolarized and depolarized states. Traces in A correspond to sections of the records shown in Figures 4 A (SWS) and 2A (wakefulness). Quantifications of the frequency content depicted in C were performed on the whole intracellular record shown in Figure 4 A.

Striatal oscillations during paradoxical sleep

Finally, we describe a distinct form of MSN activity associated with paradoxical sleep. Epochs of paradoxical sleep are relatively short and rare in the rat (Souliere et al., 2000). We recorded striatal intracellular activity during two such episodes of duration of 100 and 250 s in two cells from two rats. Paradoxical sleep emerges from SWS and is characterized by the occurrence of relatively large-amplitude EEG waves in the theta range (5–9 Hz) (Timo-Iaria et al., 1970; Gottesmann, 1992) (Fig. 7 A,B), and an almost complete loss of nuchal muscle tone (Fig. 7 A). The two cells showed a remarkably similar behavior. Spontaneous activity consisted of large amplitude (10–30 mV) subthreshold oscillations occurring at 1–2 Hz (Fig. 7 A,C), which produced skewed unimodal membrane potential distributions around a mean of −78.8 ± 4.3 mV (Fig. 7 D) and −84.1 ± 6.1 mV, respectively. The rhythmic oscillations were sculpted by the summation of presumed high-frequency depolarizing synaptic events (Fig. 7 D, inset). Both cells were silent at rest but current injection could induce firing suggesting that the lack of activity did not result from an alteration of intrinsic excitability (Fig. 7 E).

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Figure 7.

Subthreshold rhythmic oscillations in MSNs during paradoxical sleep. A , Simultaneous recordings of EMG, EEG waves and striatal cell intracellular activity. Note the complete muscular atonia. B , Spectral analysis of the EEG record showing a strong periodicity at 7 Hz. C , Autocorrelation function for the striatal activity shown in A indicating a dominant frequency at ∼1 Hz. Note the discrepancy between the striatal and cortical rhythms. D , The unimodal membrane potential distribution (bin size, 0.5 mV) was skewed toward depolarized potentials. Inset, Expansion of the membrane depolarization as indicated by the asterisk in A . E , Voltage response of the striatal neuron (top trace) to intracellular injection of a suprathtreshold current pulse (bottom trace). Results illustrated in A–E are from the same experiment.

Discussion

Our intracellular recordings from the anesthetic-free rat directly demonstrate that bistable activity is not the only spontaneous behavior generated by MSNs in physiological conditions. Rather, we showed that MSNs can exhibit various patterns of spontaneous synaptic activities providing these neurons with multiple processing capabilities. Our findings suggest that we must adjust our present conceptual frameworks for understanding the role of the striatum during physiological and pathological conditions.

Membrane properties of MSNs in the alert rat

The present study provides the first description of the intrinsic electrophysiological properties of MSNs in the anesthetic-free animal. Regardless of the state of vigilance, MSNs exhibit a highly polarized resting membrane potential, weak input resistance and time constant, and a pronounced inward rectification. Current injection from hyperpolarized potentials induced slow ramp-like depolarization, leading to a delayed spike discharge. These passive and active membrane properties, responsible for a relatively low intrinsic excitability of MSNs, are consistent with those described in vitro (Nisenbaum et al., 1994; Nisenbaum and Wilson, 1995) and in vivo under various types of anesthetics (Wilson and Kawaguchi, 1996; Wickens and Wilson, 1998; Mahon et al., 2001; Reynolds et al., 2001), where they are known to mainly result from voltage-gated potassium conductances (Wilson 1993; Nisenbaum et al. 1994; 1995; Nisenbaum and Wilson, 1995).

In addition to providing a functional identification of recorded neurons, these findings indicate that the anesthetics used in previous in vivo intracellular studies do not strongly alter the intrinsic excitability of MSNs. Moreover, they demonstrate that the distinct profiles of synaptic activity associated with the different stages of the sleep–wake cycle reflect physiological patterns of afferent activity that are lacking or hidden during anesthesia.

Composite intracellular striatal activity during wakefulness

During wakefulness, MSNs experienced continuous, slow and irregular, membrane potential fluctuations intermingled with high-frequency noise-like depolarizing events leading to a random spiking activity. This complex activity was associated with low-voltage fast EEG waves, as observed during waking in various species (Steriade, 2000; Destexhe et al., 2003). Such desynchronized EEG activity is associated in cortical pyramidal neurons with a sustained membrane depolarization that generates tonic firing (Steriade et al., 2001; Timofeev et al., 2001; Destexhe et al., 2003).

Because MSNs are innervated by thousands of corticostriatal neurons with relatively few synaptic contacts per axon (Wilson, 1995; Kincaid et al., 1998), the high-frequency noise-like membrane potential fluctuations probably originate in the uncorrelated firing of many convergent corticostriatal neurons. These small-amplitude background excitatory events triggered action potentials when concomitant with depolarizing envelopes, sculpted by the temporal summation of synaptic potentials resulting from sporadic epochs of relatively coherent cortical activity (Lopes da Silva et al., 1970; Murthy and Fetz, 1992). Because thalamic projection neurons fire tonically during alertness (Steriade, 2000), we cannot exclude that sustained discharges of glutamatergic thalamostriatal neurons (Wilson et al., 1983) contribute to the excitatory synaptic bombardment of striatal cells during wakefulness.

We cannot exclude that the intracellular activity of MSNs in the awake head-restrained animal could slightly differ from that occurring during natural behaviors, in particular in the striatal sector related to a specific sensorimotor task. However, the present findings, showing periods of irregular repetitive discharges during active waking, are consistent with the complex firing pattern observed in freely moving animals (Kimura, 1990; Schultz et al., 2003).

Cellular mechanisms underlying MSN rhythmic depolarizations during SWS and paradoxical sleep

As in other species (Steriade, 2000), SWS was correlated with high-amplitude slow EEG waves, suggesting that a large population of cortical neurons is engaged in a coherent oscillatory activity (Steriade, 2000; Steriade et al., 2001; Timofeev et al., 2001). In the anesthetic-free cat, these slow EEG potentials are correlated with the alternation of up and down states in cortical neurons (Timofeev et al., 2001). Consistently, SWS was distinguished in MSNs by the presence of rhythmic transitions between two preferred membrane potentials, a depolarized state associated with spike discharge and a quiescent hyperpolarized state, resulting in a bimodal membrane potential distribution. Given the strong temporal correlation between the rhythmic cortical field potentials and the intracellular activity of MSNs during SWS, it is very likely that the rhythmic ongoing cortical activity interacts with the active membrane properties of MSNs to determine the membrane potential shifts in striatal neurons. As proposed for the striatal up and down states during anesthesia (Nisenbaum and Wilson, 1995; Wilson, 1995; Wilson and Kawaguchi, 1996), the inwardly rectifying potassium current of MSNs should stabilize the hyperpolarized state near the potassium equilibrium potential and shunt small synaptic depolarizations. The transitions to the depolarized state may result from the temporal summation of high-frequency depolarizing events attributable to the synchronized discharge of excitatory cortical inputs. The relative stability of the membrane potential in the depolarized state may originate from interactions between the powerful depolarizing cortical synaptic drive and the activation near threshold of non-inactivating and slowly inactivating voltage-gated potassium currents (Nisenbaum et al., 1994; Nisenbaum and Wilson, 1995; Wilson and Kawaguchi, 1996).

The spontaneous activity of MSNs during paradoxical sleep was characterized by rhythmic (∼1–2 Hz) subthreshold oscillations sculpted by small-amplitude high-frequency depolarizing events. Given the theta rhythm (5–9 Hz) in the EEG activity during this state of sleep, it seems unlikely that the slow striatal oscillations reflect corticostriatal inputs. The elucidation of the origin of this activity will require records from other source of striatal afferents during paradoxical sleep.

Anesthetic-dependent versus natural activity

Up and down states, observed under urethane and/or ketamine–xylazine anesthesia, are the best described activity of striatal neurons in vivo (Wilson, 1993, 1995; Wilson and Kawaguchi, 1996; Stern et al., 1997, 1998; Wickens and Wilson, 1998; Goto and O'Donnell, 2001; Mahon et al., 2001, 2003a; Tseng et al., 2001). They are usually considered to be a unique functionally relevant activity pattern and as such shape our ideas on information processing in MSNs. Our recordings show that this two-state intracellular activity is less stereotyped during SWS than during anesthesia, with a higher temporal and voltage variability. However, our results demonstrate that the two-state activity in MSNs is not an artifact of urethane and ketamine–xylazine anesthesia but rather reflects the basic pattern of striatal activity during SWS.

We observed in SWS, at both levels of membrane polarization, a fast depolarizing voltage noise that is scarce in the anesthetized animal, particularly during the down state (Wilson and Kawaguchi, 1996; Goto and O'Donnell, 2002). The origin of this high-frequency synaptic noise during the natural hyperpolarized state is not clear. It could result from a persistent uncorrelated activity in corticostriatal and/or thalamostriatal neurons between periods of rhythmic synchrony. Alternatively, it may reflect depolarizing GABAergic events attributable to high-frequency spiking in striatal interneurons (Mallet et al., 2005) and/or extrinsic inputs arising from subcortical structures, such as the midbrain, whose activity could be altered during anesthesia (Durstewitz and Seamans, 2006). During the natural depolarized state, the increased synaptic noise might originate from corticostriatal neurons whose firing rate may be higher than under anesthesia (Steriade et al., 2001; Timofeev et al., 2001).

It is noteworthy that neurolept analgesia, associating a morphinic with a dopaminergic antagonist, induces a desynchronized EEG with low-amplitude fast waves (Mahon et al., 2001, 2003a) resembling those seen during wakefulness. Under this anesthesia, corticostriatal neurons exhibit a low-frequency irregular firing resulting in weak amplitude disorganized synaptic depolarizations in MSNs, which remain subthreshold for action potential discharge (Mahon et al., 2001, 2003a). Such a correlation between desynchronized EEG and the lack of rhythmic large-amplitude synaptic depolarizations in striatal cells is also consistent with a recent study in the urethane-anesthetized rat indicating a dramatic reduction of membrane potential fluctuations in MSNs when the rhythmic cortical ongoing activity is turned off (Kasanetz et al., 2002, 2006). During the waking state, we observed that the disorganized background synaptic activity in MSNs could generate random firing when it coincided with slow depolarizing envelopes. This irregular spiking pattern associated with unimodally distributed membrane potentials, resulting from a mixture of slow and fast membrane depolarizations, has not been described in the anesthetized animal. Although its functional significance remains to be elucidated, this complex cellular behavior provides, at least in the alert head-restrained animal, the natural intracellular activity of MSNs during waking. Contrasting with the relatively stereotyped electrical activities generated by anesthetics, our findings indicate that membrane potential fluctuations and firing patterns of central neurons are versatile and are strikingly dependent on the state of vigilance. Moreover, these results emphasize the various capabilities of computing and information processing within central networks as a function of physiological processes in which they are engaged.