Striatal medium-sized spiny neurons (MSNs) integrate and convey information from the cerebral cortex to the output nuclei of the basal ganglia. Intracellular recordings from anesthetized animals show that MSNs undergo spontaneous transitions between hyperpolarized and depolarized states. State transitions, regarded as necessary for eliciting action potential firing in MSNs, are thought to control basal ganglia function by shaping striatal output. Here, we use an anesthetic-free rat preparation to show that the intracellular activity of MSNs is not stereotyped and depends critically on vigilance state. During slow-wave sleep, much as during anesthesia, MSNs displayed rhythmic step-like membrane potential shifts, correlated with cortical field potentials. However, wakefulness was associated with a completely different pattern of temporally disorganized depolarizing synaptic events of variable amplitude. Transitions from slow-wave sleep to wakefulness converted striatal discharge from a cyclic brisk firing to an irregular pattern of action potentials. These findings illuminate different capabilities of information processing in basal ganglia networks, suggesting in particular that a novel style of striatal computation is associated with the waking state.

High-frequency stimulation (HFS) of the subthalamic nucleus (STN) remarkably alleviates motor disorders in parkinsonian patients. The mechanisms by which STN HFS exerts its beneficial effects were investigated in anesthetized rats, using a model of acute interruption of dopaminergic transmission. Combined systemic injections of SCH-23390 [R(ϩ)-7-chloro-8-hydroxy-3-methyl-1-phenyl-2,3,4,5,-tetrahydro-1H-3-benzazepine] and raclopride, antagonists of the D 1 and D 2 classes of dopaminergic receptors, respectively, were performed, and the parameters of STN HFS that reversed the neuroleptic-induced catalepsy were determined in freely moving animals. The effects of neuroleptics and the impact of STN HFS applied at parameters alleviating neuroleptic-induced catalepsy were analyzed in the substantia nigra pars reticulata (SNR), a major basal ganglia output structure, by recording the neuronal firing pattern and the responses evoked by cortical stimulation. Neuroleptic injection altered the tonic and regular mode of discharge of SNR neurons, most of them becoming irregular with bursts of spikes and pauses. The inhibitory component of the cortically evoked response, which is attributable to the activation of the direct striatonigral circuit, was decreased, whereas the late excitatory response resulting from the indirect striatopallido-subthalamo-nigral circuit was reinforced. During STN HFS, the spontaneous firing of SNR cells was either increased or decreased with a global enhancement of the firing rate in the overall population of SNR cells recorded. However, in all of the cases, SNR firing pattern was regularized, and the bias between the trans-striatal and trans-subthalamic circuits was reversed. By these effects, STN HFS restores the functional properties of the circuits by which basal ganglia contribute to motor activity.

While entropy per unit time is a meaningful index to quantify the dynamic features of experimental time series, its estimation is often hampered in practice by the finite length of the data. We here investigate the performance of entropy estimation procedures, relying either on block entropies or Lempel-Ziv complexity, when only very short symbolic sequences are available. Heuristic analytical arguments point at the influence of temporal correlations on the bias and statistical fluctuations, and put forward a reduced effective sequence length suitable for error estimation. Numerical studies are conducted using, as benchmarks, the wealth of different dynamic regimes generated by the family of logistic maps and stochastic evolutions generated by a Markov chain of tunable correlation time. Practical guidelines and validity criteria are proposed. For instance, block entropy leads to a dramatic overestimation for sequences of low entropy, whereas it outperforms Lempel-Ziv complexity at high entropy. As a general result, the quality of entropy estimation is sensitive to the sequence temporal correlation hence self-consistently depends on the entropy value itself, thus promoting a two-step procedure. Lempel-Ziv complexity is to be preferred in the first step and remains the best estimator for highly correlated sequences.