Active decorrelation in the basal ganglia.
Thursday, October 10, 2013
Charles J. Wilson Neuroscience 250:467-482.
The cytoarchitecturally-homogeneous appearance of the globus pallidus, subthalamic nucleus and substantia nigra has long been said to imply a high degree of afferent convergence and sharing of inputs by nearby neurons. Moreover, axon collaterals of neurons in the external segment of the globus pallidus and the substantia nigra pars reticulata arborize locally and make inhibitory synapses on other cells of the same type. These features suggest that the connectivity of the basal ganglia may impose spike-time correlations among the cells, and it has been puzzling that experimental studies have failed to demonstrate such correlations. One possible solution arises from studies of firing patterns in basal ganglia cells, which reveal that they are nearly all pacemaker cells. Their high rate of firing does not depend on synaptic excitation, but they fire irregularly because a dense barrage of synaptic inputs normally perturbs the timing of their autonomous activity. Theoretical and computational studies show that the responses of repetitively firing neurons to shared input or mutual synaptic coupling often defy classical intuitions about temporal synaptic integration. The patterns of spike timing among such neurons depend on the ionic mechanism of pacemaking, the level of background uncorrelated cellular and synaptic noise, and the firing rates of the neurons, as well as the properties of their synaptic connections. Application of these concepts to the basal ganglia circuitry suggests that the connectivity and physiology of these nuclei may be configured to prevent the establishment of permanent spike-timing relationships between neurons. The development of highly synchronous oscillatory patterns of activity in Parkinson’s disease may result from the loss of pacemaking by some basal ganglia neurons, and accompanying breakdown of the mechanisms responsible for active decorrelation.
Figure 1. The connectivity of the basal ganglia in the rat.
Cell numbers decrease dramatically from the neostriatum to the output cells of the GPi (entopeduncular nucleus in the rat) and SNr. However, the most critical numbers for input sharing are the number of synapses made per neuron in target structures. These numbers are mostly available for the GPe, but not for the other non-striatal nuclei.
Figure 2. Inhibitory resetting of the autonomous firing of a rat GPe cell.
The cell was recorded using the perforated patch method in a GPe slice. The stimulus (at arrow) was an artificial GABAA synaptic conductance with a 0.25 ms rise time constant, a 6 ms decay time constant, peak amplitude of 3 nS, and reversal potential of -65 mV. A. 50 superimposed trials aligned to the stimulus. The stimulus was presented so that it arrived at randomized phases in the ongoing activity. B. 3 trials selected from A to show that the stimulus changes spike timing differently depending on the phase of its arrival, and this is responsible for the reduction in variance of spike times. C. Post-stimulus histogram of spike times constructed for 620 stimulus presentations.
Figure 3. Construction of the phase-resetting curve for a GPe neuron.
Top: example traces showing interspike membrane potential trajectories in the absence of stimulation, and with brief (5 ms) subthreshold current pulses (40 pA) applied at two different times (ts1, ts2) in the interspike interval. These produce correspondingly different changes in spike timing Δt1 and Δt2. Bottom: phase resetting curve constructed from 500 trials like the ones at top. Individual points are single trials, the solid line is the resetting curve for suprathreshold stimuli.
Figure 4. Construction of post-stimulus histograms.
Post stimulus histograms calculated for excitatory and inhibitory phase resetting using an idealized GP-like phase-resetting curve (inset). The strength and the phase of resetting produced by stimulation depends on stimulus intensity. For weak stimuli, excitatory and inhibitory stimuli reset to diametrically opposite phases, but at high stimulus levels, they approach the same phase. There is no threshold for the phase resetting effect of synaptic input.
Figure 5. The origin of rate heterogeneity in the GPe.
Top: Distribution of firing rates for 3 min samples of activity in GPe neurons recorded in rat slices. Note the broad range of firing rates. Bottom: A pair of GPe neurons recorded simultaneously, showing independent spontaneous wander of firing rates over nearly the entire range seen in the histogram.
Figure 6. Effect of rate difference on phase-locking of GPe neurons.
A pair of neurons recorded simultaneously in a slice was coupled by reciprocal GABAA-like synaptic connections. The rates were controlled by passing constant current, to be approximately equal (left column) or to differ by about 30% (right column). A. Example traces showing stable antiphase firing with equal rates, and phase-walkthrough when rates differ (the faster cell advances phase relative to the slower one). B. Evolution of phase difference between the two cells. C. Instantaneous firing rates for the two cells.