Coordinate regulation of gonadotropin-releasing hormone neuronal firing patterns by cytosolic calcium and store depletion

Elevation of cytosolic free Ca2+ concentration ([Ca2+]i) in excitable cells often acts as a negative feedback signal on firing of action potentials and the associated voltage-gated Ca2+ influx. Increased [Ca2+]i stimulates Ca2+-sensitive K+ channels (IK-Ca), and this, in turn, hyperpolarizes the cell and inhibits Ca2+ influx. However, in some cells expressing IK-Ca the elevation in [Ca2+]i by depletion of intracellular stores facilitates voltage-gated Ca2+ influx. This phenomenon was studied in hypothalamic GT1 neuronal cells during store depletion caused by activation of gonadotropin-releasing hormone (GnRH) receptors and inhibition of endoplasmic reticulum (Ca2+)ATPase with thapsigargin. GnRH induced a rapid spike increase in [Ca2+]i accompanied by transient hyperpolarization, followed by a sustained [Ca2+]i plateau during which the depolarized cells fired with higher frequency. The transient hyperpolarization was caused by the initial spike in [Ca2+]i and was mediated by apamin-sensitive IK-Ca channels, which also were operative during the subsequent depolarization phase. Agonist-induced depolarization and increased firing were independent of [Ca2+]i and were not mediated by inhibition of K+ current, but by facilitation of a voltage-insensitive, Ca2+-conducting inward current. Store depletion by thapsigargin also activated this inward depolarizing current and increased the firing frequency. Thus, the pattern of firing in GT1 neurons is regulated coordinately by apamin-sensitive SK current and store depletion-activated Ca2+ current. This dual control of pacemaker activity facilitates voltage-gated Ca2+ influx at elevated [Ca2+]i levels, but also protects cells from Ca2+ overload. This process may also provide a general mechanism for the integration of voltage-gated Ca2+ influx into receptor-controlled Ca2+ mobilization.

Functional reorganization in thalamocortical networks: Transition between spindling and delta sleep rhythms

Thalamic reticularis, thalamocortical, and cortical cells participate in the 7–14-hz spindling rhythm of early sleep and the slower delta rhythms of deeper sleep, with different firing patterns. In this case study, showing the interactions of intrinsic and synaptic properties, a change in the conductance of one kind of cell effectively rewires the thalamocortical circuit, leading to the transition from the spindling to the delta rhythm. The two rhythms make different uses of the fast (GABAA) and slow (GABAB) inhibition generated by the thalamic reticularis cells.

Genetically altered AMPA-type glutamate receptor kinetics in interneurons disrupt long-range synchrony of gamma oscillation

Gamma oscillations synchronized between distant neuronal populations may be critical for binding together brain regions devoted to common processing tasks. Network modeling predicts that such synchrony depends in part on the fast time course of excitatory postsynaptic potentials (EPSPs) in interneurons, and that even moderate slowing of this time course will disrupt synchrony. We generated mice with slowed interneuron EPSPs by gene targeting, in which the gene encoding the 67-kDa form of glutamic acid decarboxylase (GAD67) was altered to drive expression of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptor subunit GluR-B. GluR-B is a determinant of the relatively slow EPSPs in excitatory neurons and is normally expressed at low levels in γ-aminobutyric acid (GABA)ergic interneurons, but at high levels in the GAD-GluR-B mice. In both wild-type and GAD-GluR-B mice, tetanic stimuli evoked gamma oscillations that were indistinguishable in local field potential recordings. Remarkably, however, oscillation synchrony between spatially separated sites was severely disrupted in the mutant, in association with changes in interneuron firing patterns. The congruence between mouse and model suggests that the rapid time course of AMPA receptor-mediated EPSPs in interneurons might serve to allow gamma oscillations to synchronize over distance.

Multineuronal codes in retinal signaling.

The visual world is presented to the brain through patterns of action potentials in the population of optic nerve fibers. Single-neuron recordings show that each retinal ganglion cell has a spatially restricted receptive field, a limited integration time, and a characteristic spectral sensitivity. Collectively, these response properties define the visual message conveyed by that neuron's action potentials. Since the size of the optic nerve is strictly constrained, one expects the retina to generate a highly efficient representation of the visual scene. By contrast, the receptive fields of nearby ganglion cells often overlap, suggesting great redundancy among the retinal output signals. Recent multineuron recordings may help resolve this paradox. They reveal concerted firing patterns among ganglion cells, in which small groups of nearby neurons fire synchronously with delays of only a few milliseconds. As there are many more such firing patterns than ganglion cells, such a distributed code might allow the retina to compress a large number of distinct visual messages into a small number of optic nerve fibers. This paper will review the evidence for a distributed coding scheme in the retinal output. The performance limits of such codes are analyzed with simple examples, illustrating that they allow a powerful trade-off between spatial and temporal resolution.

Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons.

The collective behavior of interconnected spiking nerve cells is investigated. It is shown that a variety of model systems exhibit the same short-time behavior and rapidly converge to (approximately) periodic firing patterns with locally synchronized action potentials. The dynamics of one model can be described by a downhill motion on an abstract energy landscape. Since an energy landscape makes it possible to understand and program computation done by an attractor network, the results will extend our understanding of collective computation from models based on a firing-rate description to biologically more realistic systems with integrate-and-fire neurons.