Mechanism of modulation of AMPA receptors by TARP-γ8

Fast excitatory synaptic transmission in the mammalian central nervous system is mediated by glutamate-activated α-amino-5-methyl-3-hydroxy-4-isoxazole propionate (AMPA) receptors. In neurons, AMPA receptors coassemble with transmembrane AMPA receptor regulatory proteins (TARPs). Assembly with TARP γ8 alters the biophysical properties of the receptor, producing resensitization currents in the continued presence of glutamate. Using single-channel recordings, we show that under resensitizing conditions, GluA2 AMPA receptors primarily transition to higher conductance levels, similar to activation of the receptors in the presence of cyclothiazide, which stabilizes the open state. To study the conformation associated with these states, we have used single-molecule FRET and show that this high-conductance state exhibits tighter coupling between subunits in the extracellular parts of the receptor. Furthermore, the dwell times for the transition from the tightly coupled state to the decoupled states correlate to longer open durations of the channels, thus correlating conformation and function at the single-molecule level.

Synaptic plasticity through activation of GluA3-containing AMPA-receptors

Excitatory synaptic transmission is mediated by AMPA-type glutamate receptors (AMPARs). In CA1 pyramidal neurons of the hippocampus two types of AMPARs predominate: those that contain subunits GluA1 and GluA2 (GluA1/2), and those that contain GluA2 and GluA3 (GluA2/3). Whereas subunits GluA1 and GluA2 have been extensively studied, the contribution of GluA3 to synapse physiology has remained unclear. Here we show in mice that GluA2/3s are in a low-conductance state under basal conditions, and although present at synapses they contribute little to synaptic currents. When intracellular cyclic AMP (cAMP) levels rise, GluA2/3 channels shift to a high-conductance state, leading to synaptic potentiation. This cAMP-driven synaptic potentiation requires the activation of both protein kinase A (PKA) and the GTPase Ras, and is induced upon the activation of β-adrenergic receptors. Together, these experiments reveal a novel type of plasticity at CA1 hippocampal synapses that is expressed by the activation of GluA3-containing AMPARs.

Cross-Correlations in High-Conductance States of a Model Cortical Network

Neuronal firing correlations are studied using simulations of a simple network model for a cortical column in a high-conductance state with dynamically balanced excitation and inhibition. Although correlations between individual pairs of neurons exhibit considerable heterogeneity, population averages show systematic behavior. When the network is in a stationary state, the average correlations are generically small: correlation coefficients are of order 1/N, where N is the number of neurons in the network. However, when the input to the network varies strongly in time, much larger values are found. In this situation, the network is out of balance, and the synaptic conductance is low, at times when the strongest firing occurs. However, examination of the correlation functions of synaptic currents reveals that after these bursts, balance is restored within a few milliseconds by a rapid increase in inhibitory synaptic conductance. These findings suggest an extension of the notion of the balanced state to include balanced fluctuations of synaptic currents, with a characteristic timescale of a few milliseconds.

The High-Conductance State of Cortical Networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.