Riluzole-induced oscillations in spinal networks

We previously showed in dissociated cultures of fetal rat spinal cord that disinhibition-induced bursting is based on intrinsic spiking, network recruitment, and a network refractory period after the bursts. A persistent sodium current (I(NaP)) underlies intrinsic spiking, which, by recurrent excitation, generates the bursting activity. Although full blockade of I(NaP) with riluzole disrupts such bursting, the present study shows that partial blockade of I(NaP) with low doses of riluzole maintains bursting activity with unchanged burst rate and burst duration. More important, low doses of riluzole turned bursts composed of persistent activity into bursts composed of oscillatory activity at around 5 Hz. In a search for the mechanisms underlying the generation of such intraburst oscillations, we found that activity-dependent synaptic depression was not changed with low doses of riluzole. On the other hand, low doses of riluzole strongly increased spike-frequency adaptation and led to early depolarization block when bursts were simulated by injecting long current pulses into single neurons in the absence of fast synaptic transmission. Phenytoin is another I(NaP) blocker. When applied in doses that reduced intrinsic activity by 80-90%, as did low doses of riluzole, it had no effect either on spike-frequency adaptation or on depolarization block. Nor did phenytoin induce intraburst oscillations after disinhibition. A theoretical model incorporating a depolarization block mechanism could reproduce the generation of intraburst oscillations at the network level. From these findings we conclude that riluzole-induced intraburst oscillations are a network-driven phenomenon whose major accommodation mechanism is depolarization block arising from strong sodium channel inactivation.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.