Animal Models of Epilepsy: Legacies and New Directions

http://biorxiv.org/content/early/2015/01/03/013136

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Human epilepsies encompass a wide variety of clinical, behavioral and electrical manifestations. Correspondingly, studies of this disease in nonhuman animals have brought forward an equally wide array of animal models, i.e. species and acute or chronic seizure induction protocols. Epilepsy research has a long history of comparative anatomical and physiological studies on a range of mostly mammalian species. Nonetheless, a relatively limited number of rodent models emerged as the primary choices for most epilepsy-related investigations. In many cases these animal models are selected based on convenience or tradition, though technical or experimental rationale does, and should, factor into these decisions. More complex mammalian brains and, especially, genetic model organisms including zebrafish have been studied less but offer significant advantages that are being widely recognized.

Asynchronous Rate Chaos in Spiking Neuronal Circuits

http://biorxiv.org/content/early/2015/01/02/013375

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The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean- Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.

The neural correlates of consciousness in sleep: a no-task, within-state paradigm

http://biorxiv.org/content/early/2014/12/30/012443

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