Songbirds work around computational complexity by learning song vocabulary independently of sequence.

While acquiring motor skills, animals transform their plastic motor sequences to match desired targets. However, because both the structure and temporal position of individual gestures are adjustable, the number of possible motor transformations increases exponentially with sequence length. Identifying the optimal transformation towards a given target is therefore a computationally intractable problem.

Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex.

Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures.

Modular composition and dynamics of native GABAB receptors identified by high-resolution proteomics.

GABAB receptors, the most abundant inhibitory G protein-coupled receptors in the mammalian brain, display pronounced diversity in functional properties, cellular signaling and subcellular distribution. We used high-resolution functional proteomics to identify the building blocks of these receptors in the rodent brain. Our analyses revealed that native GABAB receptors are macromolecular complexes with defined architecture, but marked diversity in subunit composition: the receptor core is assembled from GABAB1a/b, GABAB2, four KCTD proteins and a distinct set of G-protein subunits, whereas the receptor's periphery is mostly formed by transmembrane proteins of different classes.

Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability.

The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of "leak" channels.

Adaptive intrinsic plasticity in human dentate gyrus granule cells during temporal lobe epilepsy.

Granule cells in the dentate gyrus are only sparsely active in vivo and survive hippocampal sclerosis (HS) during temporal lobe epilepsy better than neighboring cells. This phenomenon could be related to intrinsic properties specifically adapted to counteract excitation. We studied the mechanisms underlying the excitability of human granule cells using acute hippocampal slices obtained during epilepsy surgery.

A reafferent and feed-forward model of song syntax generation in the Bengalese finch.

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. We present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area.

A general and efficient method for incorporating precise spike times in globally time-driven simulations.

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise.

Compositionality of arm movements can be realized by propagating synchrony.

We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law.