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. Here we show an evolutionary workaround for reducing the computational complexity of song learning in zebra finches. We prompt juveniles to modify syllable phonology and sequence in a learned song to match a newly introduced target song. Surprisingly, juveniles match each syllable to the most spectrally similar sound in the target, regardless of its temporal position, resulting in unnecessary sequence errors, that they later try to correct. Thus, zebra finches prioritize efficient learning of syllable vocabulary, at the cost of inefficient syntax learning. This strategy provides a non-optimal but computationally manageable solution to the task of vocal sequence learning.

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. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.

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. In particular, the periphery-forming constituents include signaling effectors, such as Cav2 and HCN channels, and the proteins AJAP1 and amyloid-ß A4, both of which tightly associate with the sushi domains of GABAB1a. Our results unravel the molecular diversity of GABAB receptors and their postnatal assembly dynamics and provide a roadmap for studying the cellular signaling of this inhibitory neurotransmitter receptor.

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. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. Two proposed TLE seizure mechanisms were implemented in various degrees and combinations: recurrent GC excitation via mossy fiber sprouting and increased PP input. While increasing PP strength degraded pattern separation only gradually, already the slight elevation of sprouting drastically (non-linearly) impaired pattern separation. In most tested hyperexcitable networks, leaky GCs ameliorated pattern separation. However, in some sprouting situations with all-or-none seizure behavior, pattern separation was disabled with and without leaky GCs. In the mild sprouting (and PP increase) region of non-linear impairment, leaky GCs were particularly effective in restoring pattern separation performance. These results are compatible with the hypothesis that the experimentally observed intrinsic rescaling of GCs serves to maintain the physiological function of the DG network.

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. Patch-clamp recordings were combined with pharmacology, immunocytochemistry, and computer simulations. The input resistance of granule cells correlated negatively with the duration of epilepsy and the degree of HS. Hyperpolarization-activated, ZD7288-sensitive cation (I(H), HCN) currents and highly Ba(2+)-sensitive, inwardly rectifying K(+) (Kir) currents (and HCN1 and Kir2.2 protein) were present somatodendritically and further enhanced in patients with severe HS versus mild HS. The properties and function of I(H) were characterized in granule cells. Although I(H) depolarized the membrane, it strongly reduced the input resistance and shifted the current-frequency function to higher input values. The shunting influence of HCN and Kir was similar and these conductances correlated. Resonance was not observed. Simulations suggest that the combined upregulation of Kir and HCN conductances attenuates excitatory synaptic input, while stabilizing the membrane potential and responsiveness. Thus, granule cells homeostatically downscale their input-output transfer function during epilepsy.

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. Propagating synfire activity in the HVC codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. The model also reproduces for the first time experimental findings on the influence of altered auditory feedback on the song syntax generation, and predicts song- and species-specific low frequency components in the LFP. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons.

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. Connections between chains that match the final velocity of one encoded primitive to the initial velocity of the next allow the composition of random sequences of primitives with smooth transitions. The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites. Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

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. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision.