A Comprehensive Account of Sound Sequence Imitation in the Songbird

The amazing imitation capabilities of songbirds show that they can memorize sensory sequences and transform them into motor activities which in turn generate the original sound sequences. This suggests that the bird's brain can learn 1.) to reliably reproduce spatio-temporal sensory representations and 2.) to transform them into corresponding spatio-temporal motor activations by using an inverse mapping. Neither the synaptic mechanisms nor the network architecture enabling these two fundamental aspects of imitation learning are known. We propose an architecture of coupled neuronal modules that mimick areas in the song bird and show that a unique synaptic plasticity mechanism can serve to learn both, sensory sequences in a recurrent neuronal network, as well as an inverse model that transforms the sensory memories into the corresponding motor activations. The proposed membrane potential dependent learning rule together with the architecture that includes basic features of the bird's brain represents the first comprehensive account of bird imitation learning based on spiking neurons

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Theta-specific susceptibility in a model of adaptive synaptic plasticity

Learning and memory formation are processes which are still not fullyunderstood. It is widely believed that synaptic plasticity is the mostimportant neural substrate for both. However, it has been observed thatlarge-scale theta band oscillations in the mammalian brain are beneficial forlearning, and it is not clear if and how this is linked to synaptic plasticity.Also, the underlying dynamics of synaptic plasticity itself have not beencompletely uncovered yet, especially for nonlinear interactions betweenmultiple spikes.Here, we present a new and simple dynamical model of synaptic plasticity. Itincorporates novel contributions to synaptic plasticity including adaptationprocesses. We test its ability to reproduce nonlinear effects on four differentdata sets of complex spike patterns, and show that the model can be tuned toreproduce the observed synaptic changes in great detail.When subjected to periodically varying firing rates, already linear pair basedspike timing dependent plasticity (STDP) predicts a specific susceptibility ofsynaptic plasticity to pre- and postsynaptic firing rate oscillations in thetheta-band. Our model retains this band-pass property, while for high firingrates in the nonlinear regime it modifies the specific phase relation requiredfor depression and potentiation. For realistic parameters, maximal synapticpotentiation occurs when the postsynaptic is trailing the presynaptic activityslightly. Anti-phase oscillations tend to depress it. Our results are well inline with experimental findings, providing a straightforward and mechanisticexplanation for the importance of theta oscillations for learning

Temporal aspects of contour detection.

<p>Psychophysical contour detection performances in dependence on SOA in the upper row are compared to performance of the optimal model, which best matches human behaviour, in the lower row. Iterations performed in the optimal model were rescaled to time by assuming a constant propagation speed mediated by the AF interactions (corresponding to 13.9 DVA per 200 ms, which was the average length of all contours in the stimulus ensembles). (A) and (C) show performances for different AF alignment jitters, for contours of length (color legend as inset to (C)). (B) and (D) show performances for different inter–element distances which are inversely proportional to the total number of edges in a contour, for an AF jitter of (color legend as inset to (D)). Detection performance for the optimal model was averaged over 5000 samples from each contour ensemble, instead of using only 48 samples as in the experiment, to yield a better statistics and smoother curves.</p