An Automated Procedure for Evaluating Song Imitation

Songbirds have emerged as an excellent model system to understand the neural basis of vocal and motor learning. Like humans, songbirds learn to imitate the vocalizations of their parents or other conspecific “tutors.” Young songbirds learn by comparing their own vocalizations to the memory of their tutor song, slowly improving until over the course of several weeks they can achieve an excellent imitation of the tutor. Because of the slow progression of vocal learning, and the large amounts of singing generated, automated algorithms for quantifying vocal imitation have become increasingly important for studying the mechanisms underlying this process. However, methodologies for quantifying song imitation are complicated by the highly variable songs of either juvenile birds or those that learn poorly because of experimental manipulations. Here we present a method for the evaluation of song imitation that incorporates two innovations: First, an automated procedure for selecting pupil song segments, and, second, a new algorithm, implemented in Matlab, for computing both song acoustic and sequence similarity. We tested our procedure using zebra finch song and determined a set of acoustic features for which the algorithm optimally differentiates between similar and non-similar songs.National Institutes of Health (U.S.) (R01 MH067105

A role for descending auditory cortical projections in songbird vocal learning

Many learned motor behaviors are acquired by comparing ongoing behavior with an internal representation of correct performance, rather than using an explicit external reward. For example, juvenile songbirds learn to sing by comparing their song with the memory of a tutor song. At present, the brain regions subserving song evaluation are not known. In this study, we report several findings suggesting that song evaluation involves an avian 'cortical' area previously shown to project to the dopaminergic midbrain and other downstream targets. We find that this ventral portion of the intermediate arcopallium (AIV) receives inputs from auditory cortical areas, and that lesions of AIV result in significant deficits in vocal learning. Additionally, AIV neurons exhibit fast responses to disruptive auditory feedback presented during singing, but not during nonsinging periods. Our findings suggest that auditory cortical areas may guide learning by transmitting song evaluation signals to the dopaminergic midbrain and/or other subcortical targets.National Institutes of Health (U.S.) (grant R01 MH067105)McGovern Institute for Brain Research at MIT (Internal funding

A role for descending auditory cortical projections in songbird vocal learning

Many learned motor behaviors are acquired by comparing ongoing behavior with an internal representation of correct performance, rather than using an explicit external reward. For example, juvenile songbirds learn to sing by comparing their song with the memory of a tutor song. At present, the brain regions subserving song evaluation are not known. In this study, we report several findings suggesting that song evaluation involves an avian 'cortical' area previously shown to project to the dopaminergic midbrain and other downstream targets. We find that this ventral portion of the intermediate arcopallium (AIV) receives inputs from auditory cortical areas, and that lesions of AIV result in significant deficits in vocal learning. Additionally, AIV neurons exhibit fast responses to disruptive auditory feedback presented during singing, but not during nonsinging periods. Our findings suggest that auditory cortical areas may guide learning by transmitting song evaluation signals to the dopaminergic midbrain and/or other subcortical targets.National Institutes of Health (U.S.) (Grant R01 MH067105

Expressions of Multiple Neuronal Dynamics during Sensorimotor Learning in the Motor Cortex of Behaving Monkeys

Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis), during adaptation to force-field perturbations. Perturbed trials (reaching to one direction) were practiced along with unperturbed trials (to other directions). The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.United States-Israel Binational Science FoundationIsrael Science FoundationIda Baruch FundRosetrees Trus

Regulation of Agouti-Related Protein and Pro-Opiomelanocortin Gene Expression in the Avian Arcuate Nucleus

The arcuate nucleus is generally conserved across vertebrate taxa in its neuroanatomy and neuropeptide expression. Gene expression of agouti-related protein (AGRP), neuropeptide Y (NPY), pro-opiomelanocortin (POMC), and cocaine- and amphetamine-regulated transcript (CART) has been established in the arcuate nucleus of several bird species and co-localization demonstrated for AGRP and NPY. The proteins encoded by these genes exert comparable effects on food intake in birds after central administration to those seen in other vertebrates, with AGRP and NPY being orexigenic and CART and α-melanocyte-stimulating hormone anorexigenic. We have focused on the measurement of arcuate nucleus AGRP and POMC expression in several avian models in relation to the regulation of energy balance, incubation, stress, and growth. AGRP mRNA and POMC mRNA are, respectively, up- and downregulated after energy deprivation and restriction. This suggests that coordinated changes in the activity of AGRP and POMC neurons help to drive the homeostatic response to replace depleted energy stores in birds as in other vertebrates. While AGRP and POMC expression are generally positively and negatively correlated with food intake, respectively, we review here situations in some avian models in which AGRP gene expression is dissociated from the level of food intake and may have an influence on growth independent of changes in appetite. This suggests the possibility that the central melanocortin system exerts more pleiotropic functions in birds. While the neuroanatomical arrangement of AGRP and POMC neurons and the sensitivity of their activity to nutritional state appear generally conserved with other vertebrates, detailed knowledge is lacking of the key nutritional feedback signals acting on the avian arcuate nucleus and there appear to be significant differences between birds and mammals. In particular, recently identified avian leptin genes show differences between bird species in their tissue expression patterns and appear less closely linked in their expression to nutritional state. It is presently uncertain how the regulation of the central melanocortin system in birds is brought about in the situation of the apparently reduced importance of leptin and ghrelin compared to mammals

Spectral features for representation of song.

<p>We consider the following 6 features for representing song acoustic structure: Top to bottom: gravity center, spectral width, Weiner entropy, pitch goodness frequency modulation and pitch.</p

Comparison of different methods of measuring acoustic similarity.

<p>A) Selecting the optimal set of features. For each bird we measured the similarity of extracted song bouts to its own song motif (self-similarity) and to the motif of other birds (cross-similarity). These were computed using different combinations of spectral features (indicated with different colors and symbols). Inset: The contrast between self-similarity and cross-similarity, shown for each different subset of features tested. B-E) The SI algorithm yields higher contrast than the SAP software. Acoustic (B) and sequence (D) self-similarity versus cross-similarity computed using the Similarity Index (SI) algorithm with the optimal features (red), using the SI algorithm with the set of features used by SAP (cyan), and using SAP software (blue). The contrast was significantly larger using the SI algorithm with optimal features, both for the acoustic similarity scores and sequence similarity scores (C and E, respectively).</p

Selection of tutor and pupil songs for similarity analysis.

<p>A) Example of tutor song showing stereotyped motifs. B) Examples of pupil song bouts, showing variable song structure including motifs and other irregular vocal elements. Some segments of pupil song are more similar to the tutor motif than others. Green bars represent apparent motifs. C–F) The effect of manual (blue) versus automatic (black) selection of song segments on similarity analysis (computed with Sound Analysis Pro, SAP). C) The songs of each bird are compared to other songs of the same bird (self-similarity) or to songs of other adult birds in the colony (cross-similarity). Each point in (C) plots the self-similarity vs cross-similarity score for one bird based on the acoustic similarity of the songs. D) Contrast between self- and cross-similarity, computed for acoustic similarity scores. Each point is one bird. E) Sequence self-similarity versus sequence cross-similarity. F) Contrast between self- and cross-similarity, computed for sequence similarity scores. Each line in figures D,F connects results from the same birds, carried out either by manually or automatically selecting song segments. G) Examples of inconsistent segmentation of song into syllables and silent gaps. For panels A-B, G: top, song spectrogram; middle, segmentation of the song into syllables (red bars); bottom, sound amplitude (log power).</p