Daniel Margoliash

Bio: Daniel Margoliash is an academic researcher from University of Chicago. The author has contributed to research in topics: Zebra finch & Vocal learning. The author has an hindex of 40, co-authored 107 publications receiving 8015 citations. Previous affiliations of Daniel Margoliash include University of Illinois at Chicago & California Institute of Technology.

Temporal Hierarchical Control of Singing in Birds

Abstract: Songs of birds comprise hierarchical sets of vocal gestures. In zebra finches, songs include notes and syllables (groups of notes) delivered in fixed sequences. During singing, premotor neurons in the forebrain nucleus HVc exhibited reliable changes in activity rates whose patterns were uniquely associated with syllable identity. Neurons in the forebrain nucleus robustus archistriatalis, which receives input from the HVc, exhibited precisely timed and structured bursts of activity that were uniquely associated with note identity. Hence, units of vocal behavior are represented hierarchically in the avian forebrain. The representation of temporal sequences at each level of the hierarchy may be established by means of a decoding process involving interactions of higher level input with intrinsic local circuitry. Behavior is apparently represented by precise temporal patterning of spike trains at lower levels of the hierarchy.

Recursive syntactic pattern learning by songbirds.

Abstract: Noam Chomsky's work on ‘generative grammar’ led to the concept of a set of rules that can generate a natural language with a hierarchical grammar, and the idea that this represents a uniquely human ability. In a series of experiments with European starlings, in which several types of ‘warble’ and ‘rattle’ took the place of words in a human language, the birds learnt to classify phrase structure grammars in a way that met the same criteria. Their performance can be said to be almost human on this yardstick. So if there are language processing capabilities that are uniquely human, they may be more context-free or at a higher level in the Chomsky hierarchy. Or perhaps there is no single property or processing capacity that differentiates human language from non-human communication systems. Humans regularly produce new utterances that are understood by other members of the same language community1. Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the acceptable structure of utterances2. The recursive, hierarchical embedding of language units (for example, words or phrases within shorter sentences) that is part of the ability to construct new utterances minimally requires a ‘context-free’ grammar2,3 that is more complex than the ‘finite-state’ grammars thought sufficient to specify the structure of all non-human communication signals. Recent hypotheses make the central claim that the capacity for syntactic recursion forms the computational core of a uniquely human language faculty4,5. Here we show that European starlings (Sturnus vulgaris) accurately recognize acoustic patterns defined by a recursive, self-embedding, context-free grammar. They are also able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns. Thus, the capacity to classify sequences from recursive, centre-embedded grammars is not uniquely human. This finding opens a new range of complex syntactic processing mechanisms to physiological investigation.

Acoustic parameters underlying the responses of song-specific neurons in the white-crowned sparrow.

Abstract: Songbirds such as the white-crowned sparrow memorize the song of conspecific adults during a critical period early in life and later in life develop song by utilizing auditory feedback. Neurons in one of the telencephalic nuclei controlling song have recently been shown to respond to acoustic stimuli. I investigated the auditory response properties of units in this nucleus using a technique that permitted great flexibility in manipulating complex stimuli such as song. A few of the units exhibited considerable selectivity for the individual9s own song. In wild-caught birds, song-specific units exhibited intradialect selectivity. In those birds that sang abnormal songs due to laboratory manipulation of song exposure during the critical period for song learning, units were selective for the abnormal songs. By systematic modification of a song, and by construction of complex synthetic sounds mimicking song, the acoustic parameters responsible for the response selectivity were identified. Song-specific units responded to sequences of two song parts but not to the parts in isolation. Modification of the frequencies of either part of the sequence, or increasing the interval between the parts, varied the strength of the response. Thus, temporal as well as spectral parameters were important for the response. When sequences of synthetic sounds mimicking song were effective in evoking an excitatory response, the response was sensitive to the aforementioned manipulations. Wih these techniques it was possible to elucidate the acoustic parameters required to excite song-specific units. All songs of the repertoire eliciting a strong excitatory response contained the appropriate parameters, which were missing from all weakly effective, ineffective, or inhibitory songs.

Song Replay During Sleep and Computational Rules for Sensorimotor Vocal Learning

Abstract: Songbirds learn a correspondence between vocal-motor output and auditory feedback during development. For neurons in a motor cortex analog of adult zebra finches, we show that the timing and structure of activity elicited by the playback of song during sleep matches activity during daytime singing. The motor activity leads syllables, and the matching sensory response depends on a sequence of typically up to three of the preceding syllables. Thus, sensorimotor correspondence is reflected in temporally precise activity patterns of single neurons that use long sensory memories to predict syllable sequences. Additionally, “spontaneous” activity of these neurons during sleep matches their sensorimotor activity, a form of song “replay.” These data suggest a model whereby sensorimotor correspondences are stored during singing but do not modify behavior, and off-line comparison (e.g., during sleep) of rehearsed motor output and predicted sensory feedback is used to adaptively shape motor output.

Consolidation during sleep of perceptual learning of spoken language

Abstract: Memory consolidation resulting from sleep has been seen broadly: in verbal list learning, spatial learning, and skill acquisition in visual and motor tasks. These tasks do not generalize across spatial locations or motor sequences, or to different stimuli in the same location. Although episodic rote learning constitutes a large part of any organism's learning, generalization is a hallmark of adaptive behaviour. In speech, the same phoneme often has different acoustic patterns depending on context. Training on a small set of words improves performance on novel words using the same phonemes but with different acoustic patterns, demonstrating perceptual generalization. Here we show a role of sleep in the consolidation of a naturalistic spoken-language learning task that produces generalization of phonological categories across different acoustic patterns. Recognition performance immediately after training showed a significant improvement that subsequently degraded over the span of a day's retention interval, but completely recovered following sleep. Thus, sleep facilitates the recovery and subsequent retention of material learned opportunistically at any time throughout the day. Performance recovery indicates that representations and mappings associated with generalization are refined and stabilized during sleep.

A Neural Substrate of Prediction and Reward

Abstract: The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or