Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch

Picardo, Michel A.; Merel, Josh; Katlowitz, Kalman; Vallentin, Daniela; Okobi, Daniel E.; Benezra, Sam E.; Clary, Rachel C.; Pnevmatikakis, Eftychios A.; Paninski, Liam; Long, Michael A.

doi:10.1016/j.neuron.2016.02.016

Cited by 123 publications

(164 citation statements)

References 62 publications

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“…A hallmark of HVC RA cells is their remarkable capacity to fire action potentials in an ultra-sparse and sequential, clock-like manner during singing (Hahnloser et al, 2002; Lynch et al, 2016; Picardo et al, 2016). The simulation and intracellular recording methods used here suggest that this ultra-sparse action potential activity is generated from recurrent, high frequency (30-60 Hz) and synchronous synaptic inputs rather than simply from a local excitatory chain mechanism.…”

Section: Discussionmentioning

confidence: 99%

A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

Hamaguchi

Tanaka

Mooney

2016

Neuron

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SUMMARY How do forebrain and brainstem circuits interact to produce temporally precise and reproducible behaviors? Birdsong is an elaborate, temporally precise and stereotyped vocal behavior controlled by a network of forebrain and brainstem nuclei. An influential idea is that song premotor neurons in a forebrain nucleus (HVC) form a synaptic chain that dictates song timing in a top-down manner. Here we combine physiological, dynamical and computational methods to show that song timing is not generated solely by a mechanism localized to HVC but instead is the product of a distributed and recurrent synaptic network spanning the forebrain and brainstem, of which HVC is a component.

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Section: Discussionmentioning

confidence: 99%

A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

Hamaguchi

Tanaka

Mooney

2016

Neuron

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“…There is also evidence that estrogens acting within HVC regulate song stereotypy (Meitzen et al, 2007). Aromatase (AROM) expression has been found within the POM in multiple songbirds, including canaries (Shen et al, 1995;Fusani et al, 2000), whereas AROM protein (in the absence of detectable AROM mRNA) has been found within presynaptic boutons within HVC in zebra finches (Peterson et al, 2005). Therefore, in addition to the potential actions of estrogens of a gonadal origin acting in the POM and HVC to regulate song, actions of estrogens generated from testosterone within these nuclei is also possible.…”

Section: Discussionmentioning

confidence: 99%

“…There is also evidence that neural activity/hormone action in one region can influence morphological or neurophysiology characteristics in downstream or upstream brain regions (Brenowitz and Lent, 2002;Meitzen et al, 2007;Larson et al, 2013;Brenowitz, 2015;Alward et al, 2016c), which could affect control of song. All song features listed in italics are predicted to be controlled by androgens and/or estrogens based on the results of past studies (Spiro et al, 1999;Long and Fee, 2008;Picardo et al, 2016). HVC is colored as a gradient to represent the topographical nature of its functions: lateral HVC controls aspects of syllable usage, whereas medial HVC controls aspects of syntax (Basista et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Dissociable Effects on Birdsong of Androgen Signaling in Cortex-Like Brain Regions of Canaries

2017

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The neural basis of how learned vocalizations change during development and in adulthood represents a major challenge facing cognitive neuroscience. This plasticity in the degree to which learned vocalizations can change in both humans and songbirds is linked to the actions of sex steroid hormones during ontogeny but also in adulthood in the context of seasonal changes in birdsong. We investigated the role of steroid hormone signaling in the brain on distinct features of birdsong using adult male canaries (Serinus canaria), which show extensive seasonal vocal plasticity as adults. Specifically, we bilaterally implanted the potent androgen receptor antagonist flutamide in two key brain regions that control birdsong. We show that androgen signaling in the motor cortical-like brain region, the robust nucleus of the arcopallium (RA), controls syllable and trill bandwidth stereotypy, while not significantly affecting higher order features of song such syllable-type usage (i.e., how many times each syllable type is used) or syllable sequences. In contrast, androgen signaling in the premotor cortical-like brain region, HVC (proper name), controls song variability by increasing the variability of syllable-type usage and syllable sequences, while having no effect on syllable or trill bandwidth stereotypy. Other aspects of song, such as the duration of trills and the number of syllables per song, were also differentially affected by androgen signaling in HVC versus RA. These results implicate androgens in regulating distinct features of complex motor output in a precise and nonredundant manner.

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“…Adult zebra finches (Taeniopygia guttata) sing renditions of a stereotyped motif (a sequence of 3-10 syllables), whose temporal and/or motor structure is thought to be encoded in the activities of two major types of HVC neurons (Fig. 1a) [8][9][10][11][12][13] . We implanted 16/32 site Si-probes in male, adult zebra finches and recorded simultaneously their song and neural activity in HVC; then we used these data to train a long-short-term memory network (LSTM 5 ) to translate neural activity directly onto song.…”

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confidence: 99%

A neural decoder for learned vocal behavior

Arneodo

Chen

Gilja

et al. 2017

Preprint

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State of the art BMIs succeed at decoding behavioral intention from brain activity by mapping features of neuronal ensemble activity onto a motor space 1,2 . Yet the these motor spaces are confined by current technologies to rather simple actions. To prototype a decoder of complex, natural communication signals from neural activity, we capitalize on two aspects of birdsong, a powerful animal model for vocal learning that shares many features with humans speech 3,4 . First, birdsong is temporally structured (like human speech); this temporal patterning can be built into a decoder using a recurrent neural network 5 . Second, the biomechanics of birdsong production are well understood; this enables us to employ a biophysical model of the vocal organ that captures most of the complexity of the song and reduces it to a lower dimensional parameter space 6,7 . By combining these techniques, we decode realistic synthetic birdsong directly from neural activity.Our decoder interfaces with the sensory-motor nucleus HVC (used as proper name), where neurons generate high-level motor commands that shape the production of learned song. Adult zebra finches (Taeniopygia guttata) sing renditions of a stereotyped motif (a sequence of 3-10 syllables), whose temporal and/or motor structure is thought to be encoded in the activities of two major types of HVC neurons (Fig. 1a) [8][9][10][11][12][13] . We implanted 16/32 site Si-probes in male, adult zebra finches and recorded simultaneously their song and neural activity in HVC; then we used these data to train a long-short-term memory network (LSTM 5 ) to translate neural activity directly onto song. The goal of the network is to predict the spectral components of the song at a time bin t i , given the values of neural activity features over previous time bins (Fig. 1d-f). The neural activity is (t , t , )fed as a matrix comprising mean firing rates in each time bin, of each putative single/multi-unit automatically sorted from the recordings 14 (32/64 clusters); the spectral components of the song are represented by the power across 64 log-spaced frequency bands. For each session (day), we separate the 70-110 renditions of a motif the bird sang. We then train the LSTM network to find the neural-to-song spectrum mappings, and decode the corresponding spectral components from a test set of neural activity to finally recover waveforms of synthetically generated song motifs. We employed several methods to avoid overfitting. First, the order in which each pair of neural feature window/target was presented to the network was randomized, so that the predictions of the spectral components at each time point are independent; second, we used standard techniques such as L2 weight regularization, dropout and early stopping (see Methods). We also employed two different procedures to produce the training/validation and test sets. For motif-wise training/decoding, we split the data into peer-reviewed)

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Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch

Cited by 123 publications

References 62 publications

A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

Dissociable Effects on Birdsong of Androgen Signaling in Cortex-Like Brain Regions of Canaries

A neural decoder for learned vocal behavior

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