Abstract:Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellul… Show more
“…The student neurons were in turn driven by the activity of the conductor neurons . The student also received tutor signals to guide plasticity; in the songbird, the guiding signals for each RA neuron come from several LMAN neurons (Canady et al, 1988; Garst-Orozco et al, 2014; Herrmann and Arnold, 1991). In our model, we summarized the net input from the tutor to the th student neuron as a single function .10.7554/eLife.20944.003Figure 2.Schematic representation of our rate-based model.( A ) Conductor neurons fire precisely-timed bursts, similar to HVC neurons in songbirds.…”
Section: Resultsmentioning
confidence: 99%
“…We typically chose to constrain the rates to the range 0–160 Hz (Ölveczky et al, 2005; Garst-Orozco et al, 2014). Learning slowed down with this change (Figure 4A and online Video 3) as a result of the tutor firing rates saturating when the mismatch between the motor output and the target output was large.…”
Section: Resultsmentioning
confidence: 99%
“…Although biologically there are several LMAN neurons projecting to each RA neuron, we again simplified by ‘summing’ the LMAN inputs into a single, effective tutor neuron, similarly to the approach in (Fiete et al, 2007). The LMAN-RA synapses were modeled in a current-based approach as a mixture of AMPA and NMDA receptors, following the songbird data (Garst-Orozco et al, 2014; Stark and Perkel, 1999). The initial weights for all synapses were tuned to produce RA firing patterns resembling juvenile birds (Ölveczky et al, 2011), subject to constraints from direct measurements in slice recordings (Garst-Orozco et al, 2014) (see Materials and methods for details, and Figure 5B for a comparison between neural recordings and spiking in our model).…”
Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.DOI:
http://dx.doi.org/10.7554/eLife.20944.001
“…The student neurons were in turn driven by the activity of the conductor neurons . The student also received tutor signals to guide plasticity; in the songbird, the guiding signals for each RA neuron come from several LMAN neurons (Canady et al, 1988; Garst-Orozco et al, 2014; Herrmann and Arnold, 1991). In our model, we summarized the net input from the tutor to the th student neuron as a single function .10.7554/eLife.20944.003Figure 2.Schematic representation of our rate-based model.( A ) Conductor neurons fire precisely-timed bursts, similar to HVC neurons in songbirds.…”
Section: Resultsmentioning
confidence: 99%
“…We typically chose to constrain the rates to the range 0–160 Hz (Ölveczky et al, 2005; Garst-Orozco et al, 2014). Learning slowed down with this change (Figure 4A and online Video 3) as a result of the tutor firing rates saturating when the mismatch between the motor output and the target output was large.…”
Section: Resultsmentioning
confidence: 99%
“…Although biologically there are several LMAN neurons projecting to each RA neuron, we again simplified by ‘summing’ the LMAN inputs into a single, effective tutor neuron, similarly to the approach in (Fiete et al, 2007). The LMAN-RA synapses were modeled in a current-based approach as a mixture of AMPA and NMDA receptors, following the songbird data (Garst-Orozco et al, 2014; Stark and Perkel, 1999). The initial weights for all synapses were tuned to produce RA firing patterns resembling juvenile birds (Ölveczky et al, 2011), subject to constraints from direct measurements in slice recordings (Garst-Orozco et al, 2014) (see Materials and methods for details, and Figure 5B for a comparison between neural recordings and spiking in our model).…”
Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.DOI:
http://dx.doi.org/10.7554/eLife.20944.001
“…A candidate site that is likely to be central to this conversion is the robust nucleus of the archopallium (RA), which sits between HVC and the motorneurons that coordinate syringeal and respiratory activity (Vicario, 1991b). Each RA neuron receives synaptic input from multiple HVC neurons (Fee et al, 2004; Garst-Orozco et al, 2014), and spiking within this structure covaries with certain song features (Sober et al, 2008). Despite a wealth of single-unit recordings in this region during singing (Leonardo and Fee, 2005; Yu and Margoliash, 1996), an analysis linking RA spiking activity to GTE has not yet been performed.…”
SUMMARY
The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise timepoints during song production. However, the neural representation of this behavior at a population level remains elusive. We used 2-photon microscopy to monitor ensemble activity during singing, integrating across multiple trials by adopting a Bayesian inference approach to more precisely estimate burst timing. Additionally, we examined spiking and motor-related synaptic inputs using intracellular recordings during singing. With both experimental approaches, we find that premotor events do not preferentially occur at the onsets or offsets of song syllables or at specific subsyllabic motor landmarks. These results strongly support the notion that HVC projection neurons collectively exhibit a temporal sequence during singing that is uncoupled from ongoing movements.
“…Each HVC spike in the burst window was assumed to exert a postsynaptic effect lasting 5 ms (Garst-Orozco et al, 2014) and was replaced with a 5-ms postsynaptic square pulse. For each burst, a region of the song was considered covered if, across recorded renditions, at least three post-synaptic pulses overlapped in time.…”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
