Performance variability enables adaptive plasticity ‘crystallized’ adult song
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    Performance variability enables adaptive plasticity ‘crystallized’ adult song Performance variability enables adaptive plasticity ‘crystallized’ adult song Presentation Transcript

    • Performance variability enables adaptive plasticity ‘crystallized’ 
 adult song E. C.Tumer and M. S. Brainard Nature, 2007 DIE 2014.06.03 by K. Sasahara
    • Question and Hypothesis Why residual variability exists even in the most practiced skills? Such variability is simply a ‘noise’ 
 that the nervous system is unable to control or that remains below threshold for behavioral relevance. Such variability enables trial-and-error learning, 
 in which the motor system generates variation and differentially retains behaviors that produce better outcomes. This paper experimentally tested the second possibility.
    • insight review song asonal hite-crowned nd f learning can he initial ong’, ds are ross babbling of g gradually ng’, which from one ut also begins cognizable Hatch Sensory Sensorimotor Spring Autumn Spring Crystallized Sensitive period ends Subsong Plastic song Hatch Sensorimotor Sensory Crystallized 25 60 Day 90 Sparrows Zebra finches Birdsong A complex learned behavior that requires the song system in the brain 
 for precise motor control and auditory feedback. There is a critical period for learning (i.e., only acquired early in life). M. S. Brainard and A. J. Doupe Nature 2002 t review articles rty for song learning, which involves comparing these There is as yet little evidence in the song system for dea of auditory neurons with strong suprathreshold tutorsongalone. with responses to BOS playback in anaesthetized or mals do not always show these responses when birds are ating that the strength, andperhaps the nature, of audi- es to sounds are ‘gated’ by the behavioural state of the ther sensorimotor systems, for instance locomotion in flying in insects, sensory responses related to a behav- d’ by the motor activity that generates the behaviour38 . onses are diminished unless the animal is also engaged iour. Similarly, for songbirds as for humans, auditory elf is available only when the animal is actually vocaliz- naesthesia or sleep may artificially open a gate that is erated by the act of singing. Ultimately, an understand- ural mechanisms forevaluation of auditory feedback of torequirerecordingneuralactivitywhen that feedback —thatis,duringsinging. oryareasandsensoryresponses mulus selectivity is also found in some auditory gions that provide input to the song system15,39–41 . In ehigh-levelauditoryareas(Fig.3)knownasthecaudo- riatum (NCM) and the caudal portion of the ventral m contain neurons that show more immediate early ion or neurophysiological activity in response to ongsthantoheterospecificsongs39,40 .Forthemostpart, thin these regions,unlike those within the song system, oberestrictedspecificallytoBOSortutorsongstimuli. RA DLM Syrinx HVc Area X LMAN L nXIIts Respiratory muscles NIf Dopamine neurons Figure 3 Neural substrates for learning: the song system. The motor pathway (black) is necessary for normal song production throughout life, and includes HVc (abbreviation used as proper name) and the robust nucleus of the archistriatum (RA)9 . RA projects to the tracheosyringeal portion of the hypoglossal nucleus (nXIIts), which controls the bird’s vocal organ or syrinx, and to nuclei involved in control of respiration during song7–9 . Additional nuclei afferent to HVc, including the nucleus interfacialis (NIf), are likely to be part of the motor pathway, but their role is less clear. HVc sends a second projection to the anterior forebrain pathway (AFP, red). The AFP includes Area X, which is homologous to mammalian basal ganglia19,20 , the medial nucleus of the dorsolateral thalamus (DLM), and the lateral magnocellular nucleus of the anterior neostriatum (LMAN; a frontal cortex-like nucleus). LMAN sends a projection back into to the motor pathway at the level of RA. Like basal ganglia in other vertebrates, Area X is the target of strong midbrain dopamine projections19 ;
    • Methods RESULTS A set of lightweight headphones was custom-fit to each bird in the study to generate online shifts in the pitch of auditory feedback (an example of crystallized song from one bird in our study is shown in Fig. 1a). A microphone in each bird’s cage relayed acoustic signals through sound-processing hardware capable of generating arbitrary shifts in pitch. These pitch-shifted acoustic signals were then played back through speakers in the headphones (Fig. 1b) with an average processing delay of B7 ms. Shifts in the pitch of auditory feedback and the resulting changes in the pitch of song are both measured in units of ‘cents’ (see Online Methods), where 1,200 cents corresponds to an octave and 100 cents represents the same pitch interval as a semitone (approximately a 6% change in absolute frequency). A 100-cent upward shift in pitch applied to several song syllables is shown in Figure 1c. We consistently found that shifting the pitch of auditory feedback each syllable, we quantified changes in pitch by measuring chang either the fundamental frequency or the frequency of one of the h harmonics (a harmonic feature, see Online Methods). In a tySpeakers Head Pitch shift Microphone A B C 100 ms Frequency(kHz) 1 8 A A B B C C Song output Shifted feedback a c b from an adult Bengalese finch. Spectrographic representation shows the power at each frequency (color scale) as a function of time. Three harmo features are labeled A, B and C. (b) Each bird was fit with a set of headphones that housed a pair of speakers. A microphone in the cage (s inset) provided input to online sound-processing hardware, which was us manipulate the pitch of song. Processed acoustic signals were then relay the headphone speakers via a flexible cable (not shown in photograph) an played through the speakers. (c) An upward (+100 cents) shift in the pit auditory feedback was introduced by the headphone system. For each of harmonic features labeled in a, the left spectrogram shows the bird’s aco output and the black triangle shows the frequency of the harmonic featu The right spectrogram shows the pitch-shifted auditory feedback played through the headphones and the red triangle shows the frequency of the harmonic feature in the shifted song. Black triangles are repeated next to spectrograms on the right for comparison. D B C5 10 Freq(kHz) 100 ms 0 50 100 Pitch mbaseline,cents) Mean pitch Pitch of ABCD Baseline Shift Recovery A a b Sober and Brainard, Nature Neursci 2009 wit dat dir dem var C det fre (Fi to and (Fi the day (Fi T Aft we tow a re to ally c 0.2 Baseline Escape Hit a 100 ms 5kHz a b c d e e Mean +1σ –1σ 0.4 e Baseline Downward escape Upward escape 250 ms * Hitb Escape * 5kHz Trigger time a Fundamental NATURE|Vol 450|20/27 December 2007 White noise 
 (Negative reinforcer) No noise Binary signal F0 Adult Bengalese finch (11 birds) A computerized system, in real- time, detects the target syllable and deliver white noise if the pitch is higher (or lower) than the threshold. Example of a crystallized adult song
    • Differential Reinforcement Can Adaptively Alter Features of Adult Song After at least 3 days of reinforcement, contingent w were terminated. In every case, the fundamental fre towards its original range (Fig. 2c). Hence, the nervou a representation of the initial song and both the capa to return song towards its original structure in the ab ally imposed drive. c Fundamental frequency (kHz) Fundamental frequency (z-score) Probability 0.2 0.1 0 2.2 2.3 2.2 2.3 Baseline Day 3 Escape Hit Probability d Escape Hit Probability –5 –2.5 0 2.5 5 0 0.2 0.4 Fundamental frequency (kHz) Fundamental frequency (z-score) 0.2 0.1 0 –5 –2.5 0 2.5 5 0 0.2 0.4 Probability e Baseline Day 3 Downward escape Upward escape Downward escape Upward escape f 250 ms * Hitb Escape * 5kHz Trigger time Figure 1 | Differential reinforcement can adaptively alter features of -2 -1 0 1 2 3 2.2 2.3 2.4 Time since feedback on (days) White noise on a c White noise on Baseline Morning Evening Day3 Day 1 { Lastday Adaptiveshiftinmean fundamentalfrequency(z-score) Adaptiveshiftinmean fundamentalfrequency(z-score) 0 1 2 3 –1 Lastday feedback on 0 1 2 3 –1 b Fundamental frequency(kHz) Before After F0 = 2,281± 27 Hz Variation: ~ 1% n=7 Hit rate: 91% → 17 % n=1 Even crystallized adult song has small natural variations. Hit rate was largely reduced in a few days. The negative reinforcement contingency directs either increases or decreases in pitch.
    • Adaptive Shifts in F0 Occur and Recover Rapidly and for all birds significant changes occurred within the first day (Fig. 2b, ‘evening’). The median number of syllables sung within the first half-day and full day were 605 and 1,179, respectively. By day 3, fundamental frequency stabilized at nearly asymptotic values (Fig. 2b). The induced changes in fundamental frequency recovered rapidly. After at least 3 days of reinforcement, contingent white noise bursts were terminated. In every case, the fundamental frequency reverted towards its original range (Fig. 2c). Hence, the nervous system retains a representation of the initial song and both the capacity and impetus to return song towards its original structure in the absence of extern- ally imposed drive. -2 -1 0 1 2 3 4 5 2.2 2.3 2.4 Time since feedback on (days) White noise on a c White noise on Adaptiveshiftinmean ndamentalfrequency(z-score) Adaptiveshiftinmean ndamentalfrequency(z-score) 0 1 2 3 White noise off 0 1 2 3 b Fundamental frequency(kHz) F0 approached the asymptotic range 
 within one day (7hrs = 600 songs) (a). The nervous system retains the initial song representation and the capacity to return to it. (b, c) ally imposed drive. c Fundamental frequency (kHz) Fundamental frequency (z-score) Probability 0.2 0.1 0 2.2 2.3 2.2 2.3 Baseline Day 3 Escape Hit Probability d Escape Hit Probability –5 –2.5 0 2.5 5 0 0.2 0.4 Fundamental frequency (kHz) Fundamental frequency (z-score) 0.2 0.1 0 –5 –2.5 0 2.5 5 0 0.2 0.4 Probability e Baseline Day 3 Downward escape Upward escape Downward escape Upward escape f 250 ms5kHz Trigger time Figure 1 | Differential reinforcement can adaptively alter features of adult song. a, Syllables (a, b, c, d, e, e) are normally produced with little variation. Three songs are shown for which the fundamental frequency of ‘a’ spanned 2 standard deviations of the baseline distribution (Supplementary Recording 1 contains corresponding audio files). b, White noise bursts (‘hits’) were targeted at higher pitched versions of ‘a’. c, Baseline fundamental frequency distribution for ‘a’, showing overall mean (triangle) -2 -1 0 1 2 3 4 5 2.2 2.3 2.4 Time since feedback on (days) White noise on a c White noise on Baseline Morning Evening Day3 Day 1 feedback on { Lastday Adaptiveshiftinmean fundamentalfrequency(z-score) Adaptiveshiftinmean fundamentalfrequency(z-score) 0 1 2 3 –1 Lastday feedback on Day3 feedback off White noise off 0 1 2 3 –1 b Fundamental frequency(kHz) Figure 2 | Adaptive shifts in fundamental frequency occur rapidly and recover. a, Fundamental frequency of targeted syllables for one adult bird (age 334 days). Fundamental frequency progressively increased during the n=1 n=7
    • Changes are Restricted to Targeted Song Features system was able to detect and respond precisely to that contingency. This specificity indicates an impressive capacity of the nervous system to modify discrete features of song independently. This is appropriate for vocal learning, where birds match their song to rapidly varying features of an acoustic model. If modification of one song feature generalized to cause modification of others, learning might still proceed, but such interference would probably slow its progress26 . In theory, reinforcement signals can drive learning even at long latencies to the actions that precipitate them15 . For complex motor c 1 2 3 nshiftinfeature (z-score) Target Control b Meanshiftinfundamental frequency(z-score) Mean time from target syllable (ms) a Target Mean time from target syllable (ms) –400 –300 –200 –100 0 100 200 300 400 0 2 4 6 8 10 –4 –2 0 2 4 Frequency(kHz) –400 –300 –200 –100 0 100 200 300 400 5kHz 100 ms +100 ms delay Short delay Target syllable b d Short delay +100 ms delay 0 1 2 3 Adaptiveshiftinmean ndamentalfrequency(z-score) Short delay +100 ms delay 0.2 0.4 0.6 0.8 1 Hitrate 0 3 7 15 21 25 2.2 2.3 2.4 Time (days) Fundamentalfrequency(kHz) c Feedback off Feedback on Short delay +100 ms delay Short delay Downward escape Downward escape Upward escape 28 c Fundamental frequency Duration Volume Entropy –1 0 1 2 3 Meanshiftinfeature (z-score) Target Control b Meanshiftinfundamental frequency(z-score) Mean time from target syllable (ms) –4 –2 0 2 4 –400 –300 –200 –100 0 100 200 300 400 Figure 3 | Changesarerestrictedtotargetedfeaturesofsong. a,Spectrogram illustrating analysed features for target (red bar) and control (blue bars) syllables of an individual experiment. b, Mean changes in fundamental frequency for target (red) and control (blue) syllables. Squares represent two experiments for song illustrated in a. Data from 3 additional birds (circles, triangles, diamonds) are shown without corresponding spectrograms. Filled and open symbols indicate experiments with upward and downward shifts in fundamental frequency, respectively. c, Spectral characteristics other than fundamental frequencywerenotalteredforeither target (red) or control(blue) syllables. Bars indicate mean 6 standard deviation. d Fig a, b (b) dev shi del usi dev Sym rein 1242 Nature©2007 Publish The nervous system can: detect and respond to the imposed contingency: Higher/lower pitch→white noise modify song features independently
    • Delayed Feedback Prevents Adaptive 
 Pitch Shifts Short delay (< 30ms)→Adaptive pitch shifts Long delay (+100ms)→No systematic changes Normally predictable timing is between premotor activity and sensory consequence 
 (< 70ms) altering f larger changes argeted en they g. 3a, b, o fun- s dura- ugh the nd feed- nervous ngency. nervous This is ong to tion of earning slow its at long motor skills, however, the nervous system might detect the contingency more effectively at shorter delays. We tested the importance of delay by varying the time between measurement of fundamental frequency 400 5kHz 100 ms +100 ms delay Target syllable Short delay Target syllable a b Detect time Trigger time d Short delay +100 ms delay Short delay +100 ms delay 0 3 7 15 21 25 2.2 2.3 2.4 Time (days) Fundamentalfrequency(kHz) c Feedback off Feedback on Short delay +100 ms delay Short delay Downward escape Downward escape Upward escape 28 NATURE|Vol 450|20/27 December 2007 In theory, reinforcement signals can drive learning even at long latencies to the actions that precipitate them15 . For complex motor c Fundamental frequency Duration Volume Entropy –1 0 1 2 3 Meanshiftinfeature (z-score) Target Control b Meanshiftinfundamental frequency(z-score) Mean time from target syllable (ms) a Target Mean time from target syllable (ms) –400 –300 –200 –100 0 100 200 300 400 0 2 4 6 8 10 –4 –2 0 2 4 Frequency(kHz) –400 –300 –200 –100 0 100 200 300 400 Figure 3 | Changesarerestrictedtotargetedfeaturesofsong. a,Spectrogram 5kHz 100 ms +100 ms delay d Short delay +100 ms delay –1 0 1 2 3 Adaptiveshiftinmean fundamentalfrequency(z-score) Baseline Day3 Day5 Baseline Day3 Day5 Day8 Day12 Short delay +100 ms delay 0 0.2 0.4 0.6 0.8 1 Hitrate Baseline Day3 Day5 Baseline Day3 Day5 Day8 Day12 0 3 7 15 21 25 2.2 2.3 2.4 Time (days) Fundamentalfrequency(kHz) c Feedback off Feedback on Short delay +100 ms delay Short delay Downward escape Downward escape Upward escape 28 Figure 4 | Delayed feedback prevents adaptive pitch shifts. a, b, Spectrograms illustrating short delay (a) and 1100 ms delay
    • Incremental Adjustment of Threshold Drives Large Pitch Changes in ed se er ’). p- es, ge d). es. tic ite ry ed ble cal ng es; nt us en n- lly of on ult se 25 . on he nt in ig. re el- internally by an efference copy of premotor activity17 . Consistent with this possibility, studies in both birds and mammals indicate that some movement variability derives from central neural activity rather than the periphery7,8,29,30 , suggesting that variability may be actively generated for motor exploration. Regardless of mechanism, our data indicate that natural variations present in crystallized adult song are not simply noise but rather can be exploited for trial-and-error c –2 0 2 4 6 8 10 12 2.3 2.4 2.5 2.6 Time since feedback onset (days) Fundamentalfrequency(kHz) 2.3 2.4 2.5 2.6 2.7 0 0.1 0.2 Fundamental frequency (kHz) Probability White noise a b 100 ms 2.5kHz Baseline Day 13Day 13 0.3 Baseline LETTERS Video from supplemental materials n=1
    • (Cont.) ult se 25 . on he nt in ig. re el- e- ld 5). tal ge ry ng e- b- me nt o- o’ als to a- an se in d- ng 18 . n- c d 2.3 2.4 2.5 2.6 2.7 0 0.1 Fundamental frequency (kHz) Prob 100 ms 2.5kHz –15 –10 –5 0 5 10 15 0 0.1 0.2 0.3 Probability Fundamental frequency (z-score) Baseline Last day –15 –10 –5 0 5 10 15 0 0.1 0.2 0.3 Probability Fundamental frequency (z-score) Figure 5 | Incremental adjustment of threshold drives large pitch changes. a, Changes to fundamental frequency for one experiment. Points indicate mean (6 standard deviation) on each day. Shading indicates threshold for Large changes in pitch is achieved by an incremental differential reinforcement.
 Current performance is surrounded by a ‘halo’ of variation that enables trial-and-error exploration for continuous adaptive modification. n=3 Fixed case
    • Summary Reinforcement with a binary feedback can direct precise, adaptive changes to crystallized adult song. Residual variability in well learned skills is not entirely noise but rather reflects meaningful motor exploration that can support continuous learning and optimization of performance. Graphon, Nature Neursci 2008