Evidence for a causal inverse model in an avian cortico-basal ganglia circuit

Abstract

Learning by imitation is fundamental to both communication and social behavior and requires the conversion of complex, nonlinear sensory codes for perception into similarly complex motor codes for generating action. To understand the neural substrates underlying this conversion, we study sensorimotor transformations in songbird cortical output neurons of a basal-ganglia pathway involved in song learning. Despite the complexity of sensory and motor codes, we find a simple, temporally specific, causal correspondence between them. Sensory neural responses to song playback mirror motor-related activity recorded during singing, with a temporal offset of roughly 40 ms, in agreement with short feedback loop delays estimated using electrical and auditory stimulation. Such matching of mirroring offsets and loop delays is consistent with a recent Hebbian theory of motor learning and suggests that cortico-basal ganglia pathways could support motor control via causal inverse models that can invert the rich correspondence between motor exploration and sensory feedback.

Keywords: Hebbian learning; lateral magnocellular nucleus of the anterior nidopallium; mirror neuron.

Fig. 1.

Three hypothetical sensorimotor mappings and associated mirroring offsets. Sensory-to-motor mappings could implement a causal inverse of the motor plan (A), a predictive inverse (B), or be random (C). Under a causal inverse, generated by variable sequences of song features (ABC-CBA), a spike burst in a motor neuron (neuron 2) triggers the production (black arrow) of a song feature (feature B) after latency , and the neuron receives sensory feedback (thick green arrow) from that same feature after an additional latency . In such a neuron, we expect to see a cross-covariance (CC) peak (red arrow) between singing-related and playback-evoked spike bursts (black vertical bars) at a time lag (the so-called mirroring offset, red horizontal bar) given by the delay of the sensorimotor loop . Under a predictive inverse (B), generated by stereotyped sequences of song features (ABC-ABC), the motor neuron 2 again triggers song feature B, but at the same time receives reliable feedback from the previous song feature A (thick green arrow). Thus, we expect to see a CC peak at a time lag much smaller than the sensorimotor loop delay . Finally, under a random sensory-to-motor mapping (C), we expected no CC between the motor- and sensory-evoked firing.

Fig. 2.

LMAN sensorimotor loop delay. (A) Sagittal schematic of the songbird brain. Both HVC and the LMAN project to the premotor RA. DLM, dorsal lateral nucleus of the medial thalamus; nXIIts, hypoglossal nucleus. (B) Electrical stimulation in LMAN using paired 0.2-ms current pulses of 500 µA (separated by 1 ms) during song leads to transient distortions of song syllables (brief pitch decrease, red square bracket) compared with catch trials. (Top) Log-power sound spectrograms (high and low power shown in yellow and black, respectively) of a nonstimulated (catch) syllable and a stimulated (stim) syllable. A stack plot of frequency modulation (FM; Middle) and the mean FM (Bottom) across 488 nonstimulated syllables (catch trials) and 454 stimulated syllables (Stim) reveals a transient FM increase corresponding to a brief pitch decrease (white square bracket in the sound spectrogram) roughly 20 ms (dashed red line) after stimulation onset (time origin, thick red line). (C) Log-power sound spectrogram (Top), raster plot (Middle), and mean firing rate (Bottom) of a LMAN single unit with short auditory latency of 18 ms to playback onset of the bird’s own song (n = 307 playbacks).

Fig. 3.

LMAN mirroring offset. (A) Large positive mirroring offset in an LMAN single unit. (A, i) Song oscillogram (Upper) and raw extracellular voltage trace of neural activity (Lower; Inset shows a spike burst). (A, ii) Song spectrogram of an example song bout, a song motif (marked by a red horizontal bar). The spike raster plot (Lower) shows spikes generated during production of that bout (blue rasters) and during 27 playbacks of that bout (black rasters). Firing-rate curves (Lower) are plotted in corresponding colors. (A, iii) Summary showing spike rasters during production (blue) and during playback (black) of different song motifs (delimited by vertical red lines); the firing-rate curves below are averages over all motifs (not all shown). Song-evoked firing tends to lead playback-evoked firing, in particular at the end of the motif. (A, iv) The CC function (thick red curve) of motif-related spike trains peaks at a time lag of about 50 ms. (B) The average (normalized) motif CC function (red curve) peaks at a time lag near 40 ms and exceeds there a significance threshold (black curves) of +3 Jackknife SDs (n = 50 sites in seven birds). (C) A similar behavior is seen in the population-averaged (and normalized) bout CC function (n = 48 sites in seven birds, red curve). A population-averaged random shift predictor (red dotted curve) remains below the 3 Jackknife significance threshold (black curves).