Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

Abstract

Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or "practice." Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time.NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain's code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird's song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song.

Keywords: birdsong; information theory; motor performance; motor practice; temporal coding.

Graphical abstract

Figure 1.

Single-spike information about time in song is higher during directed song. A: schematic diagram of the main brain areas involved in song learning and production and corresponding areas in the mammalian brain. (AFP, anterior forebrain pathway labeled in gray; GPi, internal segment of the globus pallidus; HVC, used as a proper name; SNr, substantia nigra pars reticulata; VTA, ventral tegmental area. LMAN, RA, and X are defined in the main text.) B: spike rasters (top) and corresponding averaged firing rates smoothed with a Gaussian kernel with standard deviation (SD) = 2 ms [peri-song time histograms (PSTHs), bottom) for one LMAN neuron during directed (left) and undirected (right) singing show increased firing rate, more bursts, and more apparent noise during undirected singing. The PSTHs are in red for directed singing and blue for undirected, with the directed pattern overlaid in light red for comparison. C: two sample traces to illustrate a PSTH that carries information about time in song (upper trace) vs. a (flat) PSTH that carries no such information (second trace) The lower three traces are a schematic of the mutual information about time in song calculation, showing how the PSTH is processed to compute the average signal-to-noise (SNR) ratio across time. D: information rate from single-spike arrival times in LMAN during directed song (DIR, red) and undirected song (UNDIR, blue), measured using Eq. 1. Data are shown with a bin size, Δt = 2 ms. Lines connect data from single neurons with recordings in both conditions. Black boxes indicate the single spike information for the neuron shown in B. Triangles indicate neurons from which recordings were made during only one context. Stars and gray bars indicate the means ± standard error (SE) across all sites. **P < 0.001, ***P < 0.0001.

Figure 2.

Selection of sites for inclusion in our analysis. The number, M, of trials recorded for each site in each condition versus the number, L, of observed patterns of spiking and silence. This number, L, is always higher during undirected than during directed singing, reflecting the increased pattern entropy (see also Fig. 5B). Sites with fewer trials than observed patterns (gray area) were excluded from our analysis. Note that although L patterns is observed, the probability of these patterns is not uniform, so that we still sample quite well the more common patterns. The hard threshold on the number of trials is related to 2^S, where S is the pattern entropy. Our pattern entropy is always less than 2^N, so that our sampling is relatively stable for N < 7. The dashed line in A indicates the threshold for having twice as many trials as observed patterns. If we restrict our analysis to these best-sampled sites, our results remain qualitatively intact. As N bins increases from 5, shown in A, to 6 (B), the number of sites we use decreases from 24/28 to 15/28 (14 sites have data in both behavioral contexts).

Figure 3.

Comparison of our information estimation method with the centered Dirichlet mixture (CDM) method shows similar results. The information quantities estimated using the linear extrapolation method are plotted versus the centered Dirichlet mixture estimation method. Sites recorded during directed singing are in red and during undirected singing are in blue. On average, these values are within about 8% of each other across all sites included in our analysis. For those sites with higher linear extrapolation (LE) information, the CDM method’s prior on the sparsity of the spiking seems to be failing. Other entropy estimation methods were closer to the LE method for these sites (data not shown).

Figure 4.

Single-spike information about time in song is lower in undirected activity across different temporal resolutions. A: mutual information between spikes and time in song for directed (DIR, red) and undirected (UNDIR, blue) conditions at 2 ms temporal resolution. Symbols are as in Fig. 1. B: information per spike increases with temporal resolution in both behavioral contexts, down to about 1 ms, where information diverges due to the sparsity of spiking aligned across trials. **P < 0.001, ***P < 0.0001.

Figure 5.

Information from sequences of spikes and silence become more comparable during directed and undirected song at longer time windows. A: an illustration (above) of counting spikes in a 10-ms window and (below) the distribution of count probabilities across all sampled time windows in song, across recording sites. Higher count events, which are mostly bursts, are not as frequent during directed singing. Error bars indicated standard deviation across all neurons. B: an illustration of how spiking symbols for temporal pattern calculations are defined (top). Spike trains are binned at 2 ms resolution. Patterns are defined in a 10-ms window. Full 5-bit patterns of spiking (1) and silence (0) are retained for computing pattern information. C: the entropy of spike patterns as a function of the number (N) of 2-ms bins in the sequence, for both behavioral conditions. We plot the spike pattern “vocabulary” as measured by the entropy for T from 1 to 6 bins. Spike pattern entropy is always significantly higher during undirected singing. (below) Mutual information between spike patterns and time in song as a function of N bins, for the set of neurons included in our analysis for N = 6. The information is expressed in bits/s so that rare patterns do not contribute significantly to the information rate. Pattern information is not significantly different between behavioral contexts for N = 5 or 6. **P < 0.001; ***P < 0.0001; ns, not significant.

Figure 6.

Undirected spiking displays a greater gain in information from temporal patterns over single spikes. Pattern information compared with single-spike information averaged across all sites during directed and undirected singing. Pattern information shows a significant gain over single spike coding for time in song during practice (A), but there is no significant gain in pattern information during performance (B). *P < 0.01; n.s., not significant.

Figure 7.

Undirected spiking shows a sharp increase in pattern information as the time resolution for resolving spikes within the window increases. Mutual information between spike patterns (solid lines) or counts (dashed lines) and time in song is plotted as a function of the temporal resolution within the window. Count is relatively unaffected by increased temporal resolution, with small gains arising from the resolving of nearby spikes into counts of 2 instead of 1. Pattern information increases around 2 ms resolution within the window, as might be expected from the measurements of temporal jitter in these spike trains.

Figure 8.

Bursts during practice carry information about time in song by pointing to different parts of song, depending upon their spike pattern. A: contribution to the total pattern information from each 5-bin pattern for a particular neuron in our data set (b39b14_12). Information for undirected patterns (blue) is more spread out across patterns, whereas mostly 1-spike patterns code for time in song during directed singing (red). B–D: smoothed, average firing rates for two different lateral magnocellular nucleus of the anterior nidopallium (LMAN) neurons for single spikes (top) during undirected trials, or particular temporal patterns (middle and bottom) with two or three spikes in a 10-ms window. The black line indicates the mean firing rate, and the gray area is ±1 SD from the mean. Peri-song time histograms (PSTHs) for the same neuron during undirected and directed singing are plotted in C and D. Significant peaks in the pattern PSTHs are indicated with asterisks. The data in A are from the same neuron as in C and D. E: pattern information grouped by count for directed activity. The majority of pattern information comes from single-spike patterns. F: same as E, but for undirected activity. During undirected singing, pattern information arises predominantly from 1-, 2-, and 3-spike patterns. *P < 0.01.

Figure 9.

Directed patterns often code for the same part of song, redundantly. A: spike or pattern rate as a function of time in song. The gray area indicates the mean rate ± 1 SD. Pattern rates peak at the same parts of song. B: spike and pattern rates for a different site. Patterns 10100 and 10010 point to somewhat nonoverlapping parts of song in this neuron. *P < 0.01.

Figure 10.

Temporal coding is more prevalent in undirected song, whereas coding is more rate-like in directed song. A and B: different spike patterns carry information in the two singing conditions. Information in patterns (white bars) versus information in counts (colored bars). During directed singing(A), most of the pattern information is contained in single-spike events. About half of this information is captured by counts. In contrast, most of the information during undirected singing is carried by higher spike count patterns, and very little information is captured by counts alone (B). Error bars indicate SE across recording sites. C: information from patterns plotted versus information from count for each recording site in lateral magnocellular nucleus of the anterior nidopallium (LMAN), in both behavioral conditions. The dashed line indicates perfect rate coding, where pattern info is equal to count info. In undirected song, spike counts carry very little information, though pattern information can be as high as 0.9 bits. During directed song, pattern information is slightly larger than count information, but points follow the rate coding line. Error bars indicate ±1 SD. D: the ratio of pattern information to count information shows a significant gain in pattern information in undirected singing for n = 5 and 6. E: an illustration of spike pattern symbols that label both the count in the window, and the arrival bin of the first spike. F: summary of the contributions to total pattern information from count alone (gray portion) and from count + first-spike-in-the-bin timing (gray + hashed portion) reveals a substantial remaining amount of pure temporal sequence information (white portion) during undirected song. *P < 0.01; **P < 0.001.

Figure 11.

Count and pattern information in a 10-ms window are equivalent in a Poisson model with 10 ms resolution. The ratio of pattern to count information is plotted versus count information for Poisson spike trains with a time-varying rate smoothed to 10 ms resolution. As expected, count and patterns are equivalent for both directed and undirected models.

Figure 12.

Time-varying rate alone does not account for the observed temporal information in lateral magnocellular nucleus of the anterior nidopallium LMAN spiking. A: the ratio of pattern to count mutual information plotted versus count information for spiking during both social contexts (blue: undirected, red: directed). Undirected spiking shows a substantial gain in pattern information over count. B: a Poisson model with the same time-varying fire rate (at 2 ms resolution) as the real neurons shows a much weaker gain in pattern information for undirected spiking, and a substantially larger gain during directed singing as compared with the real data. The inset shows results of a Poisson model where the number of trials was matched to that recorded in the data for each site and behavioral context. C: Poisson model mutual information versus data mutual information for all neurons and all behavioral contexts, and for both count (x’s) and pattern (o’s) information. The Poisson model has consistently more information, particularly in temporal patterns of spiking during directed singing. Real neurons have a higher variability at some points in time during song than expected in a Poisson model, and temporal correlations in spiking events lead to higher variability in spike patterns than in the model. These effects combine to yield the higher information rates in the model, even when the mean firing rates are matched.

Figure 13.

Bursts code for time synergistically and overall synergy is higher during undirected singing. A: synergy (dotted bars) is plotted for combinations of spikes and silence versus spike count for undirected singing as in Eq. 4. Pattern information (dotted bars) is shown for comparison in the background (colored bars). During undirected singing, we observe more synergy in higher-count patterns, corresponding to bursts. B: averaging over all patterns, we plot the percent of pattern information that comes from synergy for undirected (blue) and directed (red) trials. The percent synergy increases with pattern length, and more substantially for undirected versus directed spiking. *P < 0.01; **P < 0.001.

Figure 14.

Directed patterns synergy is low and accounts for less than 20% of the total pattern information across all sites. A: synergy (dotted bars) as a function of the number of spikes in the window, for directed spiking, as in Fig. 13. Most of the information from patterns comes from slightly redundant combinations of single spikes and silence for 1-spike patterns. Remaining patterns contain some synergy. The 00000 pattern is synergistic. Long silences code for pauses between precisely timed spikes better than uninformative short (2-ms) silences, which are scattered all throughout song. B: percent synergy for N = 5 across all sites and behavioral context in our analysis. Synergy contributes a larger fraction of pattern information during practice. *P < 0.01; n.s., not significant.