Stability of motor cortex network states during learning-associated neural reorganizations

Abstract

A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

Keywords: brain state; criticality; motor learning; neuronal avalanches; two-photon calcium imaging.

Fig. 1.

Does cortical population activity deviate from the critical network state while neural circuits undergo massive reorganizations during motor learning? A: traces of lever position in multiple trials from one representative mouse. Gray, individual trials; black, average of all trials; red, onset of the movement; left: one session from naive stage; middle: one session from middle learning stage; right: one session from expert stage. B: schematic of a recurrent neural circuit consisting of neurons (gray), connections (black), neural representation of movement (letters: P for position, v̄ for velocity, v for speed) and network state (background color). Question marks mean the network states are unknown. C: neuronal avalanches (gray) are contiguous bouts of spikes (black raster) across the population of neurons. The spike count within an avalanche determines the avalanche size. D: the shape of the avalanche size distribution reflects the level of spatiotemporal correlation within the network. A power law avalanche size distribution is a characteristic feature of the critical network state. Question mark means whether the network state is critical state is unclear. PDF, probability density function; S, avalanche size.

Fig. 2.

Imaging and quantifying the neural population activity in motor cortex while mice improve on a quantifiable behavior. A: experimental paradigm with lever-press task and chronic imaging of neural activity. B: GCaMP5G expression in layer 2/3 neurons of M1. C: fluorescent signal (ΔF/F₀) for one representative neuron, the corresponding calcium events (CaEvent), inferred spike probability (ISP), and inferred spike rates (ISR). ITI, intertrial interval.

Fig. 3.

Neural activity reorganizes during motor learning. A: the time-average inferred spike rates (color coded, see vertical scale bar) are shown for each recorded neuron across 14 learning sessions. The ranking of the inferred spike rates (ISR) in session 1 determines the order of neurons for all sessions. Representative data are shown for M1 L2/3, M1 L5, M2 L2/3, and M2 L5. Qualitatively similar reorganizations are observed for all mice tested [see Supplemental Fig. S1 (https://figshare.com/s/9245c5019517cf3912ec)]. B: pyramidal neurons (M1 L2/3) were clustered by their functional correlation (Pearson correlation), only 50 neurons with top correlations were shown here for visualization. C: cross correlation between the cross correlations of adjacent sessions [see violin plots in Supplemental Fig. S2 (https://figshare.com/s/9245c5019517cf3912ec)]. Errors bars are standard errors. D: significance test (t test) on correlations between correlations of adjacent sessions for naive stage (first day), middle stage (days 5 to 7) and expert stage (last 3 days). *P < 0.05; **P < 0.01; ***P < 0.001. Error bars are SE [see ANOVA test in Supplemental Fig. S2 (https://figshare.com/s/9245c5019517cf3912ec)].

Fig. 4.

The relation between neural activity and lever position reorganizes. A: the “peak movement position” for a single neuron is the lever position where the across-trial average inferred spike probability peaks and is significantly above noise. Each curve represents the normalized trial-averaged ISP of neuron. Two adjacent sessions are shown. The “difference of peak position” is marked by the dashed lines. B, top: histogram of the ”difference of peak” from session 1 to session 2. Bottom: histogram of the difference of peak from session 13 to session 14. We classified neurons for which difference of peak is smaller than a certain threshold as “neurons with consistent peak position” (within the fuchsia dashed box). C: fraction of neurons with consistent peak position for sessions with different intervals of first week (orange) and second week (cyan). Transparent curves: fractions with cells shuffled. Error bars are SE.

Fig. 5.

The relations between single-neuron activity and aspects of lever movement drift across sessions. A: lever position, velocity, and speed traces in multiple trials from a representative mouse. Gray, individual trials; black, average of all trials; blue, one example trace for lever position; green, the corresponding example trace for lever velocity; purple, the corresponding example trace for lever speed; left: one session from naive stage; right: one session from expert stage. Bottom: example trace of single neuron activity (ΔF/F₀) above for one trial. B: the significant correlations (Corr) between ΔF/F₀ and the aspects of lever movement indicate a relation between single neuron activity and lever position, velocity, speed, or multiple lever movement aspects for a fraction of the recorded neurons (session 2 from one example animal; M1 L2/3). Open circles indicate neurons with no significant correlation of their activity and the lever movement. C: for each session and neuron, the significant correlation of the neuron activity with aspects of lever movement are indicated by the corresponding color as defined in B. White indicates no significant correlation. Neurons are sorted in session 1 and this order remains fixed for the display of all other sessions, thus revealing the drift in relations across sessions. Neurons are from the same imaging field as B. D: correlation of representation across adjacent sessions versus sessions for all n = 30 imaging fields. Representations were assigned numerical values to compute correlations (see methods). Red curves, experimental; pink curves, shuffled. Error bars are SE. E: according to two-sample t test, the adjacent correlation of neural representation was significantly smaller (P < 0.05) for the naive stage (sessions 1 and 2) compared with middle (sessions 5–7) and expert (sessions 11–14) stages [see ANOVA test in Supplemental Fig. S3 (https://figshare.com/s/9245c5019517cf3912ec)]. *P < 0.05; **P < 0.01.

Fig. 6.

Motor cortex network states remain stable with respect to criticality during neural activity reorganization in L2/3. A: methods on neuron avalanche analysis. Top: inferred spike probability (ISP) for one representative neuron. Spike probability was inferred from calcium fluorescent signals using a spike inference algorithm. Spike probability was then thresholded (dashed red line) to a level of 3 SDs above 0, and converted to 1 (active) or 0 (inactive). Middle: raster plot of activity constructed using the thresholded spike probability. Each row represents single neuron, and each mark represents the inferred spiking activity of that neuron, i.e., the thresholded spike probability with value 1 (active). Bottom: “Network activity” (black) is the sum of all spiking neurons. A threshold (dashed red line) at median network activity defines the start and end of a “neuronal avalanche” as the time points of crossing this threshold. The avalanche size (S, yellow) is the integrated network activity for the avalanche duration (D), i.e., the time between threshold crossings. B and C: probability density functions (PDF, purple dots) of avalanche sizes and durations for M1 L2/3 in session 1 (B) and M1 L2/3 in session 14 (from same animal) (C) followed power laws with exponent τ for size and α for duration distribution. Purple dots are distributions based on empirical data, only filled dots were fitted to power laws (black lines). We shuffled spikes for each neuron separately and got the distribution for shuffled spikes (dashed gray line) for comparison. D and E: exponents for avalanche size (τ) and duration (α) distributions of all subjects and sessions for M1 L2/3 (D) and M2 L2/3 (E). The gray scale of the symbols decreases from dark to light with increasing session number. F and G: The network state quantified by κ (size distribution only). When κ is close to 1, the distribution is close to power law, otherwise, it’s not. Here the κ for M1 L2/3 (F) and M2 L2/3 (G) are close to 1. For clarity of visualization of the many overlapping points for each session, we jittered the points laterally.

Fig. 7.

Neuronal avalanche scaling relation and shape collapse analyses support the notion that criticality underlies the observed power laws in L2/3. A: the linear relationship on logarithmic axes revealed a power law relationship <S>∼D^β between average avalanche size (S) and duration (D) as predicted by criticality theory. Scaling exponent predicted from critical theory (${\beta }_{\text{pred}}=\frac{\alpha -1}{\tau -1}$, solid line) was close to the exponent fitted with experimental data (purple dots) (M1 L2/3 session 14; same animal as Fig. 6). B and C: the predicted scaling exponents and fitted scaling exponents for M1 L2/3 (7 animals) (B) and M2 L2/3 (8 animals) (C). Gray scale changes from dark to light with increasing session number. Yellow shades indicate the criterium for consistency with criticality, i.e., an exponent difference of less than 0.2 (see Ma et al. 2019).Consistency with this criticality criterium is seen in 91/96 samples in M1 L2/3 and in 60/75 samples in M2 L2/3. D and E: fitted scaling exponents (β_fit) on each single session for M1 L2/3 (D) and M2 L2/3 (E). Colors denote different subjects. F: in experimental data, scaled avalanches across durations (gray, avalanches with duration from 3 to 10 frames) show little error around the polynomial fit (red). We show one example from M1 L2/3 session 2 of a single animal. G: shuffled data (gray) have no characteristic shape and are characterized by larger error around the fit (red). Shuffled data did not accomplish shape collapse. H and I: the variance-based errors for M1 L2/3 (H) and M2 L2/3 (I) are below 0.01 (black curves, averages; colored squares, samples). In contrast, the variance-based errors for shuffled data (gray line and squares) are above 0.01. Error bars are standard errors. The two curves are significantly different on each single session (P < 0.05).

Fig. 8.

Motor cortex network states remain stable with respect to criticality during neural activity reorganization in L5. A: probability density functions (PDF) for avalanche sizes (S) and durations (D) for M2 L5 session 1 do not follow power laws, and the scaling relates fails. B: similar as A, for M1 L5 session 11 (same animal). C: κ values for M1 L5 (size distribution) were far away from 1. For clarity of visualization of the many overlapping points for each session, we jittered the points laterally. D: similar as C, for M2 L5. E: in experimental data for M1 L5, scaled avalanches across durations (gray) do not accomplish shape collapse (duration range 3 to 10). F and G: the variance-based error (black curve and colored squares) in M1 L5 (F) and M2 L5 (G) are not significantly different (t test, P > 0.05 for 21 pairs out of 28) from shuffled data (gray line and squares).