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Evidence for a neural law of effect

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Science  02 Mar 2018:
Vol. 359, Issue 6379, pp. 1024-1029
DOI: 10.1126/science.aao6058
  • Fig. 1 Closed-loop BMI paradigm for pairing specific motor cortex activity patterns with phasic VTA dopaminergic activity.

    (A) Schematic of the BMI paradigm. Each mouse receives a unilateral microwire array implant in the motor cortex (targeted to layer V) and a contralateral optical fiber implant in the VTA. Recorded single units are arbitrarily assigned into two ensembles (E), and the concomitant increase (up arrow) of one ensemble’s activity and decrease (down arrow) in the other ensemble’s activity drives the decoder to change the auditory tone produced every 500 ms. The rare, lowest-pitch tone triggers phasic optical stimulation to the VTA, whereas the rare, highest-pitch tone serves as a control. Solid triangles indicate neurons with positively modulated firing rate; open triangles indicate neurons with negatively modulated firing rate. Yellow color indicates the center-pitch tone. FR, firing rate. (B) Coronal brain slice depicting viral infection specific to the dopaminergic cells of the VTA. The immunohistochemistry labels for tyrosine hydroxylase (TH, red) and the Cre-dependent fluorescent protein (YFP, yellow) are shown. (C) BMI decoder calibration. For every session (S) during the baseline period, 500 samples of 500-ms spike counts are collected from spontaneous neural activity as the mouse freely behaves in the box with no task or auditory tones. Each ensemble’s firing rate modulation is defined as the sum of the member neurons’ normalized spike counts (mean-centered, range-normalized) and then quantized into four activation states. The decoder’s state is the difference between ensemble 1’s and ensemble 2’s activation state and is mapped into one of seven tones. The stars indicate target tones. (D) BMI calibration on baseline period spontaneous neural activity results in a Gaussian-like distribution over tones, such that target 1 (5 kHz) and target 2 (19 kHz) are rare. The mean and SEM baseline distribution for each session is plotted on the left, averaged over all animals. Baseline distributions show no change from session 1, as shown on the right. (E) Ensemble 1 and 2 firing rate modulation before target 1 and target 2 hits, averaged over all recorded cells and sessions. (F) Task schematic. Trial structure is the same for target 1 and target 2, except that a target 1 hit results in phasic VTA stimulation (2-s train of 14 Hz pulses with 10-ms width). ITI, intertrial interval.

  • Fig. 2 Target pattern reentrance increases during VTA optogenetic self-stimulation.

    (A) Distribution of the percent of time that each tone was occupied during baseline (gray) and BMI (cyan) blocks of session 1 (left) and session 4 (right) in one mouse. (No tones were actually played during the baseline block.) T1, target 1; T2, target 2. (B) Quantification of the behavioral changes between sessions 1 and 4. The session 4 occupancy gain (cyan) is the session 4 BMI distribution normalized to the session 4 baseline distribution, then normalized to the session 1 ratio. For (B) to (F), the 95% confidence interval for the baseline bootstrap distribution is plotted in gray (see supplementary methods). To generate the bootstrap distribution, the BMI session was simulated 10,000 times as though neural activity were drawn from that session’s baseline period. (C) The occupancy gain over sessions 2 through 4. For (C) to (F), mean and SEM over ChR2 animals (n = 10) are shown in cyan and over YFP animals (n = 6) are shown in black. By session 4, the behavioral changes were statistically different across tones for ChR2 but not YFP [repeated measures analysis of variance (ANOVA): ChR2, F6,48 = 3.46, P = 6.4 × 10–3; YFP, F6,30 = 0.96, P = 0.47]. In session 4, 5 kHz (target 1) was significantly different from all tones from 8 to 19 kHz (Tukey’s post hoc multiple comparisons test). (D) Top: The occupancy gain for 5 kHz (target 1) over sessions is shown. Middle: ChR2 (cyan) were significantly larger than bootstrap from sessions 2 through 4 (session 2, P = 1.2 × 10–3; session 3, P < 1 × 10–5; session 4, P < 1 × 10–5). Bottom: YFP (black) were never significantly larger than bootstrap. (E) Top: The preference gain for 5 kHz (target 1) versus 19 kHz (target 2) is plotted over sessions. Middle: ChR2 (cyan) were significantly larger than bootstrap after session 1 (P < 1 × 10–5 for sessions 2 through 4). Bottom: YFP (black) were never significantly larger than bootstrap. (F) Top: The preference gain for low-pitch tones (5 to 8 kHz, close to target 1) versus high-pitch tones (12 to 19 kHz, close to target 2) over sessions is shown. Middle: ChR2 (cyan) were significantly larger than bootstrap after session 1 (P < 1 × 10–5 for sessions 2 through 4). Bottom: YFP (black) were never significantly larger than bootstrap. For (D) to (F), an asterisk indicates that the population average is significantly larger than the baseline bootstrap distribution.

  • Fig. 3 Learning correlates with an increase in covariance of the neurons that produce the target pattern.

    (A) The decoder maps spike counts in 500-ms bins into quantizations of (ensemble 1, ensemble 2) space. Neural activity can take multiple routes to achieve target 1. (B) Analysis of variance of spike counts with 100-ms bins in a 3-s window preceding target hit. “x” indicates a spike count vector at one time point. (C) Factor analysis was used to analyze the ratio of shared variance to total variance (SOT), which ranges from 0 to 1, for the full population controlling the BMI. A two-neuron illustration shows a neural solution with SOT = 0, 0.6, and 1. (D) Correlation of change in shared variance before target 1 hit (neural covariance gain) with change in preference for target 1 over target 2 (learning), over sessions 2, 3, and 4. ChR2 animals (left) showed a significant correlation [ChR2 S4: correlation coefficient (r) = 0.86, P = 6.1 × 10–3; ChR2 pool S3, S4: r = 0.71, P = 1.0 × 10–3; ChR2 pool S2, S3, S4: r = 0.62, P = 9.8 × 10–4; ChR2 S3: r = 0.60, P = 6.5 × 10–2; ChR2 S2: r = 0.62, P = 1.3 × 10–1], whereas YFP animals (right) showed no correlation (YFP pool S2, S3, S4: r = –0.14, n.s. P = 6.4 × 10–1; YFP S4: r = –0.32, P = 6.0 × 10–1; YFP S3: r = –0.69, P = 5.1 × 10–1; YFP S2: r = 0.37, P = 5.4 × 10–1). n.s., not significant. (E) SOT of direct and indirect neurons over sessions for ChR2 learners (left, n = 5), ChR2 poor learners (middle, n = 5), and YFP subjects (right, n = 5). ChR2 learners individually showed significant preference gain for target 1 versus target 2 in both sessions 3 and 4. ChR2 poor learners constitute the remaining animals who as a population showed significant target 1 occupancy gain on sessions 3 and 4. For direct neurons, ChR2 animals’ and ChR2 learners’ SOT increased from early (sessions 1 and 2 pooled) to late training (sessions 3 and 4 pooled), whereas ChR2 poor learners and YFP did not (one-sided rank sum test; ChR2, early < late, P = 1.7 × 10–2; ChR2 learners, early < late, P = 1.6 × 10–2; ChR2 poor learners, early < late, n.s. P = 2.1 × 10–1; YFP, early < late, n.s. P = 8.3 × 10–1). For indirect neurons, SOT showed no change for all groups (ChR2 learners: early < late, n.s. P = 4.3 × 10–1; ChR poor learners, early < late, n.s. P = 2.7 × 10–1; YFP, early < late, n.s. P = 7.1 × 10–1). Traces in the insets show the average of each animal’s SOT in sessions 1 and 2 (early) versus the average of sessions 3 and 4 (late). Error bars indicate mean ± SEM. The asterisk indicates that the population average is significantly larger than the baseline bootstrap distribution.

  • Fig. 4 Covariance of the neurons that produce the target pattern gradually aligns to the decoder.

    (A) Analysis of shared variance alignment with the decoder’s ensemble 1 and ensemble 2 assignments by using the angle between the shared space and the decoder’s “ensemble 1 minus ensemble 2” axis. Curved arrow indicates rotation of the shared space to align with the decoder. (B) The angle between shared variance and the decoder axis decreased for ChR2 learners (left) but not for poor learners (middle) and YFP (right) (one-sided rank sum test comparing sessions 1 and 2 to sessions 3 and 4; ChR2 learner, late < early, P = 2.8 × 10–3; ChR2 poor learner, late < early, n.s. P = 3.7 × 10–1; YFP, late < early, n.s. P = 7.5 × 10–1). Traces in the insets show the average of each animal’s angle in sessions 1 and 2 (early) versus the average of sessions 3 and 4 (late). Error bars indicate mean ± SEM. The asterisk indicates that the population average is significantly larger than the baseline bootstrap distribution.

Supplementary Materials

  • Evidence for a neural law of effect

    Vivek R. Athalye, Fernando J. Santos, Jose M. Carmena, Rui M. Costa

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S12
    • References

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