1. Volume 96, Issue 6, Pages 1432-1446.e7 (December 2017)
Decoding a Decision Process in the Neuronal Population of Dorsal Premotor Cortex 
Román Rossi-Pool, Antonio Zainos, Manuel Alvarez, Jerónimo Zizumbo, José Vergara, Ranulfo Romo 
Neuron 
Volume 96, Issue 6, Pages e7 (December 2017)
DOI: /j.neuron
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2. Figure 1 Temporal Pattern Discrimination Task and Population Variance
(A) Trials’ sequence of events. The mechanical probe is lowered (pd), indenting (500 μm) the glabrous skin of one fingertip of the right restrained hand; the monkey places its free hand on an immovable key (kd). After a variable prestimulus period (2–4 s), the probe vibrates, generating one of two possible stimulus patterns (P1, either grouped [G] or extended [E]; 1 s duration); after a first delay (2 s), the second stimulus is delivered, again either of the two possible patterns (P2, either G or E; 1 s duration); after a second fixed delay (2 s) between the end of P2 and the probe up (pu), the monkey releases the key (ku) and presses with its free hand either the lateral or the medial push button (pb) to indicate whether P1 and P2 were the same (P2 = P1) or different (P2 ≠ P1). P1 and P2 always had equal mean frequency.
(B) Percentage of neurons (n = 1,574) with significant coding as a function of time. The traces refer to P1 (cyan), P2 (green), class (pink, stimulus pair combinations), and decision coding (black).
(C) Instantaneous total population variance (VarINS, blue). The time with highest population variance is labeled tMAX (orange dashed line). Population variance captured, at each time bin, by the first principal component (first PC, salmon), second PC (gray), third PC (teal), and fourth PC (light blue).
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3. Figure 2 Identifying Task Timing Signals from the Neuronal Population
In this figure, we restricted our analysis to the population of n = 462 neurons that were recorded in both the temporal pattern discrimination task (TPDT) and the light control task (LCT).
(A–E) dPCA was applied to the temporal marginalized covariance matrix obtained from the whole TPDT (−1 to 7.5 s). Population activity, sorted by class identity, was projected onto each dPC. Components were ordered by their explained total variance (ETV, Equation 8). (A) First dPC, ETV 25.1%; (B) second dPC, ETV 14.8%; (C) third dPC, ETV 10.3%; (D) fourth dPC, ETV 9.1%; (E); fifth dPC, ETV 6.4%.
(F–J). The only difference between (A)–(E) (TPDT) and (F)–(J) (LCT) was the activity used: projections were done onto exactly the same axes. Since dPCs were optimized on TPDT population activity, projections of LCT activity showed more fluctuations.
(K–O) First five temporal dPCs optimized for the LCT. (K) First dPC, ETV 30.5%; (L) second dPC, ETV 19.5%; (M) third dPC, ETV 10.6%; (N) fourth dPC, ETV 6.4%; (O) fifth dPC, ETV 4.3%.
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4. Figure 3 Decoding Task Parameters from the Population Response
Demixed principal-component analysis (dPCA) was applied to P1, P2, and decision marginalized covariance matrices obtained from the whole temporal pattern discrimination task (TPDT, −1 to 7.5 s). Population activity (n = 1,574), sorted by class identity, was projected onto each dPC and ordered by their explained total variance (ETV). Lines above traces mark time intervals where task parameters can be reliably decoded from single trials; colors as in Figure 1B.
(A and B) P1 marginalized. First two P1-dPCs: (A) first P1-dPC, ETV 7.2%; (B) second P1-dPC, ETV 3.1%.
(C) P2 marginalized. First P2-dPC, ETV 1.4%.
(D–F) Decision marginalized. First three decision-dPCs: (D) first decision-dPC, ETV 5.2%; (E) second decision-dPC, ETV 2.1%; (F) third decision-dPC, ETV 1.2%.
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5. Figure 4 Decoding Class from the Population Response
Class-dPCA was applied to the covariance matrix obtained from the temporal pattern discrimination task (TPDT, −1 to 7.5 s). Population activity (n = 1,574), sorted by class identity, was projected onto each dPC and ordered by their explained total variance (ETV). Gray line indicates tMAX.
(A) Persistent component: first class-dPC, ETV 11.2%.
(B) Transitory component: second class-dPC, ETV 5.35%.
(C) Class-coding component: third class-dPC, ETV 2.1%.
(D–F) Class-dPC weight distributions: (D) first: μ = −0.002; σ = 0.045; (E) second: μ = −0.0006; σ = 0.034; (F) third: μ = ; σ = Weights are equally distributed for positive and negative values.
(G) Central graph: for each neuron (n = 1,574) we use its first and second class-dPC weights to plot its location on the plane. Heatmap inset shows the joint weight distribution (20 × 20 grid, bin side length, corresponds to black dashed square; color scale goes from 0 to 0.035). Four extreme neurons exemplify each component: top insets are persistent neurons; bottom insets are transitory.
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6. Figure 5 Population Dynamics during the Comparison Period
PCA was applied at the time bin with highest population variance (n = 1,574, tMAX = 3.65 s, gray line). In contrast to Figures 2, 3, and 4, the covariance matrix was computed with a single time bin, tMAX.
(A–C) Population projections onto the first three PCs calculated at tMAX. Components were ordered by their explained variance (EV) at tMAX. These first two PCs were qualitatively the same than first and second class-dPCs. (A) Transitory component: first PC, EV 46.1% (ETV 5.4%; Equation 8). (B) Persistent component: second PC, EV 34.6% (ETV 11.3%). (C) P2 component, EV 19.3% (ETV 2.4%).
(D) Two-dimensional projections of the population activity: first and second PCs were used as projection axes. Trajectories, colored according to class identity, were plotted from t = 3 s (circles) to t = 4.5 s (crosses). Arrows indicate the flow of time.
(E) Cosine similarity (CS) between first PC (light red) or second PC (gray) against the first PC calculated at other time bins. Note: first PC is related to transitory coding and second PC to persistent coding.
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7. Figure 6
Decoding Persistent and Transient Components from the Population
(A–F) PCA was applied at different single time bins across the temporal pattern discrimination task (TPDT, n = 1,574). The first PC at these specific times (purple dashed lines) was used to project the responses across all time bins: (A) first PC calculated at t = 0.6 s. (B) t = 2 s. (C) t = 3.3 s. (D) t = 5 s. (E) t = 6 s. (F) t = 6.7 s.
(G and H) Mnemonic coding components. PCA was applied to the covariance matrix of each delay (averaged activity across each period). First PC was used to project the population response. (G) Stimulus-mnemonic (first delay). (H) Decision-mnemonic (second delay).
(A–H) Lines above traces mark time intervals where task parameters can be reliably decoded from single trials; colors as in Figure 1B.
(I) Cosine similarity between the two first PCs, calculated for all time bin pairs. Light red trace in Figure 5E is the slice along tMAX = 3.65 s.
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8. Figure 7 Decoding Task Parameters from Error Trials
dPCs calculated from hit trials were used to project the population activity in error trials during the temporal pattern discrimination task (TPDT, −1 to 7.5 s). Each trace, one per class, shows the average (solid line) and standard deviation (SD, shading) for 1,000 iterations of pseudo-randomly constructed error trial population activities. Lines above traces mark time intervals where task parameters can be reliably decoded from error trials; colors as in Figure 1B.
(A and B) Mean error activity projections onto first (A) and second (B) P1-dPC.
(C) First P2-dPC.
(D–F) First (D), second (E), and third (F) decision-dPCs.
(G–I) First (G), second (H), and third (I) class-dPCs. Note the reversal (with respect to Figures 3D–3F and 4A–4C) of decision coding (D–I). There is no such inversion for early P1 coding in (B) and (H).
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9. Figure 8
Persistent and Transient Components Are Stimulus Frequency Independent
Class-dPCA was applied to the covariance matrices obtained from the temporal pattern discrimination task (TPDT, −1 to 7.5 s) for blocks of trials with different mean frequencies. Activity (n = number of neurons), sorted by class, was projected onto the dPCs and ordered by their explained variance. Persistent component: first class-dPC (left). Transitory component: second class-dPC (right). (A) 3 Hz, n = 204. (B) 6 Hz, n = 265. (C) 7 Hz, n = 207. (D) 10 Hz, n = 145. (E) 15 Hz, n = 206. Note the similarity across frequencies.
Neuron  , e7DOI: ( /j.neuron )
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