doi: 10.1038/s41586-019-1787-x.
Epub 2019 Nov 27.
Distributed coding of choice, action and engagement across the mouse brain
Affiliations
- PMID: 31776518
- PMCID: PMC6913580
- DOI: 10.1038/s41586-019-1787-x
Item in Clipboard
Distributed coding of choice, action and engagement across the mouse brain
Nature.
2019 Dec.
Abstract
Vision, choice, action and behavioural engagement arise from neuronal activity that may be distributed across brain regions. Here we delineate the spatial distribution of neurons underlying these processes. We used Neuropixels probes1,2 to record from approximately 30,000 neurons in 42 brain regions of mice performing a visual discrimination task3. Neurons in nearly all regions responded non-specifically when the mouse initiated an action. By contrast, neurons encoding visual stimuli and upcoming choices occupied restricted regions in the neocortex, basal ganglia and midbrain. Choice signals were rare and emerged with indistinguishable timing across regions. Midbrain neurons were activated before contralateral choices and were suppressed before ipsilateral choices, whereas forebrain neurons could prefer either side. Brain-wide pre-stimulus activity predicted engagement in individual trials and in the overall task, with enhanced subcortical but suppressed neocortical activity during engagement. These results reveal organizing principles for the distribution of neurons encoding behaviourally relevant variables across the mouse brain.
Conflict of interest statement
The authors declare no competing interests.
Figures
a, Psychometric curves for mouse Cori, showing the probability of choosing Left (blue), Right (red) or NoGo (purple) as a function of stimulus contrast. Panels are grouped by pedestal contrast on each row, corresponding to subsets of trials with different minimum contrast on the left and right screens. The horizontal axis encodes the relative contrast from the pedestal value, positive numbers indicating higher contrast on the right screen, and negative numbers for higher contrast on the left screen (e.g. at pedestal=50%, a ΔContrast of +50% corresponds to trials with 50% contrast on the left screen and 100% contrast on the right screen). Dots and lines indicate the empirical fraction of choices made and 95% binomial confidence intervals for the fraction estimate, pooling data over sessions. Solid lines indicate the fit of a multinomial logistic model: where cL and cR are the contrast on the left and right, and parameters bL, sL, n, bR, sR are fit by maximum likelihood estimation to the data for each subject. b-j, As in a, for the remaining subjects. k, The model fit for all subjects overlaid, for Left choices (blue) and Right choices (orange), in both cases for pedestal=0%. l, Summary of performance on high-contrast trials. Dots reflects the session-pooled proportion correct of each mouse for trials with 100% versus 0% contrast, with 95% binomial confidence interval. m, Example segment of wheel position data showing wheel movements detected as left turns (blue), right turns (orange), or incidental movements (black). Detected onsets (green circles) and offsets (red circles) marked for each movement. Y-axis scale: distance moved at the circumference of the wheel (i.e. 2πRθ where R is wheel radius and θ its angular position). n, Wheel velocity trace for the same segment of data as in a. o, Example wheel turns aligned to the detected onset time. Dashed box indicates the region expanded in d. p, Example wheel turns aligned to detected onset time, zoomed to show the moment of takeoff, illustrating that the wheel had moved by less than 0.5mm by onset. The step-like appearance of the trace reflects the resolution of the rotary encoder (each step unit is 0.135 mm at the surface of the wheel). q, Decoding the eventual direction of the wheel movement using the instantaneous velocity at different times relative to detected movement onset reveals that the direction only starts to be decodable around 20 ms prior to detected onset, and is not reliably (>80%) decoded until the time of onset. Error bars represent s.d. across sessions (n=39).
a, Prior to insertion, probes are dipped in DiI. The brain is sliced and imaged, and locations of each probe’s DiI spots are manually identified on the Allen CCF atlas (15.1 ± 6.9 per probe). When multiple penetrations were performed in a single brain, their tracks are sufficiently far apart to avoid confusion. b, A vector is fit to the probe track using total least squares linear regression. The median distance of individual points from this vector is 39.3 μm, providing an estimate of lateral displacement error. c, To fit the longitudinal mapping from recording sites to brain locations, we used landmarks easily detectable by their electrophysiological signatures (arrows, left), linearly interpolating the location of sites between these landmarks. d, visual receptive fields served as a post-hoc check on correct alignment, but were not used to estimate track location. Each horizontally elongated plot with two vertical black lines indicates the responsiveness of all spikes recorded in an 80 μm depth bin to flashed white squares at varying locations on the three screens (see Methods, receptive field mapping). Colormap brightness is proportional to spike rate, independently scaled for each map. e, areas assigned for each recording site. Right: example DiI traces in slices corresponding to these locations. f, Example of cross-validation procedure to assess error in longitudinal alignment. For each point, the longitudinal mapping was recomputed excluding this point, and the distance from this point to the mapping fit to other points provides an estimate of longitudinal alignment error. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
Visual inspection of the DiI tracks confirms that the probe indeed passed through that region at some point along the recording span. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
a, Activity of example neurons in VISp and VISam, showing the neuron’s waveform and anatomical location (top), rasters sorted by contralateral contrast (middle), and trial-averaged firing rates (smoothed with 30 ms causal half-Gaussian) for each of the four contralateral contrasts (bottom). Shaded regions: +/- s.e. across trials. b, Colormap showing trial-averaged firing rates of all highly-activated neurons (p<10-4 compared to pre-trial activity), vertically sorted by firing latency. Latency sorting was cross-validated: latencies for each neuron were determined from odd-numbered trials, and activity from even-numbered trials is depicted in the plot. Gray scale represents average normalized firing rate across even-numbered trials with contralateral visual stimuli and movement. c-e, Curves showing mean firing rate across responsive neurons in each area, aligned to visual stimulus onset (c), movement onset (d), or reward onset (e). Shaded regions: ± s.e. across neurons. f, The focality index, defined as where pa is the proportion of neurons in area a selective for the kernel in question, measures how widely versus focally distributed a representation is, with a floor of 0.0238 for a uniform distribution (across 42 brain regions) and a max of 1.0 if all selective neurons were found in a single brain region. This focality index was 0.079 for choice, 0.069 for visual kernels and 0.040 for action kernels; the differences between Choice and Move, as well as Contralateral Vision and Action, were statistically significant (p<0.05; bias-corrected bootstrap). Dots represent the true value and error bars represent bias-corrected bootstrap-estimated 95% confidence intervals. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
a, Example fit of spiking data for an individual neuron with the kernel model. Green trace shows spike data smoothed with a causal filter, black shows the model’s prediction, and other colored traces show the components of the prediction from each kernel. Data between trials is omitted from the fitting and from this plot. b, Relationship between Move and Choice kernels, which together combine to give rise to arbitrary firing rate shapes for Left and Right choice trials. c, Cartoon of the three methods evaluated. In the Toeplitz and Cosine models, a predictor matrix X of size Ntimepoints × Npredictors is constructed from task events (illustrated, transposed, in panel c). A linear fit from predictors X to spike counts Y is estimated using elastic net regularization. In the reduced rank regression method, the predictor matrix X is the same as the Toeplitz model, but predicts Y after passing through a low-rank bottleneck (X*b), which is optimized using reduced rank regression. d, The structure of predictor matrices. The Toeplitz predictor has rows for each variable and time offset, which take non-zero values for time points (columns) corresponding to the appropriate time offset from the given event. The cosine model has similar structure but with rows replaced by smooth raised cosine functions, allowing a smaller number of basis functions. The reduced rank regression model has learned a small number of dense basis functions optimized to predict spike counts. e, Density scatterplot of cross-validated variance explained for each neuron under the Toeplitz model against the reduced rank model (upper), and for the cosine model versus the reduced rank model (lower). Each point represents one cell, colored to show density when they overlap. Plots at right are zooms on the densest region of the plots. These comparisons show that the reduced rank model consistently outperforms the other two (points lie below the diagonal), and that it overfits fewer neurons (fewer points with c.v. var expl < 0). f, The proportion of overfit explained variance, i.e. (CVtrain – CVtest)/CVtrain where CVtrain is the train set variance explained and CVtest is the test set variance explained. Smaller values for reduced rank model show it overfits less. g, Left: Population decoding of contralateral visual stimulus contrast from residual population activity in each area, after fitting a model including all other kernels. Subsequent three panels depict the same analysis for decoding of ipsilateral visual stimulus contrast; action; and direction of choice. h, Distributions of the cross-validated proportion variance explained for each neuron when shuffling the trial choice labels (left and right) versus that from the original data. A small number (14, 0.33%) of neurons are false positives in this shuffle test. Dashed line represents the 2% C.V. Variance Explained threshold employed. Y-axis is clipped.
a, The unique contribution of each predictor variable as assessed by nested prediciton. Each panel depicts the distribution of variance explained across neurons of a single brain region, using various reduced-rank kernel regression models, (c.f. Figure 3c,e and Figure 4b). Each bar shows the 10th, 25th, 50th, 75th, and 90th percentiles of the distribution for a single prediction model, color-coded by model identity. The numbers in the subplot title indicate the number of all neurons analyzed with the full model (i.e. the distribution shown with the grey bar), and the number of neurons included for nested model analysis (i.e. cells with ≥ 2% variance explained with the full model). Black bar shows distribution of variance explained by the full model in this subset; colored bars show the unique contribution of each predictor. Note that the unique contributions need not sum to the variance of the full model, as predictor variables are correlated. Variance explained by the Action kernel (yellow) is essentially global, whereas Contralateral Vision variance explained is distinctly restricted, and Choice is rare enough to be difficult to see in these plots. b, Population average firing rates across neurons for each brain region in Go, Miss, and Passive trials, selected to have matched contralateral visual stimulus contrasts. The patterns characteristic of engagement can be seen in pre-stimulus activity (i.e. before time 0): the pre-stimulus firing rate of midbrain, basal ganglia, and hippocampal regions is in the order Passive < Miss < Go; whereas pre-stimulus activity in neocortical areas instead is arranged as Passive > Miss and Go. Thalamic regions can exhibit either pattern; notably, visual thalamic regions (LGd, LP, LD) follow the pattern of neocortical areas.
a, The percentage of neurons with significant CP (i.e. neurons whose rate differed significantly between Left and Right Choices in response to the same stimulus; left two columns) and DP (i.e. neurons whose rate differed significantly between Go and NoGo trials in response to the same stimulus; right two columns), computed with the combined-conditions Choice Probability (ccCP) analysis, as a function of time aligned to visual stimulus onset (left) and movement onset (right). Horizontal dashed line represents the value expected by chance given the statistical threshold alpha=0.05. b, Percentage of neurons in each area with significant Detect Probability measured around movement onset, replicating the finding from Fig 2d,e and Fig 3e that non-selective action signals are distributed widely. * indicates that the 95% confidence intervals of the proportion did not include the proportion expected by chance (5%, horizontal dashed line). c, As in b, for Choice Probability in the same window. Though the number of trials usable in this analysis is limited and some sessions (n=6 of 39) had to be excluded for this reason, this analysis broadly replicates the finding from Fig 4b that choice selective neurons, around the time of movement onset, are restricted to frontal cortex, basal ganglia, midbrain, and certain thalamic nuclei. d, As in a, for Choice Probability in a later window well after movement onset, showing that choice-related signals are distributed more widely after movement onset, including visual and parietal cortex. These signals are too late to have participated in generating the choice. As behavior during this period is relatively un-constrained – unlike in the pre-movement period otherwise studied in this paper – and subjects may have employed diverse motor strategies, this analysis should be interpreted with caution. e, The percentage of neurons with significant Choice Probability, as a function of time relative to movement onset for selected areas (zoom and overlay of certain traces from a), replicating that choice related activity arises in the final 50-100 ms relative to movement onset and with similar timing across multiple areas. Note that six sessions were excluded from CP analysis for having too few trials; these six sessions included 20.8% of the MOs neurons determined to have choice-selective responses with kernel regression. f, The pre-stimulus Detect Probability (after subtracting 0.5, so that positive values indicate higher rates on Go trials, and negative values the reverse) versus the mean Go-Miss firing rate difference for each area (used in Fig 5d), demonstrating that these two quantities identify essentially the same factor. The pre-stimulus Detect Probability was correlated with the Engagement Index (i.e. Task-Passive difference) similarly to the Go-Miss difference (with r = 0.48, p=0.001; not shown).
a, Canonical correlation analysis is applied to firing rates at every pair of timepoints relative to a behavioral event (illustration shows 0.1s after stimulus onset in VISp and 0.15s after in MOs). Canonical correlation analysis is applied to two matrices containing each cell’s firing rate on each training set trial (90% of the total) to find dimensions in each population maximally correlated with each other. (For regularlization purposes, this is applied after dimensionality reduction using PCA). The strength of population correlation is summarized by the correlation of test set activity projected onto the first canonical dimension. b, Results on an example session, showing relationships between visual cortex, midbrain, and frontal cortex relative to stimulus onset (top) and movement onset (bottom). Visual cortical activity leads frontal and midbrain activity, as can be seen from the below-diagonal bias in correlations. However, no lead/lag relationship is seen between midbrain and frontal cortex. Gray: p>0.05. c, Average across all recording sessions that contained each pair of areas, showing similar relationships to the example in each case. d, Summary of lead-lag interactions, obtained by subtracting the averages of the jPECC coefficients over inter-area time ranges of -50 to 0 and 0 to 50ms, as a function of time relative to events. Gray region: 2*s.e. across experiments. Visual cortex reliably leads frontal cortex and midbrain at around 100ms after the stimulus; and over a range -200 to -50ms relative to movement.
a, A nested ANOVA with factors of session and subject was used to assess statistical significance of pre-stimulus task-passive firing rate differences (here normalized, unlike Fig. 5c-d and Extended Data Fig. 6b, to meet statistical assumptions) in each brain region (see Methods). All non-neocortical regions that showed a significant difference between engaged task and passive states had higher mean pre-stimulus firing rates in task context, except for visual thalamus. All neocortical regions that showed a significant difference between task and passive contexts had lower mean pre-stimulus firing rates in the task context. b, An engagement index was computed for each trial by projecting population activity onto the vector of differences between pre-stimulus activity in task and passive contexts. Histogram shows the distributions of this index over contrast-matched Miss and Go trials; p-value computed by t-test. c-f, Same plot after restricting to contrast-matched trials following rewards (c); after removing the reward effect by partial regression (i.e. by subtracting the mean within trials of each previous reward condition) (d); after restricting to contrast-matched trials following short inter-trial intervals (e) or after restricting to contrast-matched trials following long inter-trial intervals (f). The effect persists in each case. g, Histogram of pupil areas in the pre-stimulus period after previous trials that were rewarded or non-rewarded, showing the expected effect of reward on arousal as a positive control for the validity of pupil diameter measurements. h, To initiate the next trial, subjects must hold the wheel still for 500ms; video analysis shows they reduce other movements as well. Top, Total video motion energy (mean-square frame difference) as a function of time relative to stimulus onset, on each trial, for an example recording. Bottom, mean motion energy across these trials overlaid (red; shaded region represents s.e.m. across trials). Inset, example frame from video monitoring the face and arms of the mouse. i. Results of a logistic generalized linear model (GLM) predicting the probability of a Go response on the subsequent trial from each of the given variables; plot format as in Fig 5e. The null hypothesis that engagement index had no additional effect on Go probability over all other variables was rejected using a deviance test (p=1.5e-8). Each panel’s curve shows effect of one individual variable on Go probability. Red points: mean Go probability averaged over a bin, red error bars, 95% confidence interval. Black line, fit of GLM, setting all other variables to their mean; gray shading, 95% confidence interval. j. Average Go probability and GLM fit as a function of Engagement Index (x-axis), and previous trial reward (color). Correlation of P(Go) from Engagement Index persists despite the additional effect of previous trial’s reward. k, To additionally test whether engagement index more specifically relates to P(Go) than any other variable, we asked whether the engagement vector (i.e. the mean difference in pre-stimulus population activity between Task and Passive contexts) matches the population vector encoding differences prior to Go and Miss trials, better than it does differences in other behavioral variables. To do so, we computed the Pearson correlation between the engagement vector and the Go-Miss vector for each recording. This correlation coefficient can be interpreted as the cosine of the angle between the two vectors in Nneurons dimensional space. Each panel shows a scatter plot, with one dot per recording, comparing this correlation against the engagement vector’s correlation with vectors defined for each other behavioral variable. In each case we find that the correlation is greater for the Go-Miss vector (not reaching significance at alpha=0.05 level, however, for the comparison with the previous reward vector, p=0.096).
a, Mice earned water rewards by turning a wheel to indicate which of two visual gratings had higher contrast, or by not turning if no stimulus was presented. When stimuli had equal contrast, a Left or Right choice was rewarded with 50% probability. Grey rectangles indicate the three computer screens surrounding the mouse. Arrows (not visible to the mouse) indicate the rewarded wheel turn direction and the coupled movement of the visual stimulus (black X indicates reward for no turn), and the colored dashed circle (not visible to the mouse) indicates the stimulus location at which a reward was delivered. b, Timeline of the task. Subjects were free to move as soon as the stimulus appeared, but the stimulus was fixed in place and rewards were unavailable until after an auditory tone cue. If no movement was made for 1.5 s after the tone cue, a NoGo was registered. The grey region is the analysis window, from 0 to 0.4 s after stimulus onset. c, Average task performance across subjects, n=10 subjects, 39 sessions, 9,538 trials. Colormaps depict the probability of each choice given the combination of contrasts presented. d, Reaction time as a function of stimulus contrast and presence of competing stimuli. e, Mice were head-fixed with forepaws on the wheel while multiple Neuropixels probes were inserted for each recording. f, Frontal view of subject performing the behavioral task during recording, with forepaws on wheel and lick spout for acquiring rewards. g, Example electrode track histology with atlas alignment overlaid. h, Recording track locations as registered to the Allen Common Coordinate Framework 3D space. Each colored line represents the span recorded by a single probe on a single session, colored by mouse identity. D, dorsal; A, anterior; L, left. i, Summary of recording locations. Recordings were made from each of the 42 brain regions colored on the top-down view of cortex (left) and sagittal section (right). For each region, number in parentheses indicates total recorded neurons. For abbreviations, see Extended Data Table 1. j, Spike raster from an example individual trial, in which populations of neurons were simultaneously recorded across visual and frontal cortical areas, hippocampus, and thalamus. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
a-c, Rasters showing activity of three example neurons following visual stimuli presented on the contralateral or ipsilateral side alone, on correct choice trials (when they evoked wheel turns in the correct direction), miss trials (when mice failed to respond in the task context), and when stimuli were presented in a passive context with no opportunity to earn reward. Top six panels: aligned to stimulus onset, black dots represent movement. Bottom two panels: aligned to movement, black dots represent stimulus onset. d-h, Colormaps showing firing rates averaged over responsive neurons in each region, and over trials of the indicated type. Contralateral visual stimulus contrasts were matched between d, f, and h so that differences in activity do not reflect differing visual drive. Subpanels to the right of each colormap represent the percentage of neurons in each area significantly more responsive during that condition than baseline, (p<10-4; see Methods).
a. Example of regression analysis for the example VISpm neuron shown in Fig. 2b. Firing rate was averaged (solid thin line, mean; shaded regions, s. e. across trials) across the trial types indicated: all trials with contralateral stimuli (dark brown), with ipsilateral stimuli (dark blue); all trials with contralateral choices (orange), with ipsilateral choices (blue). Top plots show mean firing rate overlaid with cross-validated prediction of the regression model using all kernels (solid thick lines). Bottom plots show mean rate overlaid with fits excluding the indicated kernel (dashed lines). The good fit of the full model is lost when excluding the Contralateral Vision kernels, indicating that this neuron has stimulus-locked activity that cannot be explained by other variables. b, Similar analysis for activity of the SUB neuron from Fig. 2a, for which a good fit cannot be obtained when excluding the Action kernel. c, Box plots showing distribution of the percentage of spiking variance explained in cross-validated tests of the full model, for all neurons within each brain region. d, Fraction of neurons in each brain region for which accurate prediction of pre-movement activity required the Contralateral Vision kernel. Empty bars indicate those for which the number of neurons passing analysis criteria was < 5. e, as in (d) but for the Action kernel. f, Illustration of (d) on a brain map. White areas were not recorded. g, As in (f) but for the Action kernel. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
a, Firing rates of four example neurons, averaged across indicated trial types (shaded regions, s.e.m. across trials), cross-validated fits of the kernel model using all kernels (solid lines), and fits using all kernels except Choice (dashed lines). The VISp neuron can be accurately predicted without Choice kernel, indicating that its differing responses between left and right choices can be explained by visual responses. The other three neurons cannot be predicted without the Choice kernel. The ZI neuron also appeared in Figure 2c. b, Fraction of neurons in each brain region for which accurate prediction required the Choice kernel (false positive rate on shuffled data: 0.3%). Empty bars indicate areas for which the number of neurons passing analysis criteria was < 5. c, Time courses of population decoding of choice from frontal cortex (MOs, MOp, PL), striatum (CP), and midbrain (MRN, SCm, ZI, SNr) did not significantly differ (p>0.05, 2-way ANOVA). Shaded regions: s.e.m. across recordings. d, Left: joint peri-event canonical correlation (jPECC) analysis shows that population activity in visual cortex predicts that in frontal cortex following a ~40 ms lag, but only in the period ~200ms prior to movement. Right: population activity in midbrain and frontal cortex do not show a consistent lead/lag relationship. e, Trial-averaged firing rates of example neurons recorded in the midbrain (top row) and forebrain (bottom row) aligned to contralateral (orange) and ipsilateral choices (blue). f,g, Scatter plot of activity of individual midbrain and forebrain neurons at movement onset relative to baseline activity, for trials with contralateral versus ipsilateral choices (estimated from the kernel model). Darker points represent neurons with significant choice encoding. h, Summary of (f,g) on a brain map. Red and tan indicate regions containing neurons of unilateral or bilateral selectivity. Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
a, Comparison of population average spiking activity for several brain regions, for task-context trials when contralateral stimuli were presented but subjects did not respond (i.e. ‘Miss’ trials, blue) and for stimulus presentations during the passive context (grey). Visual stimulus contrasts were matched between the two conditions. b, Excess fraction of neurons significantly activated in task context miss trials compared to passive condition, for matched contrast stimuli. c, Brain map showing difference in pre-stimulus firing rate between task and passive conditions, averaged over all neurons in each region. d, Scatterplot showing difference in pre-stimulus rate between task and passive contexts (x-axis) and between go and miss trials within the task context (y-axis), averaged over all neurons in a region. Text size indicates number of neurons in the analysis (range 130-1534). e, On each trial, an engagement index is computed by projecting pre-stimulus population activity onto a vector defined by the each neuron’s rate difference between task and passive contexts. The graph shows probability of Go response as a function of z-scored engagement index. Red: movement probability for each bin of engagement index (error bars: s.e.m. across trials). Black: logistic regression fit with 95% confidence bands (gray). f, Histogram of differences in pre-stimulus engagement index for Go versus Miss trials for each recording. Inverted triangle represents the mean value across recordings (mean = 8.42 a.u.). Brain diagrams were derived from the Allen Mouse Brain Common Coordinate Framework (version 3 (2017); downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ ).
Similar articles
-
Brain-wide neural activity underlying memory-guided movement.Cell. 2024 Feb 1;187(3):676-691.e16. doi: 10.1016/j.cell.2023.12.035. Cell. 2024. PMID: 38306983
-
Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task.J Neurosci. 1993 Mar;13(3):900-13. doi: 10.1523/JNEUROSCI.13-03-00900.1993. J Neurosci. 1993. PMID: 8441015 Free PMC article.
-
Evaluating Visual Cues Modulates Their Representation in Mouse Visual and Cingulate Cortex.J Neurosci. 2021 Apr 14;41(15):3531-3544. doi: 10.1523/JNEUROSCI.1828-20.2021. Epub 2021 Mar 9. J Neurosci. 2021. PMID: 33687964 Free PMC article.
-
Multiple Mechanisms for Processing Reward Uncertainty in the Primate Basal Forebrain.J Neurosci. 2016 Jul 27;36(30):7852-64. doi: 10.1523/JNEUROSCI.1123-16.2016. J Neurosci. 2016. PMID: 27466331 Free PMC article.
-
Subjective neuronal coding of reward: temporal value discounting and risk.Eur J Neurosci. 2010 Jun;31(12):2124-35. doi: 10.1111/j.1460-9568.2010.07282.x. Epub 2010 May 24. Eur J Neurosci. 2010. PMID: 20497474 Review.
Cited by
-
Neural dynamics of causal inference in the macaque frontoparietal circuit.Elife. 2022 Oct 24;11:e76145. doi: 10.7554/eLife.76145. Elife. 2022. PMID: 36279158 Free PMC article.
-
ACC neural ensemble dynamics are structured by strategy prevalence.Elife. 2023 Nov 22;12:e84897. doi: 10.7554/eLife.84897. Elife. 2023. PMID: 37991007 Free PMC article.
-
Triple dissociation of visual, auditory and motor processing in mouse primary visual cortex.Nat Neurosci. 2024 Apr;27(4):758-771. doi: 10.1038/s41593-023-01564-5. Epub 2024 Feb 2. Nat Neurosci. 2024. PMID: 38307971
-
To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility.Cereb Cortex. 2023 May 24;33(11):6543-6558. doi: 10.1093/cercor/bhac523. Cereb Cortex. 2023. PMID: 36734268 Free PMC article.
-
Mice alternate between discrete strategies during perceptual decision-making.Nat Neurosci. 2022 Feb;25(2):201-212. doi: 10.1038/s41593-021-01007-z. Epub 2022 Feb 7. Nat Neurosci. 2022. PMID: 35132235 Free PMC article.
References
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Molecular Biology Databases
