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Neural signatures of natural behaviour in socializing macaques


Our understanding of the neurobiology of primate behaviour largely derives from artificial tasks in highly controlled laboratory settings, overlooking most natural behaviours that primate brains evolved to produce1,2,3. How primates navigate the multidimensional social relationships that structure daily life4 and shape survival and reproductive success5 remains largely unclear at the single-neuron level. Here we combine ethological analysis, computer vision and wireless recording technologies to identify neural signatures of natural behaviour in unrestrained, socially interacting pairs of rhesus macaques. Single-neuron and population activity in the prefrontal and temporal cortex robustly encoded 24 species-typical behaviours, as well as social context. Male–female partners demonstrated near-perfect reciprocity in grooming, a key behavioural mechanism supporting friendships and alliances6, and neural activity maintained a running account of these social investments. Confronted with an aggressive intruder, behavioural and neural population responses reflected empathy and were buffered by the presence of a partner. Our findings reveal a highly distributed neurophysiological ledger of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.

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Fig. 1: Unrestrained socially interacting macaques spontaneously express their species-typical behavioural repertoire.
Fig. 2: Single-unit activity varies across the natural behavioural repertoire.
Fig. 3: Neural population activity simultaneously encodes behaviour and social context.
Fig. 4: Neural population activity tracks grooming reciprocity.
Fig. 5: Behavioural and neural evidence of social support and empathy in response to threats.

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All data, including supplementary videos 1–9, are available at the Open Science Framework (

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We thank R. Seyfarth, A. Lowet, R. Lange, M. Jazeyari laboratories and, in particular, J. I. Sanguinetti-Scheck, for conceptual, theoretical and methodological advice; X. Jiang for help with pose tracking; J. Matelsky for artwork; and our subject monkeys Lovelace, Amos, Sallyride and Hooke, from whom all the data included in this paper are sourced. R01MH095894, R01MH108627, R37MH109728, R21AG073958, R01MH118203,866 R56MH122819 and R01NS123054 to M.L.P., Blavatnik Family Foundation Fellowship to CT, Canada Banting Fellowship and Human Frontier Science Program Fellowship to S.T.

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Authors and Affiliations



Study conceptualization: C.T., S.T. and M.L.P. Surgical implantation: S.T. and K.L.G. Data collection: C.T. and F.P. Behavioural labelling: C.T., A.A.-I. and S.T. Video motion tracking: F.P. Data analyses: C.T., S.T., R.W.D. and F.P. Figure editing: C.T. and S.T. Funding acquisition: C.T., F.P., S.T. and M.L.P. All of the authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Camille Testard.

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Competing interests

M.L.P. is a scientific advisory board member, consultant and/or co-founder of Blue Horizons International, NeuroFlow, Amplio, Cogwear Technologies, Burgeon Labs and Glassview, and receives research funding from AIIR Consulting, the SEB Group, Mars, Slalom, the Lefkort Family Research Foundation, Sisu Capital and Benjamin Franklin Technology Partners. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Behaviour transition matrix, neurotechnology and neural data quality.

a, Absolute number of transitions from one behaviour to the next for one example session. Top: To reveal ethologically relevant behavioural patterns, transitions excluded “rest” epochs, behaviours performed by other monkeys in the colony (“Other monkeys vocalize” and “Rowdy Room”), as well as ephemeral behaviours (“vocalization”, “lip smack”, “anogenital investigation”, “scratch” and “yawning”). Bottom: Transition matrix with all behaviours included. b, Sum of transitions from one behaviour to the next across all sessions, pooled across monkeys. Top: ethologically relevant behaviours. Bottom: all behaviours in our ethogram. c, Images of wireless neural recording device. Note the minimal footprint of the cranial implant on the monkey’s head. d, Example medium-amplitude unit (green) with average peak at 50 uV. Bottom timeline plot (zoomed in) shows raw signal showing the high signal-to-noise ratio of spikes recording with the wireless logger. Red line indicates the threshold used, which is 3.7 SD from noise amplitude. Only spikes crossing that line compose both the yellow and green clusters above. Noise typically has a low amplitude of +/− 15 uV. Bottom right quadrant shows principal components vs time, used to confirm stability of the waveform shape over the entire session. e, Number of units recorded across vlPFC and TEO in our two subject monkeys in each session, chronologically (N = 12 sessions, 6 per monkey). All sessions were recording within 2 months of implantation.

Extended Data Fig. 2 Single unit responses during behaviour.

a, Top: behaviour during example session; Middle: Z-scored firing rate of all units recorded in the same example session as Fig. 2, ordered by activity pattern and units ordered using Rastermap60; Bottom: Average z-scored firing across all recorded units in the same session (N = 297). Shaded area is the standard deviation. b, Neuro-ethogram from Fig. 2d statistically thresholded at FDR-corrected P < 0.01 (two-sided t-tests). c, Number of behaviours a neuron is selective for if limiting to 100 observations per behaviour (sub-sampling 100 times through the data). d, Same as Fig. 2f but for well-isolated single units only for two sessions (one in each monkey). e, Same as Fig. 2b–d & Extended Data Fig. 2b but only considering fully-isolated single units (N = 67), as defined in Methods. f, Raw firing aligned to behaviour for two additional sessions to the one in Fig. 2b and Extended Data Fig. 2a, one from each monkey. Each row is an individual neural unit, ordered using the Rastermap toolbox60. Firing rates are Z-scored to maximum firing rate for each unit. Colour bar on top represents behaviours performed over time with the same colour code as in Fig. 2b. g, Z-scored raw firing aligned to behaviour for only fully-isolated units for another session than in e, from the other monkey.

Extended Data Fig. 3 Neural population states segregate by behaviour and context across sessions and are not reducible to visuo-motor contingencies.

a-b, Whole session UMAP visualization (same session as in Fig. 2a–d). c, Average distance between neural states across behaviours in UMAP space. Pooled across sessions and monkeys. d, Average distance between neural states across social contexts. (e-h) UMAP plots as in Fig. 2a but with different parameter values. Results remain qualitatively the same. i, Left panel: Table summarizing behaviour quantification models and their accuracies for monkey detection, monkey pose estimation, and datalogger pose estimation. Right panel: Confidence score by body landmark thresholding at c = 0.2. j, Comparison of elbow joint angle for two behaviours, self-groom and groom partner. Top: depiction of the stereotypic self-groom behaviour (left), groom partner (middle), and the shared motion pattern of shoulder-elbow-wrist across both behaviours (right). Bottom: plot of joint angle θ over time for self-grooming (green) and groom partner (yellow). Shaded portion indicates onset and offset of both behaviours. k, Histograms of field of view across social contexts: alone, paired female neighbour, paired male neighbour. Field of view varied substantially within a social context and overlapped across contexts, decorrelating social context coding from visual inputs. l, Schematic of ridge regression model to test the relative importance of behaviour vs. movements and field of view to explain neural variance (Fig. 3i).

Extended Data Fig. 4 Behavioural evidence for grooming reciprocity and underlying neural correlates.

a, Simulations assuming monkeys were grooming randomly. 1 = perfectly reciprocal, 0 = fully one-sided. Simulations were run 10,000 times to obtain likelihood of the observed reciprocity if monkeys were not intentionally trying to reciprocate number of bouts (top), engage in turn-taking (middle) or reciprocate grooming duration (bottom) (see Methods). b, Correlation in duration of consecutive alternating grooming bouts (e.g. I groom you, then you groom me). c, Interbout interval for alternating grooming bouts (Give-Receive-Give, blue) vs non-alternating (Give-Give or Receive-Receive, red). Alternating bouts showed a bi-modal distribution with short inter-bout intervals <20 s and longer ones. Based on this distribution we considered alternating bouts spaced by less than 20 s to be “Turn-taking”. d, Distribution of grooming bout duration for Amos (brown) vs. Hooke (yellow). Inset: Empirical distribution of grooming bout durations (blue) and approximated distribution (turquoise). Approximation uses an exponential distribution with empirical mean bout length as a parameter. e, Correlation between ridge regression model fit and how linear & monotonic the response variable is. Top: Net groom duration; Bottom: Net number of grooming bouts. f, Ridge regression model fit (cross-validated R2) for net grooming duration, net number of bouts and time combining separated by monkey (brain areas pooled). g, Ridge regression model fit (cross-validated R2) for net grooming duration, net number of bouts and time combining separated by brain areas (monkeys pooled). h, Cross-validation (5-fold) linear decoding accuracy of (from left to right): categorical net groom duration, categorical net number of bouts, chronological grooming bout number, chronological rest bout number. Red: shuffled control. i, Distribution of single neuron cross-validated ridge regression fit for net groom duration, net groom bouts and time. Distributions are not statistically distinguishable (one-way Anova, two-sided). Boxplots in panels f & g: central mark = median; box bottom and top edges = 25th and 75th percentiles, respectively; whiskers = min-max data points.

Extended Data Fig. 5 Neural signatures of social support and empathy in response to human threats.

a, Agitation score in response to human threats as in Fig. 5b separated by monkey. b, Probability of grooming given or received in the 5 min following a threat. c, Head direction during threats to self vs. partner, when paired. d, Correlation in average neural responses to threats towards the subject vs. his partner when the subject is looking in front (left), towards the threatening stimulus, or not (right). Each point represents one unit, all units across sessions with video data were included (10 sessions). Note that, because of missing head direction data during threat bouts, we averaged neural activity across all threat data points and lost temporal variation occurring within a bout. This explains why we obtained an even higher correlation than calculated in Fig. 5d. e, Average activity during threat to subject when alone vs. paired separated by brain area. f, Representational similarity scores as if Fig. 5d separated by brain area. g, Mean Euclidean distance from baseline (centre of mass of ‘rest’ states) in UMAP neural space during threats towards the subject in the alone (dark red) vs. paired conditions (yellow), separated by monkey (same as in Fig. 4e). Shaded area represents SEM.

Extended Data Table 1 Selective literature review on sparse coding from neurophysiological data collected in traditional laboratory settings

Supplementary information

Supplementary Information 1

Cayo ethogram, basis for the experiment’s ethogram.

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Supplementary Information 2

Comparison of Cayo and Lab ethogram.

Supplementary Information 3

Ethogram (description of behaviours, grooming contexts, visuomotor variables).

Supplementary Information 4

Intervention description and schedule.

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Testard, C., Tremblay, S., Parodi, F. et al. Neural signatures of natural behaviour in socializing macaques. Nature 628, 381–390 (2024).

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