CalliCog is an open-source cognitive neuroscience toolkit for freely behaving nonhuman primates

Abstract

Nonhuman primates (NHPs) are pivotal for unlocking the complexities of human cognition, yet traditional cognitive studies remain constrained to specialized laboratories. To address this gap, we present CalliCog: an open-source, scalable in-cage platform tailored for experiments in small freely behaving primate species such as the common marmoset (Callithrix jacchus). CalliCog includes modular operant chambers that operate autonomously and integrate seamlessly with home cages, eliminating human intervention. Our results showcase the power of CalliCog to train experimentally naive marmosets in touchscreen-based cognitive tasks. Across two independent facilities, marmosets achieved touchscreen proficiency within 2 weeks and successfully completed tasks probing behavioral flexibility and working memory. Moreover, CalliCog enabled precise synchronization of behavioral data with electrocorticography (ECoG) recordings from freely moving animals, opening new frontiers for neurobehavioral research. By making CalliCog openly accessible, we aim to democratize cognitive experimentation with small NHPs, narrowing the translational gap between preclinical models and human cognition.

Keywords: CP: Biotechnology; CP: Neuroscience; automation; behavior; behavioral flexibility; cognition; electrocorticography; marmoset; operant chamber; positive reinforcement; prefrontal cortex; working memory.

Graphical abstract

Figure 1

High-throughput, automated cognitive experiments in a marmoset housing facility using CalliCog Individual home cages are fitted with operant chambers, which are used to train and test behaviorally naive animals in touchscreen-based cognitive tasks using positive reinforcement. The functions of each operant chamber are controlled by an internal agent PC, which receives trial instructions over a local network from an executive PC housed outside of the housing area. The executive PC receives behavioral data from the agent PC and queries performance against defined parameters to determine automated task progression. A single executive PC can simultaneously administer multiple operant chambers in a housing facility, facilitating a flexible and scalable approach to home-cage testing. In addition, devices for neural recording can be integrated with the local network to study the neural correlates of cognitive behavior. In this example, an off-the-shelf system for wireless telemetry recording (indicated by green arrows) is used to acquire ECoG via wireless receivers while animals engage in touchscreen-based tasks. ∗Agent PCs are synchronized to a clock set by the telemetry PC using a conventional network time protocol, allowing for synchronous behavioral and neural data timing.

Figure 2

Workflow for automated progression during behavioral training or testing Once a protocol has been designed and initiated by the user at the executive PC, behavioral experiments run autonomously by evaluating animal performance on a trial-by-trial basis. The executive PC sends trial instructions to an agent PC using the JSON text format, which the agent PC parses for trial execution. Trial data, including all timestamps of display events and touch responses, are then returned to the executive PC and logged via the SQL programming language to a local relational database (PostgreSQL). The executive PC then evaluates the logged trial data against predefined “progression criteria” to determine whether to continue the current task or progress to the next in sequence. Responding to animal performance at single-trial resolution allows the system to adapt tasks based on cognitive behaviors that can change in a single moment (e.g., learning or decision-making).

Figure 3

Experimental pipeline and animal performance in automated touchscreen training (A) Summary of the unsupervised experimental pipeline, including all stages of training and testing, used in the current study. Each experiment incorporated multiple phases through which animals progressed after achieving predefined progression criteria (refer to Table 1). (B) The touchscreen training protocol included seven individual phases in which animals were gradually trained to respond to small (250 × 250 px) stimuli regardless of color and spatial position. (C and D) Summarized performance of animals (n = 7) over all seven phases of touchscreen training. The data are presented to show the variability between animals that were housed in different animal facilities. On average, animals completed touchscreen training in 44.1 sessions or 14 days. (E) Variability between the performance of individual animals on phases 2–7 of the touchscreen training protocol. Performance across individual phases did not vary significantly. Note that animals require a minimum number of 3 sessions to complete each phase.

Figure 4

Animal performance in behavioral flexibility and working memory tests using CalliCog (A) Trial representation of the novel discrimination and reversal learning tasks. Per stimulus pair, animals perform a novel discrimination stage and a reversal learning stage in which the stimulus-reward contingencies are switched. (B) Performance in novel discrimination training. (C) Performance in novel discrimination testing. Stimulus pair 9 significantly contributed to a main effect of stimulus pair on performance (Kruskal-Wallis test with Dunn’s test), so it was omitted from subsequent analysis. (D) Averaged learning curves of individual animals across all 10 reversal learning experiments. The data are smoothed for visualization purposes using a second-order polynomial fit to a rolling window of 100 neighbors. (E) Individual performance in reversal learning experiments between animals, as characterized by the number of errors committed before reaching criterion. (F) The proportion of trials animals performed across three phases of reversal learning, as calculated from learning curves of cumulative performance using a method for learning curve analysis. Data points represent the mean number of trials per animal across all nine reversal experiments. (G) The probability of animals using win-stay or lose-shift strategies during the learning phase of reversal learning represents the likelihood of correctly adapting responses based on positive and negative feedback, respectively. (H) Trial representation of the delayed match-to-sample task (DMTS) used to assess working memory maintenance. The relationship between successful performance and the length of the variable delay period indicates the capacity for maintaining working memory over time. (I) The average working memory decay curve generated from the averaged performance data of animals (n = 3) performing the DMTS. The data are fit to a simple exponential decay function, and the chance threshold is the performance value at which the percentage of success is significantly greater than the chance performance of 50%. (J and K) Derived metrics of zero-delay performance and performance half-life from the working memory decay curves of individual animals, which represent the baseline ability to perform the task and the rate of working memory decay, respectively. ∗p < 0.05.

Figure 5

Task-related neural recording in CalliCog (A) Workflow for task-related ECoG analysis in freely behaving marmosets. Animals (n = 2) were implanted with two-channel ECoG electrodes over the bilateral prefrontal cortex, transmitting live data wirelessly during performance in the DMTS. Timestamps were recorded from all DMTS trials to define four epochs from ECoG data relevant to working memory: encoding, maintenance, retrieval, and outcome. These epochs were segmented from the recorded data and analyzed independently. (B–D) Time-locked averaged potentials of filtered, preprocessed ECoG recorded from animals M2 and M3 while performing successful trials at three exemplary delay periods of 1, 2, and 4 s (n = 70 trials per delay condition). Dotted lines represent the onset and the end of the maintenance epoch. (E) The averaged power spectra from individual epochs of successful trials from animal M2. Frequency values between 50 and 70 Hz were included in notch filtering and thus excluded from the analysis. (F) Reweighting of power quantifications from the spectra of each epoch at frequency bands of interest. Power is expressed as the absolute power of the nontransformed spectrum within each frequency band relative to the absolute power of the entire spectrum. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.