Naturalistic animal behavior exhibits a striking amount of variability in the temporal domain (Figure 1A). An individual animal’s behavioral variability can be decomposed across at least three axes: hierarchical, contextual, and stochastic. The first source of variability originates from the vast hierarchy of timescales underlying self-initiated, spontaneous behavior ranging from milliseconds to minutes in animals (and to years for humans). On the sub-second timescale, animals perform fast movements varying from tens to hundreds of milliseconds. In rodents, these movements include whisking, sniffing, and moving their limbs. On the slower timescales of seconds, animals concatenate these fast movements into behavioral sequences of self-initiated actions, such as exploratory sequences (moving around an object while sniffing, whisking, and wagging their noses) or locomotion sequences (coordinating limb and head movements to reach a landmark). These sequences follow specific syntax rules (Berridge et al., 1987) and can last several seconds. On the timescales of minutes or longer, mice may repeat the ‘walk and explore’ behavioral sequence multiple times, when engaged in some specific activity, such as foraging, persisting toward their goal for long periods. In these simple examples, a freely moving mouse exhibits behavior whose temporal organization varies over several orders of magnitudes simultaneously, ranging from the sub-second scale (actions), to several seconds (behavioral sequences), to minutes (goals to attain). A leading theory to explain the temporal organization of naturalistic behavior is that behavioral action sequences arrange in a hierarchical structure (Tinbergen, 1951; Dawkins, 1976; Simon, 1962), where actions are nested into behavioral sequences that are then grouped into activities. This hierarchy of timescales is ubiquitously observed across species during naturalistic behavior.

Figure 1

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The second source of temporal variability stems from the different contexts in which a specific behavior can be performed. For example, reaction times to a delivered stimulus can be consistently faster when an animals is expecting it compared to slower reaction times when the stimulus is unexpected (Jaramillo and Zador, 2011). Contextual sources can be either internally generated (modulations of brain state, arousal, expectation) or externally driven (pharmacological, optogenetic or genetic manipulations; or changes in task difficulty or environment).

After controlling for all known contextual effects, an individual’s behavior still exhibits a large amount of residual temporal variability across repetitions of the same behavioral unit. This residual variability across repetitions can be quantified as a property of the distribution of movement durations, which is typically very skewed with an exponential tail (Wiltschko et al., 2015; Findley et al., 2021; Recanatesi et al., 2022), suggesting a stochastic origin. We define as stochastic variability the fraction of variability in the expression of a particular behavioral unit, which cannot be explained by any readily measurable variables.

These three aspects of temporal variability (hierarchical, contextual, stochastic) can all be observed in the naturalistic behavior of a single individual during their lifetime. Other sources of behavioral variability have also been investigated. Variability in movement execution and body posture (Dhawale et al., 2017) may originate from the motor periphery, such as noise in force production within muscles (van Beers et al., 2004), or from the deterministic chaotic dynamics arising from neural activity (Costa et al., 2021) or the biomechanical structure of an animal’s body (Loveless et al., 2019). One additional source is individual phenotypic variability across different animals, an important aspect of behavior in ethological and evolutionary light (Honegger and de Bivort, 2018). Although we will briefly discuss some features of variability in movement execution and phenotypic individuality, the main focus of this review will be on the three main axes of variability described above, which pertain to the behavior of a single animal.

From this overview, we conclude that the temporal structure of naturalistic, self-initiated behavior can be decomposed along at least three axes of temporal variability (Figure 1A): a large hierarchy of simultaneous timescales at which behavior unfolds, ranging from milliseconds to minutes; contextual modulations affecting the expression of behavior at each level of this hierarchy; and stochasticity in the variability of the same behavioral unit across repetition in the same context. These three axes may or may not interact depending on the scenarios and the species. Here, we will review recent theoretical and computational results establishing the foundations of a mechanistic theory that explains how these three sources of temporal variability can arise from biologically plausible computational motifs. More specifically, we will address the following questions:

1. Neural representation: How are self-initiated actions represented in the brain and how are they concatenated into behavioral sequences?

2. Stochasticity: What neural mechanisms underlie the temporal variability observed in behavioral units across multiple repetitions within the same context?

3. Context: How do contextual modulations affect the temporal variability in behavior, enabling flexibility in action timing and behavioral sequence structure?

4. Hierarchy: How do neural circuits generate the vast hierarchy of timescales from milliseconds to minutes, hallmark of naturalistic behavior?

The mechanistic approach we will review is based on the theory of metastable attractors (Figure 1B), which is emerging as a unifying principle expounding many different aspects of the dynamics and function of neural circuits (La Camera et al., 2019). We will first establish a precise correspondence between behavioral units and neural attractors at the level of self-initiated actions (the lowest level of the temporal hierarchy). Then we will show how the emergence of behavioral sequences originates from sequences of metastable attractors. Our starting point is the observation that transitions between metastable attractors can be driven by the neural variability internally generated within a local recurrent circuit. This mechanism can naturally explain the action timing variability of stochastic origin. We will examine biologically plausible mesoscopic circuits that can learn to flexibly execute complex behavioral sequences. We will then review the neural mechanisms underlying contextual modulations of behavioral variability. We will show that the average transition time between metastable attractors can be regulated by changes in single-cell gain. Gain modulation is a principled neural mechanism mediating the effects of context, which can be induced by either internal or external perturbations and supported by different neuromodulatory and cortico-cortical pathways; or by external pharmacological or experimental interventions. Finally, we will review how a large hierarchy of timescales can naturally and robustly emerge from heterogeneities in a circuit’s structural connectivity motifs, such as neural clusters with heterogeneous sizes. Although most of the review is focused on the behavior of individual animals, we discuss how recent results on multianimal interactions and social behavior may challenge existing theories of naturalistic behavior and brain function. Appendices 1–3 provide guides to computational methods for behavioral video analyses and modeling; theoretical and experimental aspects of attractors dynamics; and biologically plausible models of metastable attractors.

The stochastic nature of naturalistic behavior

The study of naturalistic behavior based on animal videos has recently undergone a revolution due to the spectacular accuracy and efficiency of computational methods for animal pose tracking (Stephens et al., 2008; Berman et al., 2014; Hong et al., 2015; Mathis et al., 2018; Pereira et al., 2019; Nourizonoz et al., 2020; Segalin et al., 2020); see Appendix 1 and Table 1 for details). These new methods work across species and conditions, ushering a new era for computational neuroethology (Anderson and Perona, 2014; Brown and de Bivort, 2018; Datta et al., 2019). They have led to uncovering a quantitative classification of self-initiated behavior revealing a stunning amount of variability both in its lexical features (which actions to choose, in which order) and in its temporal dimension (when to act) (Berman et al., 2016; Wiltschko et al., 2015; Markowitz et al., 2018; Marshall et al., 2021; Recanatesi et al., 2022). Different ways to characterize the behavioral repertoire on short timescales have been developed, depending on the underlying assumptions of whether the basic units of behavior are discrete or continuous (Appendix 1). One can define stereotypical behaviors or postures by clustering probability density maps of spectrotemporal features extracted from behavioral videos, as in the example of fruit flies (Berman et al., 2014; Berman et al., 2016; (Figure 2A) or rats (Marshall et al., 2021; Figure 7B). Alternatively, one can capture actions or postures as discrete states of a state–space model based on Markovian dynamics, each state represented as a latent state autoregressive trajectory accounting for stochastic movement variability (Figure 2). In the case of discretization of the behavioral repertoire, the definition of the smallest building block of behavior depends on the level of granularity that best suits the scientific question being investigated. Within this discrete framework, state–space models were applied successfully to Caenorhabditis elegans (Linderman et al., 2019), Drosophila (Tao et al., 2019), zebrafish (Johnson et al., 2020), and rodents (Wiltschko et al., 2015; Markowitz et al., 2018). At the timescale of actions and postures, transition times between consecutive actions are well described by a Poisson process (Killeen and Fetterman, 1988), characterized by a right-skewed distribution of inter-action intervals (Figure 3). In this and the next section, we will focus on short timescales (up to a few seconds) where state–space models can provide parsimonious accounts of behavior. However, these models fail to account for longer timescale and non-Markovian structure in behavior, and in later sections we will move to a more data-driven approach to investigate these aspects of behavior (see Appendix 1).

Table 1

Action	The simplest building blocks of behavior are at the lowest level of the hierarchy. This definition depends on the level of granularity (spatiotemporal resolution) required and the scientific questions to be investigated (see Appendix 1). A widely used definition of action is a short stereotypical trajectory in posture space (synonyms include movemes, syllables [Anderson and Perona, 2014; Brown and de Bivort, 2018]), operationally defined as a discrete latent trajectory of an autoregressive state–space model fit to pose-tracking time-series data (Figure 2; Wiltschko et al., 2015; Findley et al., 2021). An alternative definition is in terms of short spectrotemporal representations from a time-frequency analysis of videos (Berman et al., 2016; Marshall et al., 2021). Examples include poking in or poking out of a nose port, waiting at a port, pressing a lever.
Behavioral action sequence	A combination of actions concatenated in a meaningful yet stereotyped way, lasting up to a few seconds. A sequence can occur during trial-based experimental protocols (e.g., a short sequence of actions aimed at obtaining a reward in an operant conditioning task [Geddes et al., 2018; Murakami et al., 2014]; running between opposite ends of a linear track [Maboudi et al., 2018]) or during spontaneous periods (e.g., repeatedly scratching own head; picking up and manipulating an object).
Activity	A concatenation of multiple behavioral sequences, often repeated and of variable duration, typically aimed at obtaining a goal and lasting up to minutes or even hours. Examples include grooming, foraging, mating, feeding, and exploration. Activities typically unfold in naturalistic freely moving settings devoid of experimenter-controlled trial structure.

Figure 2

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Figure 3

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The second source of temporal variability in naturalistic behavior arises from contextual modulations, which can be internally or externally driven. When internally driven, they may arise from changes in brain or behavioral state such as arousal, expectation, or task engagement. When externally driven, they may arise from changes in environmental variables, from the experimenter’s imposed task conditions, or from manipulations such as pharmacological, optogenetic, or genetic ones. Contextual modulations can affect several qualitatively different aspects of behavioral units at each level of the hierarchy (actions, sequences, activities): average duration; usage frequency; and transition probabilities between units. Moreover, context may also change the motor execution of a behavioral unit, for example, by improving the vigor of a certain movement upon learning or motivation. For each type of modulation, we will give several examples and review computational mechanisms that may explain them.

Action timing

The distribution of self-initiated action durations typically exhibits large variability, whose characteristic timescale can be extracted from their average duration (Figure 3). The average timing of an action is strongly modulated by contextual factors, both internally and externally driven. Examples of internally generated contextual factors include expectation and history effects. When events occur at predictable instants, anticipation improves performance such as reaction times. This classic effect of expectation was documented in an auditory two-alternative choice task (Jaramillo and Zador, 2011), where freely moving rats were rewarded for correctly discriminating the carrier frequency of a frequency-modulated target sound immersed in pure-tone distractors (Figure 4A). The target could occur early or late within each sound presentation, and temporal expectations on target timing were modulated by changing the ratio of trials with early or late targets within each block. When manipulating the expectations about sound timing, valid expectations accelerated reaction times and improved detection accuracy, showing enhanced perception. The auditory cortex is necessary to perform this task, and firing rates in auditory cortex populations are modulated by temporal expectations.

Figure 4

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Contextual changes in brain state may also be induced by varying levels of neuromodulators. In the self-initiated waiting task of Figure 3A, optogenetic activation of serotonergic neurons in the dorsal raphe nucleus selectively prolonged the waiting period, leading to a more patient and less impulsive behavior, but did not affect the timing of other self-initiated actions (Fonseca et al., 2015). Internally generated contextual modulations include history effects, which can affect the timing of self-initiated actions. In an operant conditioning task, where freely moving mice learned to press a lever for a minimum duration to earn a reward (Fan et al., 2012), the distribution of action timing showed dependence on the outcome of the preceding trial. After a rewarded trial, mice exhibited longer latency to initiate the next trial, but shorter press durations; after a failure, the opposite behavior occurred, with shorter latency to engage and longer press durations. Trial history effects are complex and action-specific and depend on several other factors, including prior movements, and wane with increasing inter-trial intervals. Inactivation experiments showed that these effects rely on frontal areas such as medial prefrontal and secondary motor cortices (Murakami et al., 2014; Murakami et al., 2017; Schreiner et al., 2021).

Although the contextual modulations considered so far occur on a fast timescale of a few trials, they may also be the consequence of associative learning. In a lever press task, mice learned to adjust the average duration of the lever press to different criteria in three different conditions where lever presses were always rewarded regardless of duration or only rewarded if longer than 800 or 1600 ms. Within each of the three conditions, the distribution of action timing exhibited large temporal variability, yet the average duration was starkly different between the three criteria as the mice learned the different criteria (Fan et al., 2012; Schreiner et al., 2021). Reaction times to sensory stimuli and self-initiated waiting behavior, in the form of long lever presses or nose-pokes, have emerged as a fruitful approach to test hypotheses on contextual modulations and decision-making in naturalistic scenarios.

Behavioral sequences

Contextual modulations of temporal variability may affect other aspects of natural behavior beyond average action timing, such as the frequency of occurrence of an action (‘state usage’) or the transition probabilities between actions within a behavioral sequence (Figure 5A). These effects can be uncovered by analyzing freely moving behavioral videos with state–space models, such as the autoregressive hidden Markov model (HMM) (Wiltschko et al., 2015; Figures 2B and 5A and Appendix 1). Differences in state usage and transition probabilities between conditions may shed light on the computational strategies animals deploy to solve complex ethological tasks, such as those involving sensorimotor integration. For example, in a distance estimation task where mice were trained to jump across a variable gap, a comparison of monocular and binocular mice revealed the different visually guided strategies mice may use to perform a successful jump. Mice performed more vertical head movements under monocular conditions compared to control (Figure 5A, states 2 and 3 occur more frequently in the monocular condition), revealing a reliance on motion parallax cues (Parker et al., 2021).

Figure 5

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During ongoing periods, in the absence of a task, animal behavior features a large variety of actions and behavioral sequences (Figure 2). Experimentally controlled manipulations can lead to strong changes in ongoing behavior reflected both in changes of state usage and of transition probabilities between actions, resulting in different repertoires of behavioral sequences. Examples of manipulations include exposing mice to innately aversive odors and other changes in their surrounding environment; optogenetic activation of corticostriatal pathways (Wiltschko et al., 2015); and pharmacological treatment (Wiltschko et al., 2020). In the latter study, a classification analysis predicted with high accuracy which drug and specific dose was administered to the mice from a large panel of compounds at multiple doses. Comparison of state usage and transition rates can also reveal subtle phenotypical changes in the structure of ongoing behavior in genetically modified mice compared to wildtype ones. This phenotypic fingerprinting has led to insights into the behavior of mouse models of autism spectrum disorder (Wiltschko et al., 2015; Wiltschko et al., 2020; Klibaite et al., 2021).

The temporal organization of naturalistic behavior exhibits a clear hierarchical structure (Tinbergen, 1951; Dawkins, 1976; Simon, 1962), where actions are nested into behavioral sequences that are then grouped into activities. Higher levels in the hierarchy emerge at longer timescales: actions/movements occur on a sub-second scale, behavioral sequences span at most a few seconds and activities last for longer periods of several seconds to minutes. The crucial aspect of this behavioral hierarchy is its complexity: an animal’s behavior unfolds along all timescales simultaneously. What is the organization of this vast spatiotemporal hierarchy? What are the neural mechanisms supporting and generating this nested temporal structure?

A case study: C. elegans locomotor behavior

Remarkable progress in both behavioral and neurophysiological aspects of the hierarchy has been made in the worm C. elegans (Figure 6). The worm locomotor behavior exhibits a clear hierarchical organization with three distinct timescales: sub-second body-bends and head-casts, second-long crawling undulations, and slow reverse-forward cycles (Gomez-Marin et al., 2016). At the neuronal level, this hierarchy is generated by nested neuronal dynamics where upper-level motor programs are supported by slow activity spread across many neurons, while lower-level behaviors are represented by fast local dynamics in small multifunctional populations (Kaplan et al., 2020). Persistent activity driving higher-level behaviors gates faster activity driving lower-level behaviors, such that, at lower levels, neurons show dynamics spanning multiple timescales simultaneously (Hallinen et al., 2021). Specific lower-level behaviors may only be accessed via switches at upper levels, generating a nonoverlapping, tree-like hierarchy, in which no lower-level state is connected to multiple upper-level states.

Figure 6

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At the top of this motor hierarchy, we find a much longer-lasting organization of states in terms of exploration, exploitation, and quiescence. In contrast to the strict, tree-like structure observed in the motor hierarchy, lower-level motor features are shared across these states, albeit with different frequency of occurrence. Whereas the motor hierarchy is directly generated by neuronal activity, this state-level hierarchy may rely on neuromodulation (Ben Arous et al., 2009; Flavell et al., 2013)

Behavioral hierarchies in flies and rodents

How much of this tight correspondence between behavioral and neural hierarchies generalizes from worms to insects and mammals? Are action sequences organized as a chain? Alternatively, is there a hierarchical structure where individual actions, intermediate subsequences, and overall sequences can be flexibly combined? A chain-like organization would require a single controller concatenating actions, but could be prone to error or disruption. A hierarchical structure could be error-tolerant and flexible at the cost of requiring controllers at different levels of the hierarchy (Geddes et al., 2018). We will first examine compelling evidence from the fruit fly and then discuss emerging results in rodent experiments.

By analyzing videos of ground-based fruit fly during long sessions of spontaneous behavior (Figure 2B), Berman et al., 2014 and Berman et al., 2016 mapped the entire behavioral space of freely moving flies, identifying a hundred stereotyped, frequently reoccurring actions, interspersed with bouts of nonstereotyped behaviors. Recurring behavioral categories emerged as peaks in the behavioral space probability landscape, labeled as walking, running, head grooming, and wing grooming (Figure 7A). In order to uncover the organization of behavior at different timescales, the authors estimated which behavioral representations (movements, sequences, or activities) could optimally predict the fly’s future behavior on different temporal horizons (from 50 ms to minutes), applying the information bottleneck method (Tishby et al., 2000). This predictive algorithm revealed multiple timescales in the fly behavior, organized into a hierarchical structure reflecting the underlying behavioral programs and internal states. The near future could be optimally predicted by segmenting behavior according to actions at the fastest level of the hierarchy. The optimal representation of behavior that could optimally predict the distant future up to minutes away was based on slower, coarser groups of actions grouped into activities. These longer timescales manifest as nested blocks in the transition probability matrix, implying that a strongly non-Markovian structure emerges on longer timescales (Alba et al., 2020; Figure 7A).

Figure 7

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A hallmark of the fly behavior emerging from this analysis is that the different branches of this hierarchy are segregated into a tree-like, nonoverlapping structure: for example, actions occurring during grooming do not occur during locomotion. This multiscale representation was leveraged to dissect the descending motor pathways in the fly. Optogenetic activation of single neurons during spontaneous behavior revealed that most of the descending neurons drove stereotyped behaviors that where shared by multiple neurons and often depended on the behavioral state prior to activation (Cande et al., 2018). An alternative statistical approach based on a hierarchical HMM revealed that, although all flies use the same set of low-level locomotor features, individual flies vary considerably in the high-level temporal structure of locomotion, and in how this usage is modulated by different odors (Tao et al., 2019). This behavioral idiosyncrasy of individual-to-individual phenotypic variability has been traced back to specific genes (Ayroles et al., 2014) regulating neural activity (Buchanan et al., 2015) in the central complex of the fly brain. This series of studies suggest that the fly behavior is organized hierarchically in a tree-like structure from actions to sequences to activities. This hierarchy seems to lack flexibility, such that behavioral units at lower levels are segregated into different hierarchical branches and cannot be rearranged at higher levels. Although a more flexible structure seems to emerge during social interactions (see above and Calhoun et al., 2019).

What is the structure of the behavioral hierarchy in mammals? We will start by considering the first two levels of the hierarchy, namely, how actions concatenate into sequences. A recent experimental tour de force demonstrated the existence of a hierarchical structure in the learning and execution of heterogeneous sequences in a freely moving operant task (Geddes et al., 2018). Mice learned to perform a ‘penguin dance’ consisting of a sequence of two or three consecutive left lever presses (LL or LLL) followed by a sequence of two or more right lever presses (RR or RRR) to obtain a reward. Mice acquired the sequence hierarchically rather than sequentially, a learning scheme that is inconsistent with the classic reinforcement learning paradigm, which predicts sequence learning occurs in the reverse order of execution (Sutton and Barto, 2018). Using closed-loop optogenetic stimulation, the authors revealed the differential roles played by striatal direct and indirect pathways in controlling, respectively, the expression of a single action (either L or R), or a fast switch from one subsequence to the next (from the LL block to RR, and from RR to the reward approach).

What is the structure of the rodent behavioral hierarchy at slower timescales? A multiscale analysis of ongoing behavior was performed using rat body piercing, which allowed for tracking of the three-dimensional pose with high accuracy (Figure 7B; Marshall et al., 2021). A watershed algorithm applied to behavioral probability density maps revealed the emergence of hierarchical behavioral categories at different temporal scales. Examining the behavioral transition matrix at different timescales, signature of non-Markovian dynamics peaked at 10–100 s timescales. On 15 s timescales, pattern sequences emerged featuring sequentially ordered actions, such as grooming sequences of the face followed by the body. On minute-long timescales, states of varying arousal or task engagement emerged, lacking stereotyped sequential ordering. Although the results of Marshall et al., 2021 seem to suggest the presence of a flexible, overlapping structure in the rat hierarchy, consistent with Geddes et al., 2018 and different from the tree-like hierarchy in flies (Berman et al., 2016), the analysis of Marshall et al., 2021 lacked the information bottleneck step, which was crucial to capture the fly behavior on long timescales. Future work may shed light on the nature of behavioral hierarchies in mammals using the information bottleneck.

Manipulation experiments could help clarify the structure of the rodent behavioral hierarchy and whether it allows for flexible behavioral sequences. Earlier studies of contextual modulations of rodent freely moving behavior by pharmacological or genetic manipulations were mostly confined to comparison of usage statistics of single actions (Wiltschko et al., 2015). However, more recent studies (Marshall et al., 2021; Klibaite et al., 2021), which mapped the rodent behavioral hierarchy, allowed a multiscale assessment of contextual effects. In a rat model of the fragile X syndrome, while mutant and wildtype rats had similar locomotor behavior, the former showed abnormally long grooming epochs, characterized by different behavioral sequences and states compared to wildtypes (Figure 7C). This study highlights the advantages of multiscale comparative taxonomy of naturalistic behavior to investigate behavioral manifestations of complex conditions such as autism spectrum disorder and classify the behavioral effects of different drugs. Another benefit of this multiscale approach is to allow the investigation of the individuality of rodent behavior, revealing that, although all animals drew from a common set of temporal patterns, differences between individual rats were more pronounced at longer timescales compared to short ones (Marshall et al., 2021). It would be interesting to further explore the features of individuality across species as a way of testing ecological ideas such as ‘bet-hedging’ (Honegger and de Bivort, 2018).

Multiple timescales of neural activity

What are the neural mechanisms generating the hierarchy of timescales observed during naturalistic behavior? Is there evidence that neural activity is simultaneously varying over multiple timescales? Although no studies directly addressed these questions yet, a number of experimental and theoretical approaches have provided evidence for multiple timescales of neural activity. Some evidence for temporal heterogeneity in neural activity was reported in restrained animals during stereotyped behavioral assays. A heterogeneous distribution of timescales of neural activity was found in the brainstem of the restrained larval zebrafish by measuring the decay time constant of persistent firing across a population of neurons comprising the oculomotor velocity-to-position neural integrator (Figure 8A; Miri et al., 2011). The decay times varied over a vast range 0.5–50 s across cells in individual larvae. This heterogeneous distribution of timescales was later confirmed in the primate oculomotor brainstem (Joshua et al., 2013), suggesting that timescale heterogeneity is a common feature of oculomotor integrators conserved across phylogeny. Single-neuron activity may also encode a long memory of task-related variables. In head-fixed monkeys performing a competitive game task, temporal traces of the reward delivered in previous trials were encoded in single-neuron spiking activity in frontal areas over a wide range of timescales, obeying a power law distribution up to 10 consecutive trials (Bernacchia et al., 2011).

Figure 8

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The timescale of intrinsic fluctuations in spiking activity can be also estimated from single-neuron spike autocorrelation functions (Figure 8B). In awake head-fixed primates, during periods of ongoing activity, the distribution of intrinsic autocorrelation timescales within the same cortical circuit was found to be right-skewed and approximately lognormal (Figure 8B), in the range from 10 ms to 1 s (Cavanagh et al., 2016). Moreover, comparison of the population-averaged autocorrelations during ongoing periods revealed a hierarchical structure across cortical areas, varying from 50 to 350 ms along the occipital-to-frontal axis (Murray et al., 2014; Figure 8C).

While all these results were obtained in restrained animals, it is an open question whether neural activity during naturalistic behavior exhibits temporal hierarchies similar to those observed in behavior. Current evidence from freely moving rodents engaged in waiting tasks (Murakami et al., 2017; Schreiner et al., 2021; Recanatesi et al., 2022) revealed the presence of multiple timescales in single-neuron activity from secondary motor and prefrontal cortices. These timescales range from the sub-second scale (single attractors), to a few seconds (attractor sequences), to tens of seconds or minutes (trial history dependence). It is tantalizing to speculate that even longer timescales may be present for neural activity to be able to generate the vast hierarchy of timescales observed in naturalistic behavior (Figure 7). Evidence from associative learning tasks in rodents found that population activity in hippocampal CA1 and CA3 encodes multiplexed information about several aspects of the task occurring on multiple trials such as context, place, value, and objects (McKenzie et al., 2014). Although an explicit analysis of temporal correlations was not carried out in this study, these results suggest that a hierarchy of timescales may be present in the hippocampus and emerge during learning.

Future directions: Neural mechanisms generating temporal hierarchies

Future studies should address how the hierarchy of timescales found in behavior may emerge from computational mechanisms. There are several missing links along this path. First, although temporally heterogeneous neural activity was found in several brain areas and species under different experimental conditions, no study to date investigated the presence of temporal hierarchies in neural activity during naturalistic behavior. All the necessary tools are available: recent advances in neurotechnology demonstrate the feasibility of chronic recordings of large neural populations during freely moving behavior (Juavinett et al., 2019; van Daal et al., 2021); simultaneously performing behavioral classification analyses from pose tracking software (Mathis et al., 2018; Marshall et al., 2021; Pereira et al., 2019). This first piece of the puzzle is thus within reach.

On the theory side, although recurrent dynamics can generate heterogeneous distributions of timescales (Figure 8E), this model needs to be extended to a fully realistic framework for explaining nested temporal hierarchies in naturalistic behavior such as the proposed mesoscopic circuit in Figure 5C. Here, we propose a simple roadmap to bridge this gap, building on some of the theoretical ideas we reviewed above. This theory is based on a hierarchical structure that, for lack of a better term, we will denote ‘Attractors all the way down’ (Figure 9). We start from the observation that self-initiated actions within a behavioral sequence are represented as metastable attractors in secondary motor cortex (Inagaki et al., 2019; Recanatesi et al., 2022; Figure 3). We then extend this observation to a general principle positing that behavioral units at each level of the temporal hierarchy (not only actions, but also sequences, activities) are represented by metastable attractors. The stochasticity in behavioral unit duration can be achieved by generating transitions between attractors at each level via low-dimensional variability, arising from mesoscopic feedback loops involving cortex and subcortical areas (Recanatesi et al., 2022; Figure 3D). The average transition time between behavioral units at each level will then depend on the barrier height separating the corresponding attractors (Cao et al., 2016; Mazzucato et al., 2019; Wyrick and Mazzucato, 2021). As one moves up the hierarchy from actions (fast transitions = lower barriers) to sequences and activities (slow transitions = higher barriers), the potential wells separating the corresponding attractors become deeper and the basins of attraction wider. This increase in timescales can be achieved in a biological manner by assuming that increasingly larger neural populations encode for slower behavioral features (Stern et al., 2021; see Figure 8E), which can be realized in a biologically plausible way as a hierarchy of clusters within clusters (Schaub et al., 2015). Contextual modulations in average duration of a behavioral unit at each level of the hierarchy can be implemented by top-down changes of the barrier heights between attractors via gain modulation (Figure 4C). The new theoretical ingredient required for this theory to work is the presence of a nested structure in the attractor landscape, whereas the basin of attraction of lower-level attractors (actions) is contained within the basins of higher level attractors (sequences) all the way up to the largest basins representing long-term activities. The architecture of this model provides some immediate predictions for neural activity. First, neurons exhibit conjunctive selectivity to multiple variables at different levels of the hierarchy, which has been observed, for example, in rodent hippocampus during freely moving tasks (McKenzie et al., 2014) and in primate prefrontal cortex during complex decision-making tasks (Rigotti et al., 2013). Second, single-cell activity is modulated by many attractors, in agreement with the multistable activity observed in sensory (Mazzucato et al., 2015) and frontal areas (Recanatesi et al., 2022) where neuronal representations of attractors are dense rather than sparse. Third, fluctuations in the activity of single neurons should vary over multiple timescales simultaneously, encoding information about multiple levels of the hierarchy; this is the structure that was observed in C. elegans where neurons representing movements exhibit both fast and for slow fluctuations correlated to multiple levels of the behavioral hierarchy (Kaplan et al., 2020; Figure 6).

Figure 9

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Which network architectures can implement a hierarchical attractor landscape? The behavioral hierarchy discovered in flies (Berman et al., 2016) can be approximated as a tree: behavioral units at a lower level of the hierarchy only occur during a specific unit at a higher level of the hierarchy (Figure 7). For example, movements occurring during the fruit fly locomotion activities never occur during idle activities. This structure is typical of phylogenetic classification in taxonomy, where the distance from the root to the leaves is the same for every leaf and the tree is called ultrametric (Rammal et al., 1986). It is not known whether behavioral trees are ultrametric; in order to clarify this structure, a notion of distance in behavioral space will have to be introduced and examined. A classic model of hierarchical attractors, which could potentially capture the tree-based hierarchy, is an extension of the Hopfield network where the stored pattern is hierarchically arranged into a tree (Parga and Virasoro, 1986; Figure 9). This class of models was originally proposed to explain the effect of word semantic categorization during memory encoding, storage, and retrieval. A contamination between the domains of naturalistic behavior and natural language processing has been proposed early on (Dawkins, 1976) and recently applied to describe C. elegans (Gomez-Marin et al., 2016; Gupta and Gomez-Marin, 2019) and larval zebrafish behavior (Reddy et al., 2022). It is still an open question to determine the precise structure of the behavioral hierarchy in mammals. The results of Marshall et al., 2021 seem to suggest that the hierarchy in rats may not be tree-like as the one observed in flies (Berman et al., 2016). However, the analysis of the rodent behavior in Marshall et al., 2021 lacked the information bottleneck step, which was crucial to infer the structure of the fly hierarchy at long timescale. On general grounds, it would be interesting to investigate whether the mammalian hierarchical motor system allows for flexible and adaptable behaviors, by utilizing the same motor elements across different sequences in a hierarchical manner, as represented in the mesoscopic circuit models of Murray and Escola, 2017; Logiaco and Escola, 2020; Logiaco et al., 2021 (see Figure 5) and suggested by experimental evidence in Geddes et al., 2018. This flexible hierarchy could be realized by overlapping branches and would be different from the tree-like structure found in the fly.

Are the computational mechanisms generating the tree-like hierarchy sufficient to generate flexible hierarchies? Recent theoretical work highlighted the importance of a neural circuit architecture that segregates the motor preparation and execution in different areas. In the cortico–thalamic–basal ganglia model of Figure 5B, flexible sequences can be generated by rearranging existing motifs as well as by learning new motifs without interfering with previous ones (Logiaco et al., 2021). Moreover, flexible sequences can be learned using biologically plausible Hebbian plasticity in the striatum (Murray and Escola, 2017). These theoretical models provide an exciting blueprint for further investigation into learning and expression of flexible behavioral hierarchies.

What are potential neural implementations of this network architecture? In the case of mammals, at the lowest level of the hierarchy, neural populations in the rodent secondary motor cortex (M2) generate metastable attractors representing upcoming actions (Inagaki et al., 2019; Recanatesi et al., 2022). Other subcortical areas, such as the basal ganglia or the thalamus, are likely involved in the generation of complex behavioral sequences (Murray and Escola, 2017; Logiaco et al., 2021; Recanatesi et al., 2022). How are higher-order behavioral units, such as sequences and activities, encoded? One possibility is that they could also be encoded in M2 attractors as recent evidence suggests M2 populations encode for a wide range of behavioral variables (Cazettes et al., 2021). We hypothesize an architecture featuring nested assemblies where larger populations, whose activity varies over progressively slower timescales (Stern et al., 2021; Schaub et al., 2015), hierarchically encode for slower features of behavior (from actions to sequences to activities). Experimental evidence shows that single neurons in M2 are selective to multiple actions (Recanatesi et al., 2022), suggesting that mixed selectivity (Rigotti et al., 2013) could play an important role in generating the high-dimensional hierarchical attractor landscape necessary to capture the complexity of naturalistic behavior. An alternative architecture might involve a distributed cortical circuit where the neural representations of behavioral units at different levels of the hierarchy are encoded in multiple frontal cortical areas, such as action sequences in M2, and activities in prefrontal cortex, which is known to control behavior on longer timescales such as trial history effects (Murakami et al., 2014; Murakami et al., 2017; Schreiner et al., 2021). In both scenarios, we hypothesize that while behavioral units are encoded as cortical attractors, transitions between attractor rely on feedback loops involving cortico–subcortical circuits (Murray and Escola, 2017; Logiaco et al., 2021; Recanatesi et al., 2022).

Behavioral energy landscape

The structure of naturalistic behavior emerging on long timescales in the fruit fly is a hierarchical tree-like organization (Figure 7A), which is consistent with an underlying neural circuit architecture based on hierarchical basins of attractions (Figure 9). Can we derive a representation of behavior as a complex energy landscape directly from the behavioral data itself? A promising approach is given by the probability density map approach (Berman et al., 2016). One could define the log-probability of the density map as an energy potential, where the transition rates between behavioral features yield a probability flux along this potential landscape. It is tantalizing to speculate that using the combination of energy gradient and probability flux one could derive a data-driven nonlinear dynamical system describing behavior (Wang, 2015). Other approaches to infer a potential energy landscape directly from data have been successfully applied to spike trains (Genkin and Engel, 2020; Duncker et al., 2019), although in a regime where the energy landscape has only a few minima. Alternatively, in chemical kinetics or fluid flow, methods based on the transfer operator formalism have been applied to find effective free energy landscapes and metastable states from experimental data or simulations (Bowman et al., 2013), and have been recently applied to animal behavior (Costa et al., 2021).

Neural mechanisms of flexible behavioral hierarchies

The model of nested basins of attractions proposed in Figure 9 can explain a tree-like behavioral hierarchy, where behavioral units at lower levels (e.g., actions) are not shared by different units at a higher level (e.g., sequences), as observed in fruit flies (Berman et al., 2016). Although the tree-like hierarchical organization emerged from assays where individual animals were monitored in isolation, recent studies of social behavior seem to challenge this structure. A large variety of qualitatively new behaviors arise from social interactions, including fighting, mating, and others. While some of these behaviors involve specialized behavioral units, others involve simultaneous execution of multiple behavioral units leading to multitasking. For example, during courtship, a male fly can simultaneously approach a female fly (locomotion) and sing, two behaviors that would be mutually exclusive in the absence of a female fly. As a consequence, the simple tree-like hierarchical organization of behavior observed in isolated individuals (Figure 8) might break down during social interactions and lead to a flexible organization where actions are shared between multiple sequences and activities.

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Author details

1. Luca Mazzucato

   Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of Oregon, Eugene, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   For correspondence

   lmazzuca@uoregon.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8525-7539

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