Multiple timescales of neural dynamics and integration of task-relevant signals across cortex

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior

Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.

A special role for anterior cingulate cortex, but not orbitofrontal cortex or basolateral amygdala, in choices involving information

Subjects are often willing to pay a cost for information. In a procedure that promotes paradoxical choices, animals choose between a richer option followed by a cue that is rewarded 50% of the time (No Info) vs. a leaner option followed by one of two cues that signal certain outcomes: one always rewarded (100%) and the other never rewarded, 0% (Info). Since decisions involve comparing the subjective value of options after integrating all their features, preference for information may rely on cortico-amygdalar circuitry. To test this, male and female rats were prepared with bilateral inhibitory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) in the anterior cingulate cortex, orbitofrontal cortex, basolateral amygdala, or null virus (control). We inhibited these regions after stable preference was acquired. We found that inhibition of the anterior cingulate cortex destabilized choice preference in female rats without affecting latency to choose or response rate to cues. A logistic regression fit revealed that previous choice predicted current choice in all conditions, however previously rewarded Info trials strongly predicted preference in all conditions except in female rats following anterior cingulate cortex inhibition. The results reveal a causal, sex-dependent role for the anterior cingulate cortex in decisions involving information.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas' relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.

Curiosity: primate neural circuits for novelty and information seeking

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

Evidence for Multiscale Multiplexed Representation of Visual Features in EEG

Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.

The Primate Cortical LFP Exhibits Multiple Spectral and Temporal Gradients and Widespread Task-Dependence During Visual Short-Term Memory

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3-80 Hz that differ between the two monkeys. The LFP power in each band, as well as the Sample Entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in layer 3 pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

A special role for anterior cingulate cortex, but not orbitofrontal cortex or basolateral amygdala, in choices involving information

Subjects often are willing to pay a cost for information. In a procedure that promotes paradoxical choices, animals choose between a richer option followed by a cue that is rewarded 50% of the time (No-info) vs a leaner option followed by one of two cues that signal certain outcomes: one always rewarded (100%), and the other never rewarded, 0% (Info). Since decisions involve comparing the subjective value of options after integrating all their features, preference for information may rely on cortico-amygdalar circuitry. To test this, male and female rats were prepared with bilateral inhibitory DREADDs in the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), basolateral amygdala (BLA), or null virus (control). We inhibited these regions after stable preference was acquired. We found that inhibition of ACC destabilized choice preference in female rats without affecting latency to choose or response rate to cues. A logistic regression fit revealed that the previous choice strongly predicted preference in control animals, but not in female rats following ACC inhibition. The results reveal a causal, sex-dependent role for ACC in decisions involving information.

Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior

Animals use past experience to guide future choices. The integration of experiences typically follows a hyperbolic, rather than exponential, decay pattern with a heavy tail for distant history. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends. However, it is unknown how the brain implements hyperbolic integration. We found that mouse behavior in a foraging task showed hyperbolic decay of past experience, but the activity of cortical neurons showed exponential decay. We resolved this apparent mismatch by observing that cortical neurons encode history information with heterogeneous exponential time constants that vary across neurons. A model combining these diverse timescales recreated the heavy-tailed, hyperbolic history integration observed in behavior. In particular, the time constants of retrosplenial cortex (RSC) neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced behavioral history dependence. These results indicate that behavior-relevant history information is maintained across multiple timescales in parallel and that RSC is a critical reservoir of information guiding decision-making.

Reconstructing computational system dynamics from neural data with recurrent neural networks

Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.

Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Significance statement

Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys' lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.

Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.

A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time

This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform temporal scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signals in real-time scenarios when the regular Gaussian kernel cannot be used, because of its non-causal access to information from the future, and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. We describe how the time-causal limit kernel relates to previously used temporal models, such as Koenderink's scale-time kernels and the ex-Gaussian kernel. We do also give an overview of how the time-causal limit kernel can be used for modelling the temporal processing in models for spatio-temporal and spectro-temporal receptive fields, and how it more generally has a high potential for modelling neural temporal response functions in a purely time-causal and time-recursive way, that can also handle phenomena at multiple temporal scales in a theoretically well-founded manner. We detail how this theory can be efficiently implemented for discrete data, in terms of a set of recursive filters coupled in cascade. Hence, the theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time. We conclude by stating implications of the theory for modelling temporal phenomena in biological, perceptual, neural and memory processes by mathematical models, as well as implications regarding the philosophy of time and perceptual agents. Specifically, we propose that for A-type theories of time, as well as for perceptual agents, the notion of a non-infinitesimal inner temporal scale of the temporal receptive fields has to be included in representations of the present, where the inherent nonzero temporal delay of such time-causal receptive fields implies a need for incorporating predictions from the actual time-delayed present in the layers of a perceptual hierarchy, to make it possible for a representation of the perceptual present to constitute a representation of the environment with timing properties closer to the actual present.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work has demonstrated remarkably reproducible and consistent hierarchies of neural timescales across cortical areas at rest. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this previously found lack of variability in the hierarchical organization of neural timescales could be a reflection of the structure of the laboratory contexts in which they were measured. Indeed, computational work demonstrates the existence of multiple temporal hierarchies within the same anatomical network when the input structure is altered. We posit that unconstrained behavioral environments where relatively little temporal demands are imposed from the experimenter are an ideal test bed to address the question of whether the hierarchical organization and the magnitude of neural timescales reflect ongoing behavioral demands. To tackle this question, we measured timescales of local field potential activity while rhesus macaques were foraging freely in a large open space. We find a hierarchy of neural timescales that is unique to this foraging environment. Importantly, although the magnitude of neural timescales generally expanded with task engagement, the brain areas' relative position in the hierarchy was stable across the recording sessions. Notably, the magnitude of neural timescales monotonically expanded with task engagement across a relatively long temporal scale spanning the duration of the recording session. Over shorter temporal scales, the magnitude of neural timescales changed dynamically around foraging events. Moreover, the change in the magnitude of neural timescales contained functionally relevant information, differentiating between seemingly similar events in terms of motor demands and associated reward. That is, the patterns of change were associated with the cognitive and behavioral meaning of these events. Finally, we demonstrated that brain areas were differentially affected by these behavioral demands - i.e., the expansion of neural timescales was not the same across all areas. Together, these results demonstrate that the observed hierarchy of neural timescales is context-dependent and that changes in the magnitude of neural timescales are closely related to overall task engagement and behavioral demands.

Transcranial magnetic stimulation to the angular gyrus modulates the temporal dynamics of the hippocampus and entorhinal cortex

Transcranial magnetic stimulation (TMS) delivered to the angular gyrus (AG) affects hippocampal function and associated behaviors (Thakral PP, Madore KP, Kalinowski SE, Schacter DL. Modulation of hippocampal brain networks produces changes in episodic simulation and divergent thinking. 2020a. Proc Natl Acad Sci U S A. 117:12729-12740). Here, we examine if functional magnetic resonance imaging (fMRI)-guided TMS disrupts the gradient organization of temporal signal properties, known as the temporal organization, in the hippocampus (HPC) and entorhinal cortex (ERC). For each of 2 TMS sessions, TMS was applied to either a control site (vertex) or to a left AG target region (N = 18; 14 females). Behavioral measures were then administered, and resting-state scans were acquired. Temporal dynamics were measured by tracking change in the fMRI signal (i) "within" single voxels over time, termed single-voxel autocorrelation and (ii) "between" different voxels over time, termed intervoxel similarity. TMS reduced AG connectivity with the hippocampal target and induced more rapid shifting of activity in single voxels between successive time points, lowering the single-voxel autocorrelation, within the left anteromedial HPC and posteromedial ERC. Intervoxel similarity was only marginally affected by TMS. Our findings suggest that hippocampal-targeted TMS disrupts the functional properties of the target site along the anterior/posterior axis. Further studies should examine the consequences of altering the temporal dynamics of these medial temporal areas to the successful processing of episodic information under task demand.

Foundations of a temporal RL

Recent advances in neuroscience and psychology show that the brain has access to timelines of both the past and the future. Spiking across populations of neurons in many regions of the mammalian brain maintains a robust temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can estimate an extended temporal model of the future, suggesting that the neural timeline of the past could extend through the present into the future. This paper presents a mathematical framework for learning and expressing relationships between events in continuous time. We assume that the brain has access to a temporal memory in the form of the real Laplace transform of the recent past. Hebbian associations with a diversity of synaptic time scales are formed between the past and the present that record the temporal relationships between events. Knowing the temporal relationships between the past and the present allows one to predict relationships between the present and the future, thus constructing an extended temporal prediction for the future. Both memory for the past and the predicted future are represented as the real Laplace transform, expressed as the firing rate over populations of neurons indexed by different rate constants s. The diversity of synaptic timescales allows for a temporal record over the much larger time scale of trial history. In this framework, temporal credit assignment can be assessed via a Laplace temporal difference. The Laplace temporal difference compares the future that actually follows a stimulus to the future predicted just before the stimulus was observed. This computational framework makes a number of specific neurophysiological predictions and, taken together, could provide the basis for a future iteration of RL that incorporates temporal memory as a fundamental building block.

The zona incerta in control of novelty seeking and investigation across species

Many organisms rely on a capacity to rapidly replicate, disperse, and evolve when faced with uncertainty and novelty. But mammals do not evolve and replicate quickly. They rely on a sophisticated nervous system to generate predictions and select responses when confronted with these challenges. An important component of their behavioral repertoire is the adaptive context-dependent seeking or avoiding of perceptually novel objects, even when their values have not yet been learned. Here, we outline recent cross-species breakthroughs that shed light on how the zona incerta (ZI), a relatively evolutionarily conserved brain area, supports novelty-seeking and novelty-related investigations. We then conjecture how the architecture of the ZI's anatomical connectivity - the wide-ranging top-down cortical inputs to the ZI, and its specifically strong outputs to both the brainstem action controllers and to brain areas involved in action value learning - place the ZI in a unique role at the intersection of cognitive control and learning.

Differences in temporal processing speeds between the right and left auditory cortex reflect the strength of recurrent synaptic connectivity

Brain asymmetry in the sensitivity to spectrotemporal modulation is an established functional feature that underlies the perception of speech and music. The left auditory cortex (ACx) is believed to specialize in processing fast temporal components of speech sounds, and the right ACx slower components. However, the circuit features and neural computations behind these lateralized spectrotemporal processes are poorly understood. To answer these mechanistic questions we use mice, an animal model that captures some relevant features of human communication systems. In this study, we screened for circuit features that could subserve temporal integration differences between the left and right ACx. We mapped excitatory input to principal neurons in all cortical layers and found significantly stronger recurrent connections in the superficial layers of the right ACx compared to the left. We hypothesized that the underlying recurrent neural dynamics would exhibit differential characteristic timescales corresponding to their hemispheric specialization. To investigate, we recorded spike trains from awake mice and estimated the network time constants using a statistical method to combine evidence from multiple weak signal-to-noise ratio neurons. We found longer temporal integration windows in the superficial layers of the right ACx compared to the left as predicted by stronger recurrent excitation. Our study shows substantial evidence linking stronger recurrent synaptic connections to longer network timescales. These findings support speech processing theories that purport asymmetry in temporal integration is a crucial feature of lateralization in auditory processing.

Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.

Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Cortical areas seem to form a hierarchy of intrinsic timescales, but the relevance of this organization for cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of all tested areas affected the evidence-accumulation computation. Specifically, we observed distinct changes in the weighting of sensory evidence occurring during and before silencing, such that frontal inactivations led to stronger deficits on long timescales than posterior cortical ones. Inactivation of a subset of frontal areas also led to moderate effects on behavioral processes beyond evidence accumulation. Moreover, large-scale cortical Ca2+ activity during task performance displayed different temporal integration windows. Our findings suggest that the intrinsic timescale hierarchy of distributed cortical areas is an important component of evidence-accumulation mechanisms.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

Inhibitory control of frontal metastability sets the temporal signature of cognition

Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

A flexible Bayesian framework for unbiased estimation of timescales

Timescales characterize the pace of change for many dynamic processes in nature. Timescales are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure often fails to recover the correct timescales due to a statistical bias arising from the finite sample size. We develop an alternative approach which estimates timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein-Uhlenbeck (OU) processes using adaptive approximate Bayesian computations (aABC). Our method accounts for finite sample size and noise in data and returns a posterior distribution of timescales that quantifies the estimation uncertainty and can be used for model selection. We demonstrate the accuracy of our method on synthetic data and illustrate its application to recordings from primate cortex. We provide a customizable Python package implementing our framework with different generative models suitable for diverse applications.

Individual Differences in Multisensory Processing Are Related to Broad Differences in the Balance of Local versus Distributed Information

The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy young adults completed an audiovisual simultaneity judgment task to measure their temporal binding window (TBW), which quantifies the ability to distinguish fine discrepancies in timing between auditory and visual stimuli. EEG was then recorded during a second run of the simultaneity judgment task, and partial least squares was used to relate individual differences in the TBW width to source-localized EEG measures of local entropy and mutual information, indexing local and distributed processing of information, respectively. The narrowness of the TBW, reflecting more effective multisensory processing, was related to a broad pattern of higher mutual information and lower local entropy at multiple timescales. Furthermore, a small group of temporal and frontal cortical regions, including those previously implicated in multisensory integration and response selection, respectively, played a prominent role in this pattern. Overall, these findings suggest that individual differences in multisensory processing are related to widespread individual differences in the balance of distributed versus local information processing among a large subset of brain regions, with more distributed information being associated with more effective multisensory processing. The balance of distributed versus local information processing may therefore be a useful measure for exploring individual differences in multisensory processing, its relationship to higher cognitive traits, and its disruption in neurodevelopmental disorders and clinical conditions.

Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short timescales whereas higher-association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense interareal cortical connectivity known in the modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: 1) a macroscopic gradient of synaptic excitation, 2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and 3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks.

Intrinsic neural timescales: temporal integration and segregation

We are continuously bombarded by external inputs of various timescales from the environment. How does the brain process this multitude of timescales? Recent resting state studies show a hierarchy of intrinsic neural timescales (INT) with a shorter duration in unimodal regions (e.g., visual cortex and auditory cortex) and a longer duration in transmodal regions (e.g., default mode network). This unimodal-transmodal hierarchy is present across acquisition modalities [electroencephalogram (EEG)/magnetoencephalogram (MEG) and fMRI] and can be found in different species and during a variety of different task states. Together, this suggests that the hierarchy of INT is central to the temporal integration (combining successive stimuli) and segregation (separating successive stimuli) of external inputs from the environment, leading to temporal segmentation and prediction in perception and cognition.

Modelling the perception of music in brain network dynamics

We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range in a time-dependent manner correlated with musical large-scale form, showing increased synchronization just before transitions between different parts in a musical piece (musical high-level events). The results also suggest a separation in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions.

Interactions between ventrolateral prefrontal and anterior cingulate cortex during learning and behavioural change

Hypotheses and beliefs guide credit assignment - the process of determining which previous events or actions caused an outcome. Adaptive hypothesis formation and testing are crucial in uncertain and changing environments in which associations and meanings are volatile. Despite primates' abilities to form and test hypotheses, establishing what is causally responsible for the occurrence of particular outcomes remains a fundamental challenge for credit assignment and learning. Hypotheses about what surprises are due to stochasticity inherent in an environment as opposed to real, systematic changes are necessary for identifying the environment's predictive features, but are often hard to test. We review evidence that two highly interconnected frontal cortical regions, anterior cingulate cortex and ventrolateral prefrontal area 47/12o, provide a biological substrate for linking two crucial components of hypothesis-formation and testing: the control of information seeking and credit assignment. Neuroimaging, targeted disruptions, and neurophysiological studies link an anterior cingulate - 47/12o circuit to generation of exploratory behaviour, non-instrumental information seeking, and interpretation of subsequent feedback in the service of credit assignment. Our observations support the idea that information seeking and credit assignment are linked at the level of neural circuits and explain why this circuit is important for ensuring behaviour is flexible and adaptive.

Computational models of adaptive behavior and prefrontal cortex

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

Computational mechanisms of distributed value representations and mixed learning strategies

Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

State-Dependent Effective Connectivity in Resting-State fMRI

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

Entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Timescales of Cognition in the Brain

We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.

The brain and its time: intrinsic neural timescales are key for input processing

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

Value signals guide abstraction during learning

The human brain excels at constructing and using abstractions, such as rules, or concepts. Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features. Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence. We also found that the brain area computing value signals – the ventromedial prefrontal cortex – prioritised and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation. Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction. Tagging the neural representation of a task feature with rewards evoked abstraction-based decisions. Together, these findings provide a novel interpretation of value as a goal-dependent, key factor in forging abstract representations.

Moving a Shape behind a Slit: Partial Shape Representations in Inferior Temporal Cortex

Current models of object recognition are based on spatial representations build from object features that are simultaneously present in the retinal image. However, one can recognize an object when it moves behind a static occlude, and only a small fragment of its shape is visible through a slit at a given moment in time. Such anorthoscopic perception requires spatiotemporal integration of the successively presented shape parts during slit-viewing. Human fMRI studies suggested that ventral visual stream areas represent whole shapes formed through temporal integration during anorthoscopic perception. To examine the time course of shape-selective responses during slit-viewing, we recorded the responses of single inferior temporal (IT) neurons of rhesus monkeys to moving shapes that were only partially visible through a static narrow slit. The IT neurons signaled shape identity by their response when that was cumulated across the duration of the shape presentation. Their shape preference during slit-viewing equaled that for static, whole-shape presentations. However, when analyzing their responses at a finer time scale, we showed that the IT neurons responded to particular shape fragments that were revealed by the slit. We found no evidence for temporal integration of slit-views that result in a whole-shape representation, even when the monkey was matching slit-views of a shape to static whole-shape presentations. These data suggest that, although the temporally integrated response of macaque IT neurons can signal shape identity in slit-viewing conditions, the spatiotemporal integration needed for the formation of a whole-shape percept occurs in other areas, perhaps downstream to IT.SIGNIFICANCE STATEMENT One recognizes an object when it moves behind a static occluder and only a small fragment of its shape is visible through a static slit at a given moment in time. Such anorthoscopic perception requires spatiotemporal integration of the successively presented partial shape parts. Human fMRI studies suggested that ventral visual stream areas represent shapes formed through temporal integration. We recorded the responses of inferior temporal (IT) cortical neurons of macaques during slit-viewing conditions. Although the temporally summated response of macaque IT neurons could signal shape identity under slit-viewing conditions, we found no evidence for a whole-shape representation using analyses at a finer time scale. Thus, the spatiotemporal integration needed for anorthoscopic perception does not occur within IT.

Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task

Cortical activity related to erroneous behavior in discrimination or decision-making tasks is rarely analyzed, yet it can help clarify which computations are essential during a specific task. Here, we use a hidden Markov model (HMM) to perform a trial-by-trial analysis of the ensemble activity of dorsolateral prefrontal cortex (PFdl) neurons of rhesus monkeys performing a distance discrimination task. By segmenting the neural activity into sequences of metastable states, HMM allows us to uncover modulations of the neural dynamics related to internal computations. We find that metastable dynamics slow down during error trials, while state transitions at a pivotal point during the trial take longer in difficult correct trials. Both these phenomena occur during the decision interval, with errors occurring in both easy and difficult trials. Our results provide further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

Invariant timescale hierarchy across the cortical somatosensory network

The ability of cortical networks to integrate information from different sources is essential for cognitive processes. On one hand, sensory areas exhibit fast dynamics often phase-locked to stimulation; on the other hand, frontal lobe areas with slow response latencies to stimuli must integrate and maintain information for longer periods. Thus, cortical areas may require different timescales depending on their functional role. Studying the cortical somatosensory network while monkeys discriminated between two vibrotactile stimulus patterns, we found that a hierarchical order could be established across cortical areas based on their intrinsic timescales. Further, even though subareas (areas 3b, 1, and 2) of the primary somatosensory (S1) cortex exhibit analogous firing rate responses, a clear differentiation was observed in their timescales. Importantly, we observed that this inherent timescale hierarchy was invariant between task contexts (demanding vs. nondemanding). Even if task context severely affected neural coding in cortical areas downstream to S1, their timescales remained unaffected. Moreover, we found that these time constants were invariant across neurons with different latencies or coding. Although neurons had completely different dynamics, they all exhibited comparable timescales within each cortical area. Our results suggest that this measure is demonstrative of an inherent characteristic of each cortical area, is not a dynamical feature of individual neurons, and does not depend on task demands.

A Diversity of Intrinsic Timescales Underlie Neural Computations

Neural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of these representations allows animals to behave adaptively. Short temporal windows facilitate adaptive responding in dynamic environments, while longer temporal windows promote the gradual integration of information across time. In the cognitive and motor domains, the brain sets overarching goals to be achieved within a long temporal window, which must be broken down into sequences of actions and precise movement control processed across much shorter temporal windows. Previous human neuroimaging studies and large-scale artificial network models have ascribed different processing timescales to different cortical regions, linking this to each region's position in an anatomical hierarchy determined by patterns of inter-regional connectivity. However, even within cortical regions, there is variability in responses when studied with single-neuron electrophysiology. Here, we review a series of recent electrophysiology experiments that demonstrate the heterogeneity of temporal receptive fields at the level of single neurons within a cortical region. This heterogeneity appears functionally relevant for the computations that neurons perform during decision-making and working memory. We consider anatomical and biophysical mechanisms that may give rise to a heterogeneity of timescales, including recurrent connectivity, cortical layer distribution, and neurotransmitter receptor expression. Finally, we reflect on the computational relevance of each brain region possessing a heterogeneity of neuronal timescales. We argue that this architecture is of particular importance for sensory, motor, and cognitive computations.

Anterior Cingulate Cortex and the Control of Dynamic Behavior in Primates

The brain mechanism for controlling continuous behavior in dynamic contexts must mediate action selection and learning across many timescales, responding differentially to the level of environmental uncertainty and volatility. In this review, we argue that a part of the frontal cortex known as the anterior cingulate cortex (ACC) is particularly well suited for this function. First, the ACC is interconnected with prefrontal, parietal, and subcortical regions involved in valuation and action selection. Second, the ACC integrates diverse, behaviorally relevant information across multiple timescales, producing output signals that temporally encapsulate decision and learning processes and encode high-dimensional information about the value and uncertainty of future outcomes and subsequent behaviors. Third, the ACC signals behaviorally relevant information flexibly, displaying the capacity to represent information about current and future states in a valence-, context-, task- and action-specific manner. Fourth, the ACC dynamically controls instrumental- and non-instrumental information seeking behaviors to resolve uncertainty about future outcomes. We review electrophysiological and circuit disruption studies in primates to develop this point, discuss its relationship to novel therapeutics for neuropsychiatric disorders in humans, and conclude by relating ongoing research in primates to studies of medial frontal cortical regions in rodents.

Learning from Others, but with What Confidence?

A recent study by Zhang and Gläscher (2020) in humans examines learning from one's own versus others' actions under reward uncertainty. Comparing findings from this and non-human studies on learning under perceptual uncertainty suggests a unified role for confidence in learning under different types of uncertainty across mammalian brains.

Multiple timescales of neural dynamics and integration of task-relevant signals across cortex

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.