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Temporal cognition and neural oscillations
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ScienceDirect Temporal cognition and neural oscillations Virginie van Wassenhove There remains considerable controversy concerning how time is represented in the brain and the lack of consensus has presented a challenge for our understanding of biologically plausible timing mechanisms. In a computational brain, (continuous) time should be discretized. For instance, in Bayesian models of brain functions, the discretization of time raises issues such as what duration or prediction interval should be used to compute the probability that a discrete event (e.g. a neural spike) will occur. Typically, the researcher in the lab defines durations a priori (e.g. the window within which we count spikes) but how the brain sets its own duration or temporal boundary to accumulate evidence for representing time is unclear. When dealing with space, spatiotopic mapping seen throughout cortex intrinsically provides the spatial reference for information processing; by analogy, when dealing with time, neural oscillations are hypothesized to provide a built-in chronoarchitecture for information processing. However, how such structuring principles become conscious and how time becomes intelligible to the mind remains unclear. Address Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Universite´ Paris-Sud, Universite´ Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France Corresponding author: van Wassenhove, Virginie ([email protected])
Current Opinion in Behavioural Sciences 2016, 8:124–130 This review comes from a themed issue on Timing behaviour Edited by Richard B Ivry and Warren H Meck
http://dx.doi.org/10.1016/j.cobeha.2016.02.012 2352-1546/# 2016 Elsevier Ltd. All rights reserved.
Temporal cognition distinguishes two ways in which the nonverbal brain represents time: the interval sense and the phase sense [1,2]. Interval timing is the ability of an organism to quantify an interval of time and compare event durations; it is characterized by scalar properties [3,4,5]. The phase sense refers to the internal mapping of events in time, supporting the ability to predict when an event will occur in a given period of time. Distinguishing interval timing from phase sense is functionally relevant because it allows positing distinct neural operations for handling time in the brain: whereas the mapping of events in time (phase sense) requires a mechanism to Current Opinion in Behavioral Sciences 2016, 8:124–130
encode or timestamp events in a cognitive map (in the manner we consciously map historical events on a calendar), estimating duration requires the quantification of internal distances and relational operations between two recorded quantities [1,6,7]. Both are essential and complementary facets of temporal cognition.
Passive biological clocks versus predictive neural oscillations Living matter is characterized by quantifiable cyclic processes which define time moments (Figure 1a). Generically, biological clocks are innate physiological processes [8] which fluctuate over time, in phase with the rhythmic properties of their environment (Figure 1b) [9,10]. They are found across functional scales from molecules to behaviour [11]. Curiously however, if the activity of a biological system was fully driven by changes in the environment, intrinsic rhythms would be unnecessary for the organism; rhythms observed in the absence of entrainment would be a non-parsimonious and energetically costly characteristic of a biological system. Yet, intrinsic rhythms subsist in the absence of entrainment (Figure 1b). In the mammalian brain, three oscillatory neural structures display autonomous rhythms: the retina, the suprachiasmatic nucleus and the olfactory bulb [9]. These structures are considered master clocks which can enslave neural oscillators in the cortex and cerebellum although spontaneous neuronal oscillations can readily be observed from single cells to neural networks [12,13,14,15–17]. Cyclic processes characterize life but what fundamentally distinguishes animals from other living matter is their capacity to generate timing in a manner that remains coherent with, yet partially independent from, the temporal properties of their environment. It is here argued that for time to become conscious — and not solely the chronoarchitecture onto which all cognitive operations are built — brain rhythms need to uncouple from external rhythms and actively generate timing. Perhaps an obvious illustration of uncoupling or temporal individuation is an animal’s capacity for locomotion that is the ability to change its space-time configuration with respect to its environment. While well-described neural central-pattern generators in basic vertebrates such as the lamprey generate rhythms that are nearly isomorphic to the animal’s undulations [18], as the complexity of microcircuitry increases, this one-to-one mapping between rhythm generators and expressed motion or behaviour disappears. Instead, intentional movements take over and temporally regulate information through active sensing www.sciencedirect.com
Temporal cognition and neural oscillations van Wassenhove 125
Figure 1
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Current Opinion in Behavioral Sciences
Representing and quantifying time at different functional scales. (a) Let’s imagine a magnetoencephalography or electroencephalography (M/EEG) study in which brain responses to two experimental conditions were collected. M/EEG responses recorded in condition 2 (red trials) appear better aligned in time than those of condition 1 (black). Bottom left: each oscillatory response in a trial can be characterized by its amplitude and its period. Bottom right: oscillatory responses in one condition can be described as the distribution of phase responses across trials. In grey, a uniform distribution suggests no preferential phase in condition 1 although clear alpha bursts (8–12 Hz) are seen in the time domain (top graph, black traces). To the contrary, a preferential phase distribution is observed in the red condition consistent with the alignment seen in the time domain (bottom graph, red traces). (b) Circadian clocks and behaviour. Top panel: in this example, an animal shows four periods of activity in one day under natural day/night conditions. Bottom panel: when taken away from the circadian cycle, the animal’s behaviour keeps its rhythmic activity (frequency maintained) but slowly drifts away from the day/night cycle (phase change). (c). Frequency-tagging and entrainment of neuronal responses. Top panel: fast transient changes (e.g. a visual grating at 15 Hz) elicit a steady-state response in visual cortex. Similar steady-state responses can be obtained in auditory and tactile sensory modalities. Middle panel: neural entrainment can be observed at slower rhythms, for instance when a sound is presented every 500 ms or at 2 Hz. In both examples, the mean neural responses recorded with M/EEG is phase-locked to the presentation of the stimuli [(e.g. 44)]. Bottom panel: example of temporal individuation. Contrarily to the prediction that the phase of steadystate responses should remain constant throughout the stimulation period, the phase of entrained neural oscillations can shift in a manner consistent with participants’ conscious temporal order perception [43].
[19] thus establishing a closed-loop circuitry for information processing in cortex [20]. In the absence of movement, neural oscillations can thus be conceived as internalized time operators which modulate the information sampling in the environment thereby implementing attentional processes [19–22]. www.sciencedirect.com
With respect to temporal cognition specifically, a novel feature of neural oscillations is that they operate on selftimed inputs suggesting that closed-loop circuitries can build temporal individuation which includes an animal’s capacity to represent information. However, this individuation can only exist if brain rhythms can uncouple from Current Opinion in Behavioral Sciences 2016, 8:124–130
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the environmental rhythms so as to build a stable system for mental representations and insure invariant coordinates for the representation of the self in space-time. For instance, in his criticism of the ‘platonic’ or idealized threshold neuron, Llina´s suggested that spontaneous activity was crucially informative on the vectorial coordinate space in which sensorimotor transformations occurred [15], which is a fundamental architectural need for space-time representations supporting cognition [1,6]. More recently, algorithms for spatial navigation have been proposed to be recycled for mental functions, some of which fundamentally entail neural oscillations as a temporal metric [23]. In other words, in the absence of movement, neural oscillations may provide a mental metric for the representation of time [24–28]. If demonstrated, the uncoupling of brain rhythms from environmental rhythms are a crucial first step towards the possibility of autonomous, predictive and iterative processing of temporal content in the mind.
Neural oscillations and the individuation of ‘intelligent agents’ An important and influential observation in the field is that neural oscillators can be entrained by rhythmic sensory information that is closest to their resonant frequency (Figure 1c). Although neural oscillations are sometimes used generically to describe the rhythmic fluctuations of neural responses over time, it is important to emphasize that the level of observation defines the nature of neural oscillations being described — hence, their possible function and type of informational content being handled. These have been thoroughly reviewed elsewhere [14,29]: microscopic membrane fluctuations and intrinsic neural properties can lead to resonant properties of the cell; at the mesoscopic scale, neural oscillations reflect the mean activity of neural population; at the macroscopic scale, rhythmic fluctuations capture inter-areal synchronizations over time and space [29,30– 32]. In non-invasive human recordings (e.g. M/EEG), it is theoretically impossible to demonstrate that evoked activity results from phase-resetting of ongoing oscillatory activity [33] but reasonable inferences on the origins of the signal and the use of mathematically informed approaches can provide useful criteria for interpretation [34,35]. In this context, neural entrainment can shed light on the propensity of neurons and neural populations to ‘resonate’ with sensory stimuli, and address not only how external temporal structure can be internalized in brain responses but more importantly, how brain responses can dissociate from external temporal cues to represent an individual’s time experience. Entrained neural responses tend to be seen as passive phenomena in which brain activity reacts or calibrates itself to the temporal statistics of the environment. Numerous studies have demonstrated that neural entrainment — that is temporally calibrating the Current Opinion in Behavioral Sciences 2016, 8:124–130
brain — increases the processing efficiency within and across sensory modalities [(e.g. 21,36,37–40)]. Because low-frequency neural oscillations phasically define windows of opportunities for neural spiking [14,29], entraining neural oscillations is hypothesized to align the optimal phase of neural oscillations to the external stimulation, thereby optimizing the encoding of sensory information. One important working hypothesis is thus that entrainment regulates implicit timing so as to benefit the encoding of generic — visual, speech, multisensory — content [41,42]. However, this also begs the question of the relationship between implicit and conscious timing. For instance, Hermann and colleagues [38] suggested that neural entrainment provides a reference frame for veridical time representation and that phase shifts of the entrained neural oscillations capture subjective timing. Consistent with this idea, and contrary to the notion of passive entrainment in typical phase-locked-responses, recent findings suggest that the phase response of entrained neural oscillations can depart from stationarity thus maintaining an endogenous internal metric for the representation of (space)time [43]. Specifically, rhythmic presentation of desynchronized audiovisual stimuli elicited the expected peak oscillatory response in sensory cortices at the entrainment rate (Figure 1c). As M/EEG records the mean oscillatory response of entrained neural oscillators, entrained responses should phase-lock steadily to the rhythmic presentation of the stimuli [44]. However, a shift in the phase of the entrained auditory response was observed which linearly predicted participants’ sensitivity to audiovisual temporal order following the entrainment period. These findings suggest that the brain tends to stabilize its internal time metric in spite of external temporal evidence so as to maintain a unitary perception of the most probable (multisensory) event out there. A growing body of evidence is consistent with the suggested hypothesis that endogenous phase-resetting and shifting mechanisms play an important role for conscious timing. For instance, using a foreperiod paradigm in which the probability of a target occurrence was manipulated, Cravo and colleagues [45] reported higher phaselocking values of theta oscillations for U-shaped as compared to skewed distributions; U-shaped probability distributions also elicited significantly more cross-frequency-coupling between theta phase (7 Hz) and beta power (20–25 Hz) than skewed distributions. These results were demonstrated that endogenous phase-reset can be elicited by temporal anticipation. This finding is important because it demonstrates that the phase of neural oscillations can be under endogenous control on the basis of an internalized representation of the temporal statistics of events. Endogenous phase adjustments have also been reported when participants anticipated a distractor during a working memory task [46]. Consistent www.sciencedirect.com
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with this, a decrease of power in the endogenous alpha rhythm has been reported at the moment at which an event was expected to occur [47,48]. The decrease of alpha synchronization may mark the end of an arousal state or alternatively, reflect the phase-resetting of new windows of opportunity for sensory processing [47]. The latter hypothesis was consistent with the temporal prioritization role assigned to alpha oscillations [49]. Recently, Samaha and colleagues [50] also reported that temporal cueing biased the phase of ongoing alpha oscillations
towards an optimal phase for visual discrimination, consistent with the modulation of phase responses in sensory cortices by attention [39,51–54]. Formal approaches supporting the internal hierarchal temporal organization have also been described [55,56] to serve temporal expectation [55,57,58]. The endogenous control of brain rhythms has been further illustrated by the elegant study of Nozaradan and colleagues [59] who showed that mentally generating an auditory beat elicited an increased oscillatory peak response at that frequency;
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Current Opinion in Behavioral Sciences
Temporal multiplexing and representing time in the brain. (a) In the striatal-beat-frequency model [3], the phase-resetting of cortical oscillators marks the onset of an interval and the state of the oscillators at stimulus offset is read out through coincidence detection in the striatum. The resulting beat frequency is the outcome of the comparison between cortical oscillators with stored durations in long-term memory (LTM). The SBF has been suggested to count cycles through oscillatory multiplexing [27]. (b) If WM actively participates in quantifying time, discretized temporal units can be stored and counted [67]. This requires steady precision of oscillatory frequencies as well as arithmetic operations on time units to free memory capacity as time unfolds and the number of discretized units increases. For instance, if 1 time unit is 200 ms (or one cycle of a theta oscillation), no more than 100 4 to 7 time units can be stored at once in WM before repeating the operation again. The 4–7 time units are integrated by cycle which necessitates additional readouts. Temporal integration could thus be realized as magnitude estimation through discretized time cycles [64]. Crucially, the decision-boundary may be set by the phase of the neural oscillation so that one oscillatory cycle defines the prediction interval over which accumulation of evidence is realized. The temporal metric could naturally be a decisive bound on evidence accumulation mechanisms. As predicted by WM models and algorithms for space-time encoding [23], cross-frequency-coupling could naturally keep track of event order. www.sciencedirect.com
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this response was observed in addition to the peak responses of brain rhythms entrained by the acoustic inputs as measured by EEG. Altogether, these results suggest that neural oscillations not only play a mechanistic role in information processing but also that the phase of neural oscillations plays a tangible role in representing conscious timing. The evidence for endogenous control of oscillatory neural responses suggests a stable coordinate system for the representation of time which is necessary for high-level temporal cognition.
Time as content Cross-frequency coupling (sp. coupling between the phase of theta/alpha and the power of gamma) has been proposed as a mechanism for working memory (WM) [(e.g. 49,60)]. Supra-second interval timing has recently been suggested to instantiate one such implementation of WM [27] capitalizing on early models of oscillatory mechanisms for time encoding [26,61]. This hypothesis provides a useful and empirically testable framework although it raises many questions regarding the specific role of WM for timing (Figure 2). For instance, in light of the recently demonstrated capacity to maintain sequences of quantified time units in WM [62], one could ask whether similar mechanisms can at once encode time as a magnitude and flag events in time for ordered sequence mapping. One testable hypothesis is that oscillatory cycles naturally bound the accumulation of evidence process during magnitude estimation and, importantly, intrinsically define the prediction interval over which probabilistic mapping can be made. Evidence for cyclic processes serving evidence accumulation is building [63], and the implication that the time dimension would constrain evidence accumulation during magnitude estimation has also been suggested [64]. Mapping time and the phase sense also entail ordering events in a temporal metric. Seminal studies have suggested that the phase of alpha oscillations could serve as such metric for the conscious ordering of events [22,65]. The phase of entrained neural oscillations may be informative, by revealing the invariant properties of an individual’s chronoarchitecture, not only through the characteristic frequencies of neural oscillators, but also their characteristic phase [43]. Whether the phase of different neural oscillatory frequencies can provide the metric for time and whether different frequencies code for different temporal granularities that are consciously intelligible remains highly speculative. Interestingly, the algorithm used for the spatiotemporal mapping of distances physically traversed by an animal has been speculated to serve distance computations of various mental contents [23]. In this context, the recent discovery of context-invariant prospective speed cells [66] provide Current Opinion in Behavioral Sciences 2016, 8:124–130
an important building-block in understanding internal representations of time as mental distances.
Conclusions In the predictive brain, analyzing information consists in computing the residual error between an internal mental model and incoming sensorimotor information. The internal model is a system of representations against which non-invariant inputs can be compared and updated. If time is rendered intelligible through prediction, representing time is also quantifying the extent of the temporal perturbations to the system within the metric of that system. Neural oscillations appear a fit candidate to capture internal time metrics and build a temporal self.
Conflict of interest statement Nothing declared.
Acknowledgements This work was supported by an ERC-YStG-263584 and an ANR10JCJC1904 to V.vW. Some of these ideas were presented at the recent ‘Power Law and Multiple Scales in Neural Systems’ workshop organized by the EITN, Paris (March, 2015). I thank the overall NeuroSpin and UNCOG stimulating work environment as well as the intelligent agents populating my team at the moment for their inquisitiveness on the last version of this manuscript, among which, Denis Engemann, Baptiste Gauthier, Laetitia Grabot, Tadeusz Kononowicz, Benoıˆt Martin and Ignacio Polti. I am grateful to Prof. Richard Ivry for his insightful and helpful comments on previous versions of this manuscript.
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ScienceDirect Temporal cognition and neural oscillations Virginie van Wassenhove There remains considerable controversy concerning how time is represented in the brain and the lack of consensus has presented a challenge for our understanding of biologically plausible timing mechanisms. In a computational brain, (continuous) time should be discretized. For instance, in Bayesian models of brain functions, the discretization of time raises issues such as what duration or prediction interval should be used to compute the probability that a discrete event (e.g. a neural spike) will occur. Typically, the researcher in the lab defines durations a priori (e.g. the window within which we count spikes) but how the brain sets its own duration or temporal boundary to accumulate evidence for representing time is unclear. When dealing with space, spatiotopic mapping seen throughout cortex intrinsically provides the spatial reference for information processing; by analogy, when dealing with time, neural oscillations are hypothesized to provide a built-in chronoarchitecture for information processing. However, how such structuring principles become conscious and how time becomes intelligible to the mind remains unclear. Address Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Universite´ Paris-Sud, Universite´ Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France Corresponding author: van Wassenhove, Virginie ([email protected])
Current Opinion in Behavioural Sciences 2016, 8:124–130 This review comes from a themed issue on Timing behaviour Edited by Richard B Ivry and Warren H Meck
http://dx.doi.org/10.1016/j.cobeha.2016.02.012 2352-1546/# 2016 Elsevier Ltd. All rights reserved.
Temporal cognition distinguishes two ways in which the nonverbal brain represents time: the interval sense and the phase sense [1,2]. Interval timing is the ability of an organism to quantify an interval of time and compare event durations; it is characterized by scalar properties [3,4,5]. The phase sense refers to the internal mapping of events in time, supporting the ability to predict when an event will occur in a given period of time. Distinguishing interval timing from phase sense is functionally relevant because it allows positing distinct neural operations for handling time in the brain: whereas the mapping of events in time (phase sense) requires a mechanism to Current Opinion in Behavioral Sciences 2016, 8:124–130
encode or timestamp events in a cognitive map (in the manner we consciously map historical events on a calendar), estimating duration requires the quantification of internal distances and relational operations between two recorded quantities [1,6,7]. Both are essential and complementary facets of temporal cognition.
Passive biological clocks versus predictive neural oscillations Living matter is characterized by quantifiable cyclic processes which define time moments (Figure 1a). Generically, biological clocks are innate physiological processes [8] which fluctuate over time, in phase with the rhythmic properties of their environment (Figure 1b) [9,10]. They are found across functional scales from molecules to behaviour [11]. Curiously however, if the activity of a biological system was fully driven by changes in the environment, intrinsic rhythms would be unnecessary for the organism; rhythms observed in the absence of entrainment would be a non-parsimonious and energetically costly characteristic of a biological system. Yet, intrinsic rhythms subsist in the absence of entrainment (Figure 1b). In the mammalian brain, three oscillatory neural structures display autonomous rhythms: the retina, the suprachiasmatic nucleus and the olfactory bulb [9]. These structures are considered master clocks which can enslave neural oscillators in the cortex and cerebellum although spontaneous neuronal oscillations can readily be observed from single cells to neural networks [12,13,14,15–17]. Cyclic processes characterize life but what fundamentally distinguishes animals from other living matter is their capacity to generate timing in a manner that remains coherent with, yet partially independent from, the temporal properties of their environment. It is here argued that for time to become conscious — and not solely the chronoarchitecture onto which all cognitive operations are built — brain rhythms need to uncouple from external rhythms and actively generate timing. Perhaps an obvious illustration of uncoupling or temporal individuation is an animal’s capacity for locomotion that is the ability to change its space-time configuration with respect to its environment. While well-described neural central-pattern generators in basic vertebrates such as the lamprey generate rhythms that are nearly isomorphic to the animal’s undulations [18], as the complexity of microcircuitry increases, this one-to-one mapping between rhythm generators and expressed motion or behaviour disappears. Instead, intentional movements take over and temporally regulate information through active sensing www.sciencedirect.com
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Figure 1
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Current Opinion in Behavioral Sciences
Representing and quantifying time at different functional scales. (a) Let’s imagine a magnetoencephalography or electroencephalography (M/EEG) study in which brain responses to two experimental conditions were collected. M/EEG responses recorded in condition 2 (red trials) appear better aligned in time than those of condition 1 (black). Bottom left: each oscillatory response in a trial can be characterized by its amplitude and its period. Bottom right: oscillatory responses in one condition can be described as the distribution of phase responses across trials. In grey, a uniform distribution suggests no preferential phase in condition 1 although clear alpha bursts (8–12 Hz) are seen in the time domain (top graph, black traces). To the contrary, a preferential phase distribution is observed in the red condition consistent with the alignment seen in the time domain (bottom graph, red traces). (b) Circadian clocks and behaviour. Top panel: in this example, an animal shows four periods of activity in one day under natural day/night conditions. Bottom panel: when taken away from the circadian cycle, the animal’s behaviour keeps its rhythmic activity (frequency maintained) but slowly drifts away from the day/night cycle (phase change). (c). Frequency-tagging and entrainment of neuronal responses. Top panel: fast transient changes (e.g. a visual grating at 15 Hz) elicit a steady-state response in visual cortex. Similar steady-state responses can be obtained in auditory and tactile sensory modalities. Middle panel: neural entrainment can be observed at slower rhythms, for instance when a sound is presented every 500 ms or at 2 Hz. In both examples, the mean neural responses recorded with M/EEG is phase-locked to the presentation of the stimuli [(e.g. 44)]. Bottom panel: example of temporal individuation. Contrarily to the prediction that the phase of steadystate responses should remain constant throughout the stimulation period, the phase of entrained neural oscillations can shift in a manner consistent with participants’ conscious temporal order perception [43].
[19] thus establishing a closed-loop circuitry for information processing in cortex [20]. In the absence of movement, neural oscillations can thus be conceived as internalized time operators which modulate the information sampling in the environment thereby implementing attentional processes [19–22]. www.sciencedirect.com
With respect to temporal cognition specifically, a novel feature of neural oscillations is that they operate on selftimed inputs suggesting that closed-loop circuitries can build temporal individuation which includes an animal’s capacity to represent information. However, this individuation can only exist if brain rhythms can uncouple from Current Opinion in Behavioral Sciences 2016, 8:124–130
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the environmental rhythms so as to build a stable system for mental representations and insure invariant coordinates for the representation of the self in space-time. For instance, in his criticism of the ‘platonic’ or idealized threshold neuron, Llina´s suggested that spontaneous activity was crucially informative on the vectorial coordinate space in which sensorimotor transformations occurred [15], which is a fundamental architectural need for space-time representations supporting cognition [1,6]. More recently, algorithms for spatial navigation have been proposed to be recycled for mental functions, some of which fundamentally entail neural oscillations as a temporal metric [23]. In other words, in the absence of movement, neural oscillations may provide a mental metric for the representation of time [24–28]. If demonstrated, the uncoupling of brain rhythms from environmental rhythms are a crucial first step towards the possibility of autonomous, predictive and iterative processing of temporal content in the mind.
Neural oscillations and the individuation of ‘intelligent agents’ An important and influential observation in the field is that neural oscillators can be entrained by rhythmic sensory information that is closest to their resonant frequency (Figure 1c). Although neural oscillations are sometimes used generically to describe the rhythmic fluctuations of neural responses over time, it is important to emphasize that the level of observation defines the nature of neural oscillations being described — hence, their possible function and type of informational content being handled. These have been thoroughly reviewed elsewhere [14,29]: microscopic membrane fluctuations and intrinsic neural properties can lead to resonant properties of the cell; at the mesoscopic scale, neural oscillations reflect the mean activity of neural population; at the macroscopic scale, rhythmic fluctuations capture inter-areal synchronizations over time and space [29,30– 32]. In non-invasive human recordings (e.g. M/EEG), it is theoretically impossible to demonstrate that evoked activity results from phase-resetting of ongoing oscillatory activity [33] but reasonable inferences on the origins of the signal and the use of mathematically informed approaches can provide useful criteria for interpretation [34,35]. In this context, neural entrainment can shed light on the propensity of neurons and neural populations to ‘resonate’ with sensory stimuli, and address not only how external temporal structure can be internalized in brain responses but more importantly, how brain responses can dissociate from external temporal cues to represent an individual’s time experience. Entrained neural responses tend to be seen as passive phenomena in which brain activity reacts or calibrates itself to the temporal statistics of the environment. Numerous studies have demonstrated that neural entrainment — that is temporally calibrating the Current Opinion in Behavioral Sciences 2016, 8:124–130
brain — increases the processing efficiency within and across sensory modalities [(e.g. 21,36,37–40)]. Because low-frequency neural oscillations phasically define windows of opportunities for neural spiking [14,29], entraining neural oscillations is hypothesized to align the optimal phase of neural oscillations to the external stimulation, thereby optimizing the encoding of sensory information. One important working hypothesis is thus that entrainment regulates implicit timing so as to benefit the encoding of generic — visual, speech, multisensory — content [41,42]. However, this also begs the question of the relationship between implicit and conscious timing. For instance, Hermann and colleagues [38] suggested that neural entrainment provides a reference frame for veridical time representation and that phase shifts of the entrained neural oscillations capture subjective timing. Consistent with this idea, and contrary to the notion of passive entrainment in typical phase-locked-responses, recent findings suggest that the phase response of entrained neural oscillations can depart from stationarity thus maintaining an endogenous internal metric for the representation of (space)time [43]. Specifically, rhythmic presentation of desynchronized audiovisual stimuli elicited the expected peak oscillatory response in sensory cortices at the entrainment rate (Figure 1c). As M/EEG records the mean oscillatory response of entrained neural oscillators, entrained responses should phase-lock steadily to the rhythmic presentation of the stimuli [44]. However, a shift in the phase of the entrained auditory response was observed which linearly predicted participants’ sensitivity to audiovisual temporal order following the entrainment period. These findings suggest that the brain tends to stabilize its internal time metric in spite of external temporal evidence so as to maintain a unitary perception of the most probable (multisensory) event out there. A growing body of evidence is consistent with the suggested hypothesis that endogenous phase-resetting and shifting mechanisms play an important role for conscious timing. For instance, using a foreperiod paradigm in which the probability of a target occurrence was manipulated, Cravo and colleagues [45] reported higher phaselocking values of theta oscillations for U-shaped as compared to skewed distributions; U-shaped probability distributions also elicited significantly more cross-frequency-coupling between theta phase (7 Hz) and beta power (20–25 Hz) than skewed distributions. These results were demonstrated that endogenous phase-reset can be elicited by temporal anticipation. This finding is important because it demonstrates that the phase of neural oscillations can be under endogenous control on the basis of an internalized representation of the temporal statistics of events. Endogenous phase adjustments have also been reported when participants anticipated a distractor during a working memory task [46]. Consistent www.sciencedirect.com
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with this, a decrease of power in the endogenous alpha rhythm has been reported at the moment at which an event was expected to occur [47,48]. The decrease of alpha synchronization may mark the end of an arousal state or alternatively, reflect the phase-resetting of new windows of opportunity for sensory processing [47]. The latter hypothesis was consistent with the temporal prioritization role assigned to alpha oscillations [49]. Recently, Samaha and colleagues [50] also reported that temporal cueing biased the phase of ongoing alpha oscillations
towards an optimal phase for visual discrimination, consistent with the modulation of phase responses in sensory cortices by attention [39,51–54]. Formal approaches supporting the internal hierarchal temporal organization have also been described [55,56] to serve temporal expectation [55,57,58]. The endogenous control of brain rhythms has been further illustrated by the elegant study of Nozaradan and colleagues [59] who showed that mentally generating an auditory beat elicited an increased oscillatory peak response at that frequency;
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Current Opinion in Behavioral Sciences
Temporal multiplexing and representing time in the brain. (a) In the striatal-beat-frequency model [3], the phase-resetting of cortical oscillators marks the onset of an interval and the state of the oscillators at stimulus offset is read out through coincidence detection in the striatum. The resulting beat frequency is the outcome of the comparison between cortical oscillators with stored durations in long-term memory (LTM). The SBF has been suggested to count cycles through oscillatory multiplexing [27]. (b) If WM actively participates in quantifying time, discretized temporal units can be stored and counted [67]. This requires steady precision of oscillatory frequencies as well as arithmetic operations on time units to free memory capacity as time unfolds and the number of discretized units increases. For instance, if 1 time unit is 200 ms (or one cycle of a theta oscillation), no more than 100 4 to 7 time units can be stored at once in WM before repeating the operation again. The 4–7 time units are integrated by cycle which necessitates additional readouts. Temporal integration could thus be realized as magnitude estimation through discretized time cycles [64]. Crucially, the decision-boundary may be set by the phase of the neural oscillation so that one oscillatory cycle defines the prediction interval over which accumulation of evidence is realized. The temporal metric could naturally be a decisive bound on evidence accumulation mechanisms. As predicted by WM models and algorithms for space-time encoding [23], cross-frequency-coupling could naturally keep track of event order. www.sciencedirect.com
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this response was observed in addition to the peak responses of brain rhythms entrained by the acoustic inputs as measured by EEG. Altogether, these results suggest that neural oscillations not only play a mechanistic role in information processing but also that the phase of neural oscillations plays a tangible role in representing conscious timing. The evidence for endogenous control of oscillatory neural responses suggests a stable coordinate system for the representation of time which is necessary for high-level temporal cognition.
Time as content Cross-frequency coupling (sp. coupling between the phase of theta/alpha and the power of gamma) has been proposed as a mechanism for working memory (WM) [(e.g. 49,60)]. Supra-second interval timing has recently been suggested to instantiate one such implementation of WM [27] capitalizing on early models of oscillatory mechanisms for time encoding [26,61]. This hypothesis provides a useful and empirically testable framework although it raises many questions regarding the specific role of WM for timing (Figure 2). For instance, in light of the recently demonstrated capacity to maintain sequences of quantified time units in WM [62], one could ask whether similar mechanisms can at once encode time as a magnitude and flag events in time for ordered sequence mapping. One testable hypothesis is that oscillatory cycles naturally bound the accumulation of evidence process during magnitude estimation and, importantly, intrinsically define the prediction interval over which probabilistic mapping can be made. Evidence for cyclic processes serving evidence accumulation is building [63], and the implication that the time dimension would constrain evidence accumulation during magnitude estimation has also been suggested [64]. Mapping time and the phase sense also entail ordering events in a temporal metric. Seminal studies have suggested that the phase of alpha oscillations could serve as such metric for the conscious ordering of events [22,65]. The phase of entrained neural oscillations may be informative, by revealing the invariant properties of an individual’s chronoarchitecture, not only through the characteristic frequencies of neural oscillators, but also their characteristic phase [43]. Whether the phase of different neural oscillatory frequencies can provide the metric for time and whether different frequencies code for different temporal granularities that are consciously intelligible remains highly speculative. Interestingly, the algorithm used for the spatiotemporal mapping of distances physically traversed by an animal has been speculated to serve distance computations of various mental contents [23]. In this context, the recent discovery of context-invariant prospective speed cells [66] provide Current Opinion in Behavioral Sciences 2016, 8:124–130
an important building-block in understanding internal representations of time as mental distances.
Conclusions In the predictive brain, analyzing information consists in computing the residual error between an internal mental model and incoming sensorimotor information. The internal model is a system of representations against which non-invariant inputs can be compared and updated. If time is rendered intelligible through prediction, representing time is also quantifying the extent of the temporal perturbations to the system within the metric of that system. Neural oscillations appear a fit candidate to capture internal time metrics and build a temporal self.
Conflict of interest statement Nothing declared.
Acknowledgements This work was supported by an ERC-YStG-263584 and an ANR10JCJC1904 to V.vW. Some of these ideas were presented at the recent ‘Power Law and Multiple Scales in Neural Systems’ workshop organized by the EITN, Paris (March, 2015). I thank the overall NeuroSpin and UNCOG stimulating work environment as well as the intelligent agents populating my team at the moment for their inquisitiveness on the last version of this manuscript, among which, Denis Engemann, Baptiste Gauthier, Laetitia Grabot, Tadeusz Kononowicz, Benoıˆt Martin and Ignacio Polti. I am grateful to Prof. Richard Ivry for his insightful and helpful comments on previous versions of this manuscript.
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