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@guOscillatoryMultiplexingNeural2015a

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Oscillatory multiplexing of neural population codes for interval timing and working memory

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Abstract

Interval timing and working memory are critical components of cognition that are supported by neural oscillations in prefrontal-striatal-hippocampal circuits. In this review, the properties of interval timing and working memory are explored in terms of behavioral, anatomical, pharmacological, and neurophysiological findings. We then describe the various neurobiological theories that have been developed to explain these cognitive processes - largely independent of each other. Following this, a coupled excitatory - inhibitory oscillation (EIO) model of temporal processing is proposed to address the shared oscillatory properties of interval timing and working memory. Using this integrative approach, we describe a hybrid model explaining how interval timing and working memory can originate from the same oscillatory processes, but differ in terms of which dimension of the neural oscillation is utilized for the extraction of item, temporal order, and duration information. This extension of the striatal beat-frequency (SBF) model of interval timing (Matell and Meck, 2000, 2004) is based on prefrontal-striatal-hippocampal circuit dynamics and has direct relevance to the pathophysiological distortions observed in time perception and working memory in a variety of psychiatric and neurological conditions.

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Phase Amplitude Coupling

In addition to the phase locking of spikes to theta oscillations, studies utilizing LFPs or EEG/MEG have reported that phaseamplitude coupling (PAC) between theta-gamma oscillations is involved in working memory (e.g., Axmacher et al., 2010; Jensen and Colgin, 2007; Lisman and Idiart, 1995; Maris et al., 2011; Mizuhara and Yamaguchi, 2011; Sauseng et al., 2009; Schack et al., 2002; van der Meij et al., 2012). PAC, also referred to as “nested oscillations”, refers to the phenomenon of coupling between the amplitude of a faster oscillation and the phase of a slower oscillation. Numerous studies have shown evidence of PAC in various species including humans, monkeys, and rats (e.g., Canolty et al., 2006; Chrobak and Buzsáki, 1998; Lakatos et al., 2005, 2008; Sirota et al., 2008; Tort et al., 2009; van der Meij et al., 2012). PAC has been most commonly observed between theta and gamma oscillations; however, recent evidence shows that PAC can occur at multiple frequencies with theta oscillations also being entrained to delta oscillations (e.g., He et al., 2010; Lakatos et al., 2005; Maris et al., 2011; Miller et al., 2010).

The functional role of PAC is thought to involve local neural computations and their communication across large-scale brain networks (Canolty and Knight, 2010). With the extensive evidence of PACs occurring over various frequencies and brain regions, PAC has been shown to be involved in multiple cognitive domains, important for working memory (e.g., Axmacher et al., 2010; Lisman and Idiart, 1995; Maris et al., 2011; Mizuhara and Yamaguchi, 2011; Schack et al., 2002; van der Meij et al., 2012), and also for decision-making, spatial navigation, learning, and perception (Kepecs et al., 2006; Lakatos et al., 2005). Canolty et al. (2006) examined the PAC patterns in different cognitive tasks by recording the electrocorticogram (ECoG) of epilepsy patients, showing that similar tasks evoked similar spatial patterns of theta and gamma PAC—suggesting that “cognitive architectures” can be described by these patterns of oscillatory activity.

In working memory tasks, the amplitude of gamma is modulated by theta phase in multiple brain areas, while PAC strength is modulated in a heterogeneous manner. Maris et al. (2011), for example, showed that working memory tasks are associated with increases in PAC strength in the parietal-temporal and right frontal areas, with an associated decrease in strength in right temporal lobe in humans. Moreover, when the amplitude- and phase-providing oscillations are spatially dissociated using decomposition techniques, the PAC was shown to be widely distributed across the brain, with the spatial extent of the phase-providing (slower) oscillations tending to be more widespread than the amplitude providing (faster) oscillations (Maris et al., 2011; van der Meij et al., 2012). The widely distributed PAC networks demonstrated in these human studies, together with the evidence of cross-area couplings from rodents and primates (Liebe et al., 2012; Sirota et al., 2008; Tort et al., 2009), support the functional role of PAC in facilitating communication among brain areas by modulating the synchronous patterns of neural firing.

More in the paper but seems heavily focused on evidence of observed rythms

Transclude of State-Dependent-Network-(SDN)-model#state-dependent-network-sdn-model

stochastic ramp and trigger (SRT) model

The stochastic ramp and trigger (SRT) model, which is an integration-based model, proposes that time is encoded as an average firing rate of neural populations and detected with a fixed threshold (Simen et al., 2011);

Dual Oscillator Interference Model

Sec. 6.4

A dual-oscillator interference model has been proposed to explain the phase precession of place-cell firing to theta oscillations (Burgess and O’Keefe, 2011; Burgess et al., 2007; O’Keefe and Burgess, 2005; O’Keefe and Recce, 1993).

Not entire certain how this is different from the EIO model, but the latter is based on the former.

  • maybe because it’s for place cells?

the firing of place cells shows theta-phase precession during spatial navigation (Fig. 4A). For example, as a subject reaches its target area, the place-cell firing occurs at earlier phases of the theta oscillation of the LFP so that the firing phase of the cell reflects the relative distance that the subject has traveled through the cell’s place field (e.g., firing at late phases of theta indicates that the subject has just entered the place field—Burgess et al., 1994; Skaggs et al., 1996).

In order to explain the features of place cells, the dual oscillator interference model hypothesized that the firing of place cells occurs at a slightly higher frequency than the LFP theta oscillation so that the firing precesses to the earlier phases of the LFP theta (Burgess et al., 2007; O’Keefe and Burgess, 2005).

For example, if a neuron receives an oscillatory somatic input at 10 Hz – which is the LFP oscillation frequency – and an oscillatory dendritic input at 11.5 Hz, the sum of the two oscillators will show that a high-frequency oscillation (e.g., 10.75 Hz) is entrained within a low frequency oscillation (e.g., 0.75 Hz), which appears similar to the PAC phenomenon described above (Fig. 4B).

At which point, the firing of the place cell occurs at the peak of the dual oscillator sum (oscillation of 10.75 Hz) so that the firing will show a phase precession compared to the somatic oscillatory input (LFP oscillation of 10 Hz).

In relation to this hypothesis, recent intra-cellular recordings from a virtual spatial navigation task with mice showed that the membrane potential oscillation (MPO), which may correspond to the dual oscillator sum, occurred at a higher theta frequency compared to the LFP theta oscillation, and the cell fired at the peaks of the MPO which resulted in the phase precession of spikes to the LFP theta oscillation (Harvey et al., 2009).

The basic concept of our coupled EIO model is similar to the original dual oscillator model (e.g., Burgess et al., 2007; O’Keefe and Burgess, 2005) although one of the oscillators, the baseline somatic oscillation at the frequency of LFP, is substituted in the EIO model with the inhibitory oscillation which represents population feedback. The two models do not necessarily exclude each other, however, the EIO model can explain the PAC phenomenon at a neural population level and can provide a better foundation for oscillatory models of interval timing and working memory.

integrated timing model of Taatgen et al. (2007)

In this model, timing is not considered in isolation, but a temporal module inspired by Scalar Timing Theory is embedded within a cognitive architecture that provides further constraints on other cognitive faculties such as action-selection, memory retrieval, and interactions with the outside world. Using this integrated timing model, all aspects of a (behavioral) task can be modeled, from perception of a stimulus of which the display duration needs to be estimated, to the key presses required for the reproduction.

In a series of papers, van Rijn and Taatgen (2008) have shown that this model is capable of explaining many different interval timing phenomena, from parallel estimation of multiple intervals to including context- or memory-mixing effects (Gu and Meck, 2011; Taatgen and van Rijn, 2011), but also that this integrated timing model can be used to explain the role of temporal information in higher level cognitive tasks such as driving a car or multitasking in a video game (e.g., Moon and Anderson, 2013; van Rijn, 2014).

The existing temporal system in the integrated timing model closely follows the outline of the Scalar Timing Theory, in that a separate start signal is needed to start the clock after which at any point the current state of the clock can be tested. Comparing the state of the clock with a previously experienced clock-state, and executing a key press as soon as these states are similar enough, straightforwardly implements a temporal reproduction task. This comparison process does not need to be an active process, as the striatal production rules (Stocco et al., 2010) in the decision making system are dormant until their conditions are matched, similar in functioning as the medium spiny neurons discussed earlier.

This detection process is functionally identical to the firing of a MSN representing a particular duration, in both cases is the information that a specific interval is perceived made available to the other cognitive processes.

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