Abstract:Time is an essential dimension of our environment that allows us to extract meaningful information about speed of movement, speech, motor actions and fine motor control. Traditionally, models of time have tried to quantify how the brain might process the duration of an event. The most commonly cited are the pacemaker-accumulator model and the beat frequency model of interval timing, which explain how duration is perceived, represented and encoded. Here we posit such models as providing a powerful tool for simu… Show more
“…This model is based on a multi-process theory that assumes three levels of timing processes common to animals and humans ( Fig. 1 ; modified from Hartcher-O'Brien et al, 2016 ). The first level consists of a pacemaker and accumulator.…”
Section: Introductionmentioning
confidence: 99%
“…The final level represents the decision mechanism. During this stage, the contents of the working memory module are compared to those of the reference memory of the pulse-duration on previous occasions, to which the subject responds accordingly (e.g., see Carroll et al, 2008 ; Hartcher-O'Brien et al, 2016 ). Fig.…”
Positive symptoms of schizophrenia may be related to distortions in time perception. To examine this issue, we conducted a meta-analysis to determine whether positive symptoms are associated with deficits in time processing performance. MEDLINE and Web of Science were searched from January 1980 through March 2017, and all related articles and their references were scrutinized to find relevant studies. Studies were selected if they included participants with a diagnosis of schizophrenia, and reported data from behavioral measures of interval timing (e.g. duration discrimination and temporal order judgement). The results indicated that positive symptoms of schizophrenia are related with overestimation of interval timing (i.e., acceleration of the “internal clock”), and suggest that time perception may be associated with psychosis.
“…This model is based on a multi-process theory that assumes three levels of timing processes common to animals and humans ( Fig. 1 ; modified from Hartcher-O'Brien et al, 2016 ). The first level consists of a pacemaker and accumulator.…”
Section: Introductionmentioning
confidence: 99%
“…The final level represents the decision mechanism. During this stage, the contents of the working memory module are compared to those of the reference memory of the pulse-duration on previous occasions, to which the subject responds accordingly (e.g., see Carroll et al, 2008 ; Hartcher-O'Brien et al, 2016 ). Fig.…”
Positive symptoms of schizophrenia may be related to distortions in time perception. To examine this issue, we conducted a meta-analysis to determine whether positive symptoms are associated with deficits in time processing performance. MEDLINE and Web of Science were searched from January 1980 through March 2017, and all related articles and their references were scrutinized to find relevant studies. Studies were selected if they included participants with a diagnosis of schizophrenia, and reported data from behavioral measures of interval timing (e.g. duration discrimination and temporal order judgement). The results indicated that positive symptoms of schizophrenia are related with overestimation of interval timing (i.e., acceleration of the “internal clock”), and suggest that time perception may be associated with psychosis.
“…Addyman et al (2017) have been able to categorize competing models as a function of their core timing process, e.g., the speeds and types of variability of their pacemaker-accumulator, multiple oscillators, memory decay, climbing activations, random process and contextual change-with numerous implementations. Integrative concepts like the ICAT model, in particular, are only beginning to be investigated and understood at the neurobiological level as well as in practical, everyday terms (van Rijn, 2014; see Hartcher-O'Brien et al, 2016).…”
The majority of studies in the field of timing and time perception have generally focused on sub-and supra-second time scales, specific behavioral processes, and/or discrete neuronal circuits. In an attempt to find common elements of interval timing from a broader perspective, we review the literature and highlight the need for cell and molecular studies that can delineate the neural mechanisms underlying temporal processing. Moreover, given the recent attention to the function of microtubule proteins and their potential contributions to learning and memory consolidation/re-consolidation, we propose that these proteins play key roles in coding temporal information in cerebellar Purkinje cells (PCs) and striatal medium spiny neurons (MSNs). The presence of microtubules at relevant neuronal sites, as well as their adaptability, dynamic structure, and longevity, makes them a suitable candidate for neural plasticity at both intra-and inter-cellular levels. As a consequence, microtubules appear capable of maintaining a temporal code or engram and thereby regulate the firing patterns of PCs and MSNs known to be involved in interval timing. This proposed mechanism would control the storage of temporal information triggered by postsynaptic activation of mGluR7. This, in turn, leads to alterations in microtubule dynamics through a "read-write" memory process involving alterations in microtubule dynamics and their hexagonal lattice structures involved in the molecular basis of temporal memory.
“…Relation to interval timing and other models for beat 793 production 794 Many interval timing models involve accumulation (continuous time or counting of 795 pacemaker cycles) with adjustment of threshold or ramp speed [6,7] to match the desired 796 time interval. Applications to periodic beat phenomena, say the metronome case, would 797 include instantaneous resetting and some form of phase adjustment/correction [56,57]. 798 Algorithmic models may not specifically identify the accumulator as such, but instead 799 refer to counters or elapsed time.…”
mentioning
confidence: 99%
“…All oscillators are reset at t = 0; 841 differences in frequencies of convergent units will eventually lead to collective 842 near-coincidence (so-called beating phenomenon of non-identical oscillators) at a time 843 that through learned choices (synapses onto coincidence detector units) can match the 844 desired interval. It may be extended to the periodic case and considered for beat 845 generation as discussed in [56,57] although the brain regions involved may be different 846 for explicit time estimation than for rhythmic prediction/reproduction [71,72].…”
When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise.
Author summaryMusic is integral to human experience and is appreciated across a wide range of cultures. 1 Although many features distinguish different musical traditions, rhythm is central to 2 nearly all. Most humans can detect and move along to the beat through finger or foot 3 tapping, hand clapping or other bodily movements. But many people have a hard time 4 "keeping a beat", or say they have "no sense of rhythm". There appears to be a 5 disconnect between our ability to perceive a beat versus our ability to produce a beat, 6 as a drummer would do as part of a musical group. Producing a beat requires beat 7 generation, the process by which we learn how to keep track of the specific time 8 intervals between beats, as well as executing the motor movement needed to produce 9 the sound associated with a beat. In this paper, we begin to explore neural mechanisms 10 that may be responsible for our ability to generate and keep a beat. We develop a 11 January 15, 20191/32 computational model that includes different neurons and shows how they cooperate to 12 learn a beat and keep it, even after the stimulus is removed, across a range of 13 frequencies relevant to music. Our dynamical systems model leads to predictions for 14 how the brain may react when learning a beat. Our findings and techniques should be 15 widely applicable to those interested in understanding how the brain processes time, 16 particularly in the context of m...
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