Increase In Interval Duration Research Articles

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human.

The human and dog have sinus arrhythmia; however, the beat-to-beat interval changes were hypothesized to be different. Geometric analyses (R–R interval tachograms, dynamic Poincaré plots) to examine rate changes on a beat-to-beat basis were analyzed along with time and frequency domain heart rate variability from 40 human and 130 canine 24-h electrocardiographic recordings. Humans had bell-shaped beat-interval distributions, narrow interval bands across time with continuous interval change and linear changes in rate. In contrast, dogs had skewed non-singular beat distributions, wide interval bands {despite faster average heart rate of dogs [mean (range); 81 (64–119)] bpm compared to humans [74.5 (59–103) p = 0.005]} with regions displaying a paucity of intervals (zone of avoidance) and linear plus non-linear rate changes. In the dog, dynamic Poincaré plots showed linear rate changes as intervals prolonged until a point of divergence from the line of identity at a mean interval of 598.5 (95% CI: 583.5–613.5) ms (bifurcation interval). The dog had bimodal beat distribution during sleep with slower rates and greater variability than during active hours that showed singular interval distributions, higher rates and less variability. During sleep, Poincaré plots of the dog had clustered or branched patterns of intervals. A slower rate supported greater parasympathetic modulation with a branched compared to the clustered distribution. Treatment with atropine eliminated the non-linear patterns, while hydromorphone shifted the bifurcated branching and beat clustering to longer intervals. These results demonstrate the unique non-linear nature of beat-to-beat variability in the dog compared to humans with increases in interval duration (decrease heart rate). These results provoke the possibility that changes are linear with a dominant sympathetic modulation and non-linear with a dominant parasympathetic modulation. The abrupt bifurcation, zone of avoidance and beat-to-beat patterning are concordant with other studies demonstrating the development of exit block from the sinus node with parasympathetic modulation influencing not only the oscillation of the pacing cells, but conduction to the atria. Studies are required to associate the in vivo sinus node beat patterns identified in this study to the mapping of sinus impulse origin and exit from the sinus node.

Timing in talking: what is it used for, and how is it controlled?

In the first part of the paper, we summarize the linguistic factors that shape speech timing patterns, including the prosodic structures which govern them, and suggest that speech timing patterns are used to aid utterance recognition. In the spirit of optimal control theory, we propose that recognition requirements are balanced against requirements such as rate of speech and style, as well as movement costs, to yield (near-)optimal planned surface timing patterns; additional factors may influence the implementation of that plan. In the second part of the paper, we discuss theories of timing control in models of speech production and motor control. We present three types of evidence that support models of speech production that involve extrinsic timing. These include (i) increasing variability with increases in interval duration, (ii) evidence that speakers refer to and plan surface durations, and (iii) independent timing of movement onsets and offsets.

Reservoir of neurons with adaptive time constants: a hybrid model for robust motor-sensory temporal processing

The ability to precisely quantify time on the scale ofhundreds of milliseconds is critical towards the proces-sing of complex sensory and motor patterns. However,the natures of neural mechanisms for temporal proces-sing (at this scale) in the brain are mostly unknown.Based on experimental data (psychophysics, cell cul-tures, electrophysiology) and theoretical studies, it is lar-gely debated whetherdedicated circuits or intrinsicmechanisms of neural circuits underlie the timing pro-cess [1]. One specific type of timing model, namelystate-dependent networks (SDN) [2], shows that time isencoded in the temporal patterns of activity of neuralpopulations and emerges from the internal dynamics ofrecurrent networks. This can be achieved without theneed of dedicated timing units. However, such intrinsicmodels in their present form have difficulty accountingfor crossmodal transfer [1]. In contrast, recent experi-mental evidence indicates that medial premotor corticalneurons of behaving monkeys show specific intervaltuning across modalities (auditory and visual) [3]. In thiswork we propose a hybrid model, making the hypothesisthat dedicated interval tuning mechanisms of individualneurons augment the intrinsic dynamics of large recur-rent networks (dynamic reservoir). Using a networkmodel of rate-coded neurons starting with random initi-alization of synaptic connections, we propose a learningrule based on local active information storage (LAIS) [4]to adapt neuronal time constants with respect to theinput stimuli to the network. Measured at each spatio-temporal location of the reservoir, LAIS gives a prob-abilistic measure of the amount of information in theprevious state of the neuron that is relevant in predict-ing the next state. Interestingly high LAIS regions in thenetwork correlate to significant events in time (intervals)of the driving stimulus. Furthermore, we combine thiswith mutual information driven intrinsic plasticityscheme in order to stabilize chaotic activity in the net-work. Incoming input drives the network which, in turn,is connected to readout neurons (Figure 1.A) that dis-play the learned behavior for temporally dependent sen-sory motor tasks. Reservoir-to-output connections canbe adapted using both supervised and reward modulatedlearning rules. Using single and multiple interval discri-mination tasks, we show that our network reproduces(across modalities) a linear increase in temporal variabil-ity with increase in interval duration. This correlation isalso observed in experimental data [3]. Furthermore wedemonstrate that our dedicated timing mechanism com-plements the inherent transient dynamics of the net-work by successfully learning complex time dependentmotor behaviors; like handwriting generation (Figure 1.Band C), locomotion pattern transformation and temporalmemory tasks. In essence, our hybrid model demon-stratesthattimecanbeencodedbyacombinationofdedicated and intrinsic mechanisms with the possibilityto ‘learn’ the temporal structure of incoming stimuli [5].