Neural Transmission Delays Research Articles

Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations.

Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the α frequency range (∼8-12 Hz). This suggests that α oscillations, a prominent feature of EEG recordings, may be a spectral "fingerprint" of predictive sensory processing. Here, we probed this possibility by investigating whether oscillations over the visual cortex predictively encode visual information. Specifically, we examined whether their power carries information about the position of a moving stimulus, in a temporally predictive fashion. In two experiments (N = 32, 18 female; N = 34, 17 female), participants viewed an apparent-motion stimulus moving along a circular path while EEG was recorded. To investigate the encoding of stimulus-position information, we developed a method of deriving probabilistic spatial maps from oscillatory power estimates. With this method, we demonstrate that it is possible to reconstruct the trajectory of a moving stimulus from α/low-β oscillations, tracking its position even across unexpected motion reversals. We also show that future position representations are activated in the absence of direct visual input, demonstrating that temporally predictive mechanisms manifest in α/β band oscillations. In a second experiment, we replicate these findings and show that the encoding of information in this range is not driven by visual entrainment. By demonstrating that occipital α/β oscillations carry stimulus-related information, in a temporally predictive fashion, we provide empirical evidence of these rhythms as a spectral "fingerprint" of hierarchical predictive processing in the human visual system.SIGNIFICANCE STATEMENT "Hierarchical predictive coding" is a general model of sensory information processing in the brain. When in silico predictive coding models are constrained by neural transmission delays, their activity naturally oscillates in roughly the α range (∼8-12 Hz). Using time-resolved EEG decoding, we show that neural rhythms in this approximate range (α/low-β) over the human visual cortex predictively encode the position of a moving stimulus. From the amplitude of these oscillations, we are able to reconstruct the stimulus' trajectory, revealing signatures of temporally predictive processing. This provides direct neural evidence linking occipital α/β rhythms to predictive visual processing, supporting the emerging view of such oscillations as a potential spectral "fingerprint" of hierarchical predictive processing in the human visual system.

Blurred Lines: Memory, Perceptions, and Consciousness: Commentary on "Consciousness as a Memory System" by Budson et al (2022).

In the previous issue, Budson, Richman, and Kensinger (2022) put forth the intriguing proposal that consciousness may have evolved from the episodic memory system. In addition to providing a possible evolutionary trajectory for consciousness, I believe that viewing consciousness as an extension of memory in this way is particularly useful for understanding some of the puzzling temporal complexities that are inherent to consciousness. For example, due to neural transmission delays, our conscious experience must necessarily lag the outside world, which creates a paradox for both conscious perception (Do we see the past, rather than the present?) and action (How can we make rapid decisions if it takes so long to become conscious of something?). These paradoxes can be elegantly solved by treating consciousness as a memory system. Finally, the proposal put forth by Budson and colleagues (2022) aligns with the emerging perspective that consciousness, like memory, represents a narrative time line of events rather than any single instant. However, I believe that this conceptualization can be further extended to include not only the past, but also the future. In this way, consciousness can be provocatively viewed as the remembered past, present, and future.

Facilitation and inhibition effects of anodal and cathodal tDCS over areas MT+ on the flash-lag effect.

The perceived position of a moving object in vision entails an accumulation of neural signals over space and time. Due to neural signal transmission delays, the visual system cannot acquire immediate information about the moving object's position. Although physiological and psychophysical studies on the flash-lag effect (FLE), a moving object is perceived ahead of a flash even when they are aligned at the same location, have shown that the visual system develops the mechanisms of predicting the object's location to compensate for the neural delays, the neural mechanisms of motion-induced location prediction are not still understood well. Here, we investigated the role of neural activity changes in areas MT+ (specialized for motion processing) and the potential contralateral processing preference of MT+ in modulating the FLE. Using transcranial direct current stimulations (tDCS) over the left and right MT+ between pre- and posttests of the FLE in different motion directions, we measured the effects of tDCS on the FLE. The results found that anodal and cathodal tDCS enhanced and reduced the FLE with the moving object heading to but not deviating from the side of the brain stimulated, respectively, compared with sham tDCS. These findings suggest a causal role of area MT+ in motion-induced location prediction, which may involve the integration of position information.NEW & NOTEWORTHY Perceived positions of moving objects are related to neural activities in areas MT+. We demonstrate that tDCS over areas MT+ can modulate the FLE, and further anodal and cathodal tDCS facilitated and inhibited the FLE with a moving object heading to but not deviating from the side of the brain stimulated, respectively. These findings suggest a causal role of area MT+ in motion-induced location prediction and contribute to understanding the neural mechanism of the FLE.

Perception in real-time: predicting the present, reconstructing the past.

We feel that we perceive events in the environment as they unfold in real-time. However, this intuitive view of perception is impossible to implement in the nervous system due to biological constraints such as neural transmission delays. I propose a new way of thinking about real-time perception: at any given moment, instead of representing a single timepoint, perceptual mechanisms represent an entire timeline. On this timeline, predictive mechanisms predict ahead to compensate for delays in incoming sensory input, and reconstruction mechanisms retroactively revise perception when those predictions do not come true. This proposal integrates and extends previous work to address a crucial gap in our understanding of a fundamental aspect of our everyday life: the experience of perceiving the present.

Time for Space and the Stability of Prospective Control: Reaching-to-Grasp Gibson.

Gibson formulated an approach to goal-directed behavior using prospective information in the context of visually guided locomotion and manual behavior. The former was Gibson's paradigm case, but it is the rapidity of targeted reaching that has provided the special challenge for stable control. Recent treatments of visually guided reaching assume that internal forward models are required to generate stable behavior given delays caused by neural transmission times. Internal models are representations of the sort eschewed by Gibson in favor of prospective information. Reaching is usually described as guided using relative distances of hand and target, but prospective information is usually temporal rather than spatial. We describe proportional rate control models that incorporate time dimensioned prospective information and show they remain stable in the face of delays. The use of time-dimensioned prospective information removes the need for internal models for stable behavior despite neural transmission delays and allows Gibson's approach to prevail.

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity.

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing-dependent plasticity (STDP). We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Because of the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalization that arises when human observers are required to localize a moving object relative to a flashed static object (the flash-lag effect; FLE).SIGNIFICANCE STATEMENT Our ability to track and respond to rapidly changing visual stimuli, such as a fast-moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object to predict its current position, despite the delays that result from neural transmission. Here, we show how the neural circuits underlying this ability can be learned through spike-timing-dependent synaptic plasticity and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

Nonlinear robust compensator design for postural control of a biomechanical model with delayed feedback

Postural stability and balance regulation is an intricate neurophysiological task which entails coordination of movements for successful execution. This task is proficiently regulated by central nervous system. The sensory feedback through muscles via proprioceptors has neural transmission delays which make the movement coordination and computations by central nervous system a complex problem to deal with. This paper addresses a nonlinear robust technique based on feedback linearization for postural stabilization of a single-link biomechanical model in the presence of physiological latencies. We included neural transmission delays in sensory feedback from proprioceptors. We developed [Formula: see text] optimal controller and integrated it with feedback linearization to calculate the joint torque for the biomechanical task. This modeling scheme is simulated in MATLAB/SimMechanics, and the simulation results for the nonlinear biomechanical model are developed. The joint torque compensates for the delays and settles the motion profiles within anatomical constraints. The position profile shows a bit higher overshoot (0.02, 0.03 rad) in case of delays; however, the settling time is same for the profiles with and without delay. The extensor torque is same for all profiles; however, the flexion torque increases for the delayed case. The simulation results show the applicability of this scheme for further analysis of the biomechanical task.

Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis.

Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion-position illusions in human observers, and that it provides a solution to the temporal binding problem.

Motion Extrapolation for Eye Movements Predicts Perceived Motion-Induced Position Shifts.

Transmission delays in the nervous system pose challenges for the accurate localization of moving objects as the brain must rely on outdated information to determine their position in space. Acting effectively in the present requires that the brain compensates not only for the time lost in the transmission and processing of sensory information, but also for the expected time that will be spent preparing and executing motor programs. Failure to account for these delays will result in the mislocalization and mistargeting of moving objects. In the visuomotor system, where sensory and motor processes are tightly coupled, this predicts that the perceived position of an object should be related to the latency of saccadic eye movements aimed at it. Here we use the flash-grab effect, a mislocalization of briefly flashed stimuli in the direction of a reversing moving background, to induce shifts of perceived visual position in human observers (male and female). We find a linear relationship between saccade latency and perceived position shift, challenging the classic dissociation between "vision for action" and "vision for perception" for tasks of this kind and showing that oculomotor position representations are either shared with or tightly coupled to perceptual position representations. Altogether, we show that the visual system uses both the spatial and temporal characteristics of an upcoming saccade to localize visual objects for both action and perception.SIGNIFICANCE STATEMENT Accurately localizing moving objects is a computational challenge for the brain due to the inevitable delays that result from neural transmission. To solve this, the brain might implement motion extrapolation, predicting where an object ought to be at the present moment. Here, we use the flash-grab effect to induce perceptual position shifts and show that the latency of imminent saccades predicts the perceived position of the objects they target. This counterintuitive finding is important because it not only shows that motion extrapolation mechanisms indeed work to reduce the behavioral impact of neural transmission delays in the human brain, but also that these mechanisms are closely matched in the perceptual and oculomotor systems.

Neural Control and Precision of Spike Phasing in Flight Muscles

Rhythmic locomotor behavior in animals requires exact timing of muscle activation within the locomotor cycle. Neural strategies for timing control that employ higher brain function, however, suffer from synaptic and neural transmission delays, making them inefficient for control of fast-frequent locomotor systems. Evolutionary pressure on muscle timing control is particularly pronounced in flying insects with wing flapping periods of few milliseconds. In these animals, sensory integration is often achieved at the level of the peripheral nervous system, circumventing the central brain and controlling spike activation phases with little delay, rather than muscle spike frequency. This review is engaged in the precision with which flies adjust power output of their flight muscles and highlights the significance of visual and proprioceptive feedback loops for muscle spike control. Recent results suggest that in flies peripheral feedback loops are keys enabling precise heading control and body stability in flight, and potentially similar to the function of local circuits for locomotor control found in the spinal chord of vertebrates.

Contribution of muscle short-range stiffness to initial changes in joint kinetics and kinematics during perturbations to standing balance: A simulation study

Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.

Independence of Movement Preparation and Movement Initiation.

Initiating a movement in response to a visual stimulus takes significantly longer than might be expected on the basis of neural transmission delays, but it is unclear why. In a visually guided reaching task, we forced human participants to move at lower-than-normal reaction times to test whether normal reaction times are strictly necessary for accurate movement. We found that participants were, in fact, capable of moving accurately ∼80 ms earlier than their reaction times would suggest. Reaction times thus include a seemingly unnecessary delay that accounts for approximately one-third of their duration. Close examination of participants' behavior in conventional reaction-time conditions revealed that they generated occasional, spontaneous errors in trials in which their reaction time was unusually short. The pattern of these errors could be well accounted for by a simple model in which the timing of movement initiation is independent of the timing of movement preparation. This independence provides an explanation for why reaction times are usually so sluggish: delaying the mean time of movement initiation relative to preparation reduces the risk that a movement will be initiated before it has been appropriately prepared. Our results suggest that preparation and initiation of movement are mechanistically independent and may have a distinct neural basis. The results also demonstrate that, even in strongly stimulus-driven tasks, presentation of a stimulus does not directly trigger a movement. Rather, the stimulus appears to trigger an internal decision whether to make a movement, reflecting a volitional rather than reactive mode of control.

Transitioning from Saccade to Smooth Pursuit Eye Movements using Linear Quadratic Tracking Control

An optimal linear quadratic tracking controller (LQTC) which emulates smooth pursuit eye movements has been shown to accurately track various motion profiles in the presence of neural transmission delays. However, the initial orientation of the eye relative to the location of where a target first becomes salient affects the time course in which the desired trajectory is fully acquired. It is shown that a control strategy that incorporates a latent saccade to rapidly reposition the gaze to a predicted point on the trajectory path, then transitions the engagement smoothly using the LQTC, produces eye movements consistent with experimental observations.

Linear Quadratic Tracking Control of Smooth Pursuit Eye Movements

Conventional feedback control models of the oculomotor system fail to account for the destabilizing effects of neural transmission delays. To address this shortcoming, a linear quadratic tracking algorithm used to control smoothly pursuing eye movements of various target trajectories is presented. Based on the type of input to the system, it is shown that stability, in the presence of large motor feedback delays, can be maintained by modulating weighting factors intrinsic to the model. Conditions, such as the initial orientation of the eye relative to the location of where a target first becomes salient and the possible oscillatory nature that the reference trajectory may present, play important roles in determining the optimal cost to go motor control strategy at the onset of a tracking movement.

A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion

It is often assumed that central pattern generators, which generate rhythmic patterns without rhythmic inputs, play a key role in the spinal control of human locomotion. We propose a neural control model in which the spinal control generates muscle stimulations mainly through integrated reflex pathways with no central pattern generator. Using a physics-based neuromuscular human model, we show that this control network is sufficient to compose steady and transitional 3-D locomotion behaviours, including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans. Neural networks along the spinal cord contribute substantially to generating locomotion behaviours in humans and other legged animals. However, the neural circuitry involved in this spinal control remains unclear. We here propose a specific circuitry that emphasizes feedback integration over central pattern generation. The circuitry is based on neurophysiologically plausible muscle-reflex pathways that are organized in 10 spinal modules realizing limb functions essential to legged systems in stance and swing. These modules are combined with a supraspinal control layer that adjusts the desired foot placements and selects the leg that is to transition into swing control during double support. Using physics-based simulation, we test the proposed circuitry in a neuromuscular human model that includes neural transmission delays, musculotendon dynamics and compliant foot-ground contacts. We find that the control network is sufficient to compose steady and transitional 3-D locomotion behaviours including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans.

Encoding of event timing in the phase of neural oscillations

Time perception is a critical component of conscious experience. To be in synchrony with the environment, the brain must deal not only with differences in the speed of light and sound but also with its computational and neural transmission delays. Here, we asked whether the brain could actively compensate for temporal delays by changing its processing time. Specifically, can changes in neural timing or in the phase of neural oscillation index perceived timing? For this, a lag-adaptation paradigm was used to manipulate participants' perceived audiovisual (AV) simultaneity of events while they were recorded with magnetoencephalography (MEG). Desynchronized AV stimuli were presented rhythmically to elicit a robust 1Hz frequency-tagging of auditory and visual cortical responses. As participants' perception of AV simultaneity shifted, systematic changes in the phase of entrained neural oscillations were observed. This suggests that neural entrainment is not a passive response and that the entrained neural oscillation shifts in time. Crucially, our results indicate that shifts in neural timing in auditory cortices linearly map participants' perceived AV simultaneity. To our knowledge, these results provide the first mechanistic evidence for active neural compensation in the encoding of sensory event timing in support of the emergence of time awareness.

A computational neuromusculoskeletal model of human arm movements

This work proposes a computational neuromusculoskeletal model of human arm movements. The model consists of three components: the supraspinal neural control system, the spinal motor system, and the muscle-tendon actuation system. In the supraspinal neural system model, the cerebellum is regarded as having feedforward control and the cerebrum as feedback control principally based on the feedback-error learning scheme. This computational model proposes that the feedforward control of the cerebellum may not need to be an explicit locus of an inverse dynamic model. This model also includes the modularly organized spinal motor system such that it simplifies controlling redundant muscular actuators. Cerebellar feedforward control and the spinal motor system are assumed to be adaptive. The two motor adaptations seem to synergistically promote motion flexibility and simplify the neural system structure. The neural control system is combined with the Hill-type muscle-tendon model to generate arm movement. The overall model proposes that an approximate inverse dynamic model may implicitly be constructed over the integrated neuromusculoskeletal system, and it is not necessary to be explicitly computed in a specific motor system. To cope with the human neural system, neuromuscular activation dynamics and neural transmission delays are included in the model. A computational simulation study using the model was implemented to verify the feasibility of the model. Center out reaching movements and learning of those movements as well s generations of figure eightlike movements were computationally tested. A plausible motor control scheme of movement is discussed using the model.

338 神経伝達遅れを考慮したCPGによる歩行動作の生成(OS4-01 : バイオロボティクス,バイオロボティクス)

338 神経伝達遅れを考慮したCPGによる歩行動作の生成(OS4-01 : バイオロボティクス,バイオロボティクス)

3927 3次元歩行シミュレータにおける神経伝達遅れの補償(J16 ヒューマンダイナミクス,2005年度年次大会)

3927 3次元歩行シミュレータにおける神経伝達遅れの補償(J16 ヒューマンダイナミクス,2005年度年次大会)

Compensation of neural delays in visual‐motor behaviour: No evidence for shorter afferent delays for visual motion

There are significant neural transmission and processing delays within the nervous system. How then are behaviours observed both in nature and high‐speed ball games, which require temporal accuracy to within several milliseconds, possible? Until recently, most investigators attributed the observed success in interceptive behaviours to learning or prediction built into motor programs that take these delays into account. Several investigators have proposed, however, that sensory mechanisms also contribute to compensation. Sensory compensation is particularly important when moving objects are the targets of interceptive behaviour, as in this case neural delays in the visual system could lead to errors in the communication of crucial position information of the object of interest. However, given the biological significance of visual motion the visual system could have evolved neural pathways optimized for the rapid transmission of motion signals. Alternatively, the visual system could take a sample of visual motion and compensate for the delays through prediction based on the sample. This mechanism is the visual analogue of the previously proposed “internal forward model” for motor control. We conducted four experiments using the flash‐lag effect to ask if the nervous system is naturally geared to processing moving items with a speed greater than stationary flashes. Our results show that the nervous system does not process moving items more quickly than stationary flashes.