Noise in the nervous system

Enhancement of ITD Coding Within the Initial Stages of the Auditory Pathway

Sensory systems use a variety of strategies to increase the signal-to-noise ratio in their inputs at the receptor level. However, important cues for sound localization are not present at the individual ears but are computed after inputs from the two ears converge within the brain, and we hypothesized that additional strategies to enhance the representation of these cues might be employed in the initial stages after binaural convergence. Specifically, we investigated the transformation that takes place between the first two stages of the gerbil auditory pathway that are sensitive to differences in the arrival time of a sound at the two ears (interaural time differences; ITDs): the medial superior olive (MSO), where ITD tuning originates, and the dorsal nucleus of the lateral lemniscus (DNLL), to which the MSO sends direct projections. We use a combined experimental and computational approach to demonstrate that the coding of ITDs is dramatically enhanced between these two stages, with the mutual information in the responses of single neurons increasing by a factor of 2. We also show that this enhancement is related to an increase in dynamic range for neurons with high preferred frequencies and a decrease in variability for neurons with low preferred frequencies. These results suggest that a major role of the initial stages of the ITD pathway may be to enhance the representation created at the site of coincidence detection and illustrate the potential of this pathway as a model system for the study of strategies for enhancing sensory representations in the mammalian brain.

Comparison of Langevin and Markov channel noise models for neuronal signal generation

The stochastic opening and closing of voltage-gated ion channels produce noise in neurons. The effect of this noise on the neuronal performance has been modeled using either an approximate or Langevin model based on stochastic differential equations or an exact model based on a Markov process model of channel gating. Yet whether the Langevin model accurately reproduces the channel noise produced by the Markov model remains unclear. Here we present a comparison between Langevin and Markov models of channel noise in neurons using single compartment Hodgkin-Huxley models containing either Na+ and K+, or only K+ voltage-gated ion channels. The performance of the Langevin and Markov models was quantified over a range of stimulus statistics, membrane areas, and channel numbers. We find that in comparison to the Markov model, the Langevin model underestimates the noise contributed by voltage-gated ion channels, overestimating information rates for both spiking and nonspiking membranes. Even with increasing numbers of channels, the difference between the two models persists. This suggests that the Langevin model may not be suitable for accurately simulating channel noise in neurons, even in simulations with large numbers of ion channels.

Biological proton pumping in an oscillating electric field

Time-dependent external perturbations provide powerful probes of the function of molecular machines. Here we study biological proton pumping in an oscillating electric field. The protein cytochrome c oxidase is the main energy transducer in aerobic life, converting chemical energy into an electric potential by pumping protons across a membrane. With the help of master-equation descriptions that recover the key thermodynamic and kinetic properties of this biological "fuel cell," we show that the proton pumping efficiency and the electronic currents in steady state depend significantly on the frequency and amplitude of the applied field, allowing us to distinguish between different microscopic mechanisms of the machine. A spectral analysis reveals dominant reaction steps consistent with an electron-gated pumping mechanism.

Interpersonal pattern dynamics and adaptive behavior in multiagent neurobiological systems: conceptual model and data

Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker-defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.

Timing variability and not force variability predicts the endpoint accuracy of fast and slow isometric contractions

The purpose of the study was to determine the contributions of endpoint variance and trajectory variability to the endpoint accuracy of goal-directed isometric contractions when the target force and contraction speed were varied. Thirteen young adults (25 +/- 6 years) performed blocks of 15 trials at each of 2 contraction speeds and 4 target forces. Subjects were instructed to match the peak of a parabolic force trajectory to a target force by controlling the abduction force exerted by the index finger. The time to peak force was either 150 ms (fast) or 1 s (slow). The target forces were 20, 40, 60, and 80% of the maximal force that could be achieved in 150 ms during an MVC. The same absolute forces were required for both contraction speeds. Endpoint accuracy and variability in force and time along with intramuscular EMG activity of the agonist (first dorsal interosseus) and antagonist (second palmar interosseus) muscles were quantified for each block of trials. The principal dependent variables were endpoint error (shortest distance between the coordinates of the target and the peak force), endpoint variance (sum of the variance in peak force and time to peak force), trial-to-trial variability (SD of peak force and time to peak force), SD of the force trajectory (SD of the detrended force from force onset to peak force), normalized peak EMG amplitude, and the SD of normalized peak EMG amplitude. Stepwise multiple linear regression models were used to determine the EMG activity parameters that could explain the differences observed in endpoint error and endpoint variance. Endpoint error increased with target force for the fast contractions, but not for the slow contractions. In contrast, endpoint variance was greatest at the lowest force and was not associated with endpoint error at either contraction speed. Furthermore, force trajectory SD was not associated with endpoint error or endpoint variance for either contraction speed. Only the trial-to-trial variability of the timing predicted endpoint accuracy for fast and slow contractions. These findings indicate that endpoint error in tasks that require force and timing accuracy is minimized by controlling timing variability but not force variability, and that endpoint error is not related to the amplitude of the activation signal.

Trial-to-trial variability of single cells in motor cortices is dynamically modified during visuomotor adaptation

Neurons in all brain areas exhibit variability in their spiking activity. Although part of this variability can be considered as noise that is detrimental to information processing, recent findings indicate that variability can also be beneficial. In particular, it was suggested that variability in the motor system allows for exploration of possible motor states and therefore can facilitate learning and adaptation to new environments. Here, we provide evidence to support this idea by analyzing the variability of neurons in the primary motor cortex (M1) and in the supplementary motor area (SMA-proper) of monkeys adapting to new rotational visuomotor tasks. We found that trial-to-trial variability increased during learning and exhibited four main characteristics: (1) modulation occurred preferentially during a delay period when the target of movement was already known, but before movement onset; (2) variability returned to its initial levels toward the end of learning; (3) the increase in variability was more apparent in cells with preferred movement directions close to those experienced during learning; and (4) the increase in variability emerged at early phases of learning in the SMA, whereas in M1 behavior reached plateau levels of performance. These results are highly consistent with previous findings that showed similar trends in variability across a population of neurons. Together, the results strengthen the idea that single-cell variability can be much more than mere noise and may be an integral part of the underlying mechanism of sensorimotor learning.

Attractor networks

An attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long-term memory, short-term memory, attention, and decision making. The article then shows how the random firing of neurons can influence the stability of these networks by introducing stochastic noise, and how these effects are involved in probabilistic decision making, and implicated in some disorders of cortical function such as poor short-term memory and attention, schizophrenia, and obsessive-compulsive disorder. Copyright © 2009 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

A cell-electrode interface noise model for high-density microelectrode arrays

A cell-electrode interface noise model is developed which is dedicated to enable the co-simulation of the cell-electrode electrical characteristics, along with the electronics of novel CMOS-based MEA. The electrode noise is investigated for Pt and Pt black electrodes. It is shown that the electrode noise can be the dominant noise source in the full system. Moreover, Pt black electrodes benefit from up to 5 microV(rms) decrease of the electrode output noise, for small electrodes. Furthermore, the cell-electrode interface noise spectral density is shown to be 10 dB to 20 dB larger at 1 kHz when a cell is lying on top of the electrode. This increase depends on the neural cell adhesion on the MEA surface.

Factors responsible for force steadiness impairment with fatigue

In this study we investigated the contribution of muscle activation to the impairment of fine force control with fatigue. In three experiments, we manipulated muscle activation and measured force variability before and after a fatigue protocol. When muscle activation was left free (subjects had to match the same absolute force pre- and post-fatigue), fatigue increased muscle activation at moderate force levels only, and force variability increased regardless of the level of force. When muscle activation was controlled (subjects had to match the same electromyographic activity), fatigue no longer increased force variability, except at low force levels. When voluntary muscle activation was suppressed (muscles were electrically stimulated), force variability was unaffected by fatigue. We conclude that the impairment of force steadiness with fatigue is mainly due to the increase in muscle activation at moderate forces, but there are other central sources of force fluctuation present at low force levels.

The independent effect of added mass on the stability of the sagittal plane leg kinematics during steady-state human walking

This study investigated the independent effect of added mass on the stability of the leg kinematics during human walking. We reasoned that adding mass would influence the body's inertial state and thus challenge the ability of the leg to redirect and accelerate the total mass of the body while walking. We hypothesized that walking with added mass would reduce the stability of the leg kinematics. Lower extremity sagittal plane joint kinematics were recorded for 23 subjects as they walked on a treadmill at their preferred speed with and without added mass. The total mass of each subject was manipulated with combinations of simulated reduced gravity and added load. The stability of the leg kinematics was evaluated by computing the eigenvalues of the Poincaré map (i.e. Floquet analysis) that defined the position and velocity of the right hip, knee and ankle at heel-contact and mid-swing. Significant differences in stability were found between the various added mass conditions (P=0.040) and instant in the gait cycle (P=0.001). Post-hoc analysis revealed that walking with 30% added mass compromised the stability of the leg kinematics compared with walking without additional mass (P=0.031). In addition, greater instability was detected at the instance of heel-contact compared with mid-swing (P=0.001). Our results reveal that walking with added mass gives rise to greater disturbances in the leg kinematics, and may be related to the redirection and acceleration of the body throughout the gait cycle. Walking with added mass reduces the stability of the leg kinematics and possibly the overall balance of the walking pattern.

Probing for local activity-related modulation of the infrared backscattering of the brain cortex

The possibility to measure the metabolic activity of the brain cortex, with submillimeter spatial and subsecond temporal resolution, would open up enticing scenarios in addressing basic issues on the relation between different structural components of brain signal processing, and in providing an operational pathway to interaction with (dis)functional signal patterns. In the present article, we report the description of a simple system that allows the detection of the minute changes that occur in the optical backscattering of the cortex as a metabolic response to external stimuli. The simplicity of the system is compatible with scalability to an implantable probe. We validate the system on an animal model, and we propose an algorithm to extract meaningful data from the measured signal. We thus show the detection of individual haemodynamic cortical responses to individual stimulation events, and we provide operational considerations on the signal structure.

Direct estimation of inhomogeneous Markov interval models of spike trains

A necessary ingredient for a quantitative theory of neural coding is appropriate "spike kinematics": a precise description of spike trains. While summarizing experiments by complete spike time collections is clearly inefficient and probably unnecessary, the most common probabilistic model used in neurophysiology, the inhomogeneous Poisson process, often seems too crude. Recently a more general model, the inhomogeneous Markov interval model (Berry & Meister, 1998 ; Kass & Ventura, 2001 ), was considered, which takes into account both the current experimental time and the time from the last spike. Several techniques were proposed to estimate the parameters of these models from data. Here we propose a direct method of estimation that is easy to implement, fast, and conceptually simple. The method is illustrated with an analysis of sample data from the cat's superior colliculus.

Buildup of choice-predictive activity in human motor cortex during perceptual decision making

Simple perceptual decisions are ideally suited for studying the sensorimotor transformations underlying flexible behavior. During perceptual detection, a noisy sensory signal is converted into a behavioral report of the presence or absence of a perceptual experience. Here, we used magnetoencephalography (MEG) to link the dynamics of neural population activity in human motor cortex to perceptual choices in a "yes/no" visual motion detection task. We found that (1) motor response-selective MEG activity in the "gamma" (64-100 Hz) and "beta" (12-36 Hz) frequency ranges predicted subjects' choices several seconds before their overt manual response; (2) this choice-predictive activity built up gradually during stimulus viewing toward both "yes" and "no" choices; and (3) the choice-predictive activity in motor cortex reflected the temporal integral of gamma-band activity in motion-sensitive area MT during stimulus viewing. Because gamma-band activity in MT reflects visual motion strength, these findings suggest that, during motion detection, motor plans for both "yes" and "no" choices result from continuously accumulating sensory evidence. We conclude that frequency-specific neural population activity at the cortical output stage of sensorimotor pathways provides a window into the mechanisms underlying perceptual decisions.

A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex

The responses of neocortical cells to sensory stimuli are variable and state dependent. It has been hypothesized that intrinsic cortical dynamics play an important role in trial-to-trial variability; the precise nature of this dependence, however, is poorly understood. We show here that in auditory cortex of urethane-anesthetized rats, population responses to click stimuli can be quantitatively predicted on a trial-by-trial basis by a simple dynamical system model estimated from spontaneous activity immediately preceding stimulus presentation. Changes in cortical state correspond consistently to changes in model dynamics, reflecting a nonlinear, self-exciting system in synchronized states and an approximately linear system in desynchronized states. We propose that the complex and state-dependent pattern of trial-to-trial variability can be explained by a simple principle: sensory responses are shaped by the same intrinsic dynamics that govern ongoing spontaneous activity.

On the open-loop and feedback processes that underlie the formation of trajectories during visual and nonvisual locomotion in humans

We investigated the nature of the control mechanisms at work during goal-oriented locomotion. In particular, we tested the effects of vision, locomotor speed, and the presence of via points on the geometric and kinematic properties of locomotor trajectories. We first observed that the average trajectories recorded in visual and nonvisual locomotion were highly comparable, suggesting the existence of vision-independent processes underlying the formation of locomotor trajectories. Then by analyzing and comparing the variability around the average trajectories across different experimental conditions, we were able to demonstrate the existence of on-line feedback control in both visual and nonvisual locomotion and to clarify the relations between visual and nonvisual control strategies. Based on these insights, we designed a model in which maximum-smoothness and optimal feedback control principles account, respectively, for the open-loop and feedback processes. Taken together, the experimental and modeling findings provide a novel understanding of the nature of the motor, sensory, and "navigational" processes underlying goal-oriented locomotion.

Motor learning is optimally tuned to the properties of motor noise

In motor learning, our brain uses movement errors to adjust planning of future movements. This process has traditionally been studied by examining how motor planning is adjusted in response to visuomotor or dynamic perturbations. Here, I show that the learning strategy can be better identified from the statistics of movements made in the absence of perturbations. The strategy identified this way differs from the learning mechanism assumed in mainstream models for motor learning. Crucial for this strategy is that motor noise arises partly centrally, in movement planning, and partly peripherally, in movement execution. Corrections are made by modification of central planning signals from the previous movement, which include the effects of planning but not execution noise. The size of the corrections is such that the movement variability is minimized. This physiologically plausible strategy is optimally tuned to the properties of motor noise, and likely underlies learning in many motor tasks.

Dynamics and stability of muscle activations during walking in healthy young and older adults

To facilitate stable walking, humans must generate appropriate motor patterns and effective corrective responses to perturbations. Yet most EMG analyses do not address the continuous nature of muscle activation dynamics over multiple strides. We compared muscle activation dynamics in young and older adults by defining a multivariate state space for muscle activity. Eighteen healthy older and 17 younger adults walked on a treadmill for 2 trials of 5 min each at each of 5 controlled speeds (80-120% of preferred). EMG linear envelopes of v. lateralis, b. femoris, gastrocnemius, and t. anterior of the left leg were obtained. Interstride variability, local dynamic stability (divergence exponents), and orbital stability (maximum Floquet multipliers; FM) were calculated. Both age groups exhibited similar preferred walking speeds (p=0.86). Amplitudes and variability of individual EMG linear envelopes increased with speed (p<0.01) in all muscles but gastrocnemius. Older adults also exhibited greater variability in b. femoris and t. anterior (p<0.004). When comparing continuous multivariate EMG dynamics, older adults demonstrated greater local and orbital instability of their EMG patterns (p<0.01). We also compared how muscle activation dynamics were manifested in kinematics. Local divergence exponents were strongly correlated between kinematics and EMG, independent of age and walking speed, while variability and max FM were not. These changes in EMG dynamics may be related to increased neuromotor noise associated with aging and may indicate subtle deterioration of gait function that could lead to future functional declines.

Error-probability noise benefits in threshold neural signal detection

Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural signal detection by reducing the probability of detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary signal detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold signal detection when the signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of signal probability densities at the detection threshold when the signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.

Probability distributions of Markovian sodium channel states during propagating axonal impulses with or without recovery supernormality

This study addressed a macroscopic neurophysiological phenomenon - supernormality during the recovery phase of propagating axonal impulses - in explicit chemical terms. Excitation was reconstructed numerically using the kinetic scheme of multiple-state probabilistic transitions within a population of voltage-dependent sodium channels (NaCh) derived by Vandenberg and Bezanilla ("PC" scheme). Each NaCh transition was characterized as a reversible Markov process with voltage-dependent rate constants associated with each respective directional transition. While recovery reconstructed with the Hodgkin-Huxley formalism included a supernormal period, the PC scheme did not. The present analysis showed that the occurrence and degree of supernormality with the PC scheme was determined by the relative speed of the transitions within the closed loop of the kinetic scheme; supernormality was promoted by speeding these kinetics. The analysis also showed that concurrent with supernormality, the faster loop kinetics caused (1) an elevation in the C(1) --> C(2) transitions, and (2) a reduction in the I(4) --> I(5) transitions. Thus, macroscopic functionality in information processing could be expressed in terms of probabilistic interstate transitions among a population of NaCh molecules.

Variability of blowfly head optomotor responses

Behavioural responses of an animal are variable even when the animal experiences the same sensory input several times. This variability can arise from stochastic processes inherent to the nervous system. Also, the internal state of an animal may influence a particular behavioural response. In the present study, we analyse the variability of visually induced head pitch responses of tethered blowflies by high-speed cinematography. We found these optomotor responses to be highly variable in amplitude. Most of the variability can be attributed to two different internal states of the flies with high and low optomotor gain, respectively. Even within a given activity state, there is some variability of head optomotor responses. The amount of this variability differs for the two optomotor gain states. Moreover, these two activity states can be distinguished on a fine timescale and without visual stimulation, on the basis of the occurrence of peculiar head jitter movements. Head jitter goes along with high gain optomotor responses and haltere oscillations. Halteres are evolutionary transformed hindwings that oscillate when blowflies walk or fly. Their main function is to serve as equilibrium organs by detecting Coriolis forces and to mediate gaze stabilisation. However, their basic oscillating activity was also suggested to provide a gain-modulating signal. Our experiments demonstrate that halteres are not necessary for high gain head pitch to occur. Nevertheless, we find the halteres to be responsible for one component of head jitter movements. This component may be the inevitable consequence of their function as equilibrium and gaze-stabilising organs.

Principles of neural ensemble physiology underlying the operation of brain-machine interfaces

Research on brain-machine interfaces has been ongoing for at least a decade. During this period, simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons have been used for direct, real-time control of various artificial devices. Brain-machine interfaces have also added greatly to our knowledge of the fundamental physiological principles governing the operation of large neural ensembles. Further understanding of these principles is likely to have a key role in the future development of neuroprosthetics for restoring mobility in severely paralysed patients.

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA)-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

Quasistatic approximation of the interspike interval distribution of neurons driven by time-dependent inputs

Variability in neural responses is a ubiquitous phenomenon in neurons, usually modeled with stochastic differential equations. In particular, stochastic integrate-and-fire models are widely used to simplify theoretical studies. The statistical properties of the generated spikes depend on the stimulating input current. Given that real sensory neurons are driven by time-dependent signals, here we study how the interspike interval distribution of integrate-and-fire neurons depends on the evolution of the stimulus in a quasistatic limit. We obtain a closed-form expression for this distribution, and we compare it to the one obtained with numerical simulations for several time-dependent currents. For slow inputs, the quasistatic distribution provides a very good description of the data. The results obtained for the integrate-and-fire model can be extended to other nonautonomous stochastic systems where the first passage time problem has an explicit solution.

Key role of coupling, delay, and noise in resting brain fluctuations

A growing body of neuroimaging research has documented that, in the absence of an explicit task, the brain shows temporally coherent activity. This so-called "resting state" activity or, more explicitly, the default-mode network, has been associated with daydreaming, free association, stream of consciousness, or inner rehearsal in humans, but similar patterns have also been found under anesthesia and in monkeys. Spatiotemporal activity patterns in the default-mode network are both complex and consistent, which raises the question whether they are the expression of an interesting cognitive architecture or the consequence of intrinsic network constraints. In numerical simulation, we studied the dynamics of a simplified cortical network using 38 noise-driven (Wilson-Cowan) oscillators, which in isolation remain just below their oscillatory threshold. Time delay coupling based on lengths and strengths of primate corticocortical pathways leads to the emergence of 2 sets of 40-Hz oscillators. The sets showed synchronization that was anticorrelated at <0.1 Hz across the sets in line with a wide range of recent experimental observations. Systematic variation of conduction velocity, coupling strength, and noise level indicate a high sensitivity of emerging synchrony as well as simulated blood flow blood oxygen level-dependent (BOLD) on the underlying parameter values. Optimal sensitivity was observed around conduction velocities of 1-2 m/s, with very weak coupling between oscillators. An additional finding was that the optimal noise level had a characteristic scale, indicating the presence of stochastic resonance, which allows the network dynamics to respond with high sensitivity to changes in diffuse feedback activity.

Symbiotic relationship between brain structure and dynamics

BACKGROUND

Brain structure and dynamics are interdependent through processes such as activity-dependent neuroplasticity. In this study, we aim to theoretically examine this interdependence in a model of spontaneous cortical activity. To this end, we simulate spontaneous brain dynamics on structural connectivity networks, using coupled nonlinear maps. On slow time scales structural connectivity is gradually adjusted towards the resulting functional patterns via an unsupervised, activity-dependent rewiring rule. The present model has been previously shown to generate cortical-like, modular small-world structural topology from initially random connectivity. We provide further biophysical justification for this model and quantitatively characterize the relationship between structure, function and dynamics that accompanies the ensuing self-organization.

RESULTS

We show that coupled chaotic dynamics generate ordered and modular functional patterns, even on a random underlying structural connectivity. Consequently, structural connectivity becomes more modular as it rewires towards these functional patterns. Functional networks reflect the underlying structural networks on slow time scales, but significantly less so on faster time scales. In spite of ordered functional topology, structural networks remain robustly interconnected--and therefore small-world--due to the presence of central, inter-modular hub nodes. The noisy dynamics of these hubs enable them to persist despite ongoing rewiring and despite their comparative absence in functional networks.

CONCLUSION

Our results outline a theoretical mechanism by which brain dynamics may facilitate neuroanatomical self-organization. We find time scale dependent differences between structural and functional networks. These differences are likely to arise from the distinct dynamics of central structural nodes.

Choosing to switch: spontaneous task switching despite associated behavioral costs

The literature shows that switching among simple cognitive tasks is difficult and involves a performance cost. Accordingly, cost-benefit considerations seem to predict that task switching would not occur spontaneously. Here we show that spontaneous task switching is a robust phenomenon, despite its costs. In Experiment 1, participants had to judge shapes according to one of three possible dimensions. Importantly, they were given the option to choose another relevant dimension or let the computer program change the dimension for them, but only if they wanted to do so. The results showed that spontaneous task switching was prevalent, despite robust switching costs. Experiment 2 extended this finding in showing spontaneous switching from an easy task to a more difficult task. The authors provide two possible explanations for the phenomenon that posit that spontaneous switching may be unpreventable or even advantageous.

Human sound-localization behaviour after multiple changes in eye position

Orienting the eyes towards a peripheral sound source calls for a transformation of the head-centred sound coordinates into an oculocentric motor command, which requires an estimate of current eye position. Current models of saccadic control explain spatial accuracy by oculocentric transformations that rely on efference copies of relative eye-displacement signals, rather than on absolute eye position in the orbit. In principle, the gaze-control system could keep track of instantaneous eye position by vector addition of intervening eye-displacement commands. However, given that each motor update is endowed with some noise, the neural estimate of eye orientation is then expected to become noisier with increasing number of intervening saccades. As a consequence, the localization response will also be noisier. According to the alternative, in which target updates rely on feedback of the current eye position, such an increase in errors would be absent. In an attempt to dissociate these hypotheses, we studied the influence of the accumulation of oculomotor commands prior to a sound-localization response. Head-restrained subjects generated voluntary eye movements in darkness in random directions for a period between 0.2 and 15 s, after which they rapidly reoriented the eyes towards a brief sound burst. The results demonstrate that the audiomotor system programmes the orienting response on the basis of actual eye position, rather than on an accumulated estimate from intervening eye displacements.

A dynamic corral model of receptor trafficking at a synapse

The postsynaptic density (PSD) is a cytoskeletal specialization within the postsynaptic membrane of a neuron that helps to concentrate and organize neurotransmitter receptors at a chemical synapse. The total number of receptors within the PSD, which is a major factor in determining the physiological strength or weight of a synapse, fluctuates due to the surface diffusion of receptors into and out of the PSD, and the interactions of receptors with scaffolding proteins and cytoskeletal elements within the PSD. In this article, we present a stochastic model of protein receptor trafficking at the PSD that takes into account these various processes. The PSD is treated as a stochastically gated corral, which contributes a source of extrinsic or environmental noise that supplements the intrinsic noise arising from small receptor numbers. Using a combination of stochastic analysis and Monte Carlo simulations, we determine the time-dependent variation in the mean and variance of synaptic receptor numbers for a variety of initial conditions that simulate fluorescence recovery after photobleaching experiments, and indicate how such data might be used to infer certain properties of the PSD.

Ideal observer analysis of signal quality in retinal circuits

The function of the retina is crucial, for it must encode visual signals so the brain can detect objects in the visual world. However, the biological mechanisms of the retina add noise to the visual signal and therefore reduce its quality and capacity to inform about the world. Because an organism's survival depends on its ability to unambiguously detect visual stimuli in the presence of noise, its retinal circuits must have evolved to maximize signal quality, suggesting that each retinal circuit has a specific functional role. Here we explain how an ideal observer can measure signal quality to determine the functional roles of retinal circuits. In a visual discrimination task the ideal observer can measure from a neural response the increment threshold, the number of distinguishable response levels, and the neural code, which are fundamental measures of signal quality relevant to behavior. It can compare the signal quality in stimulus and response to determine the optimal stimulus, and can measure the specific loss of signal quality by a neuron's receptive field for non-optimal stimuli. Taking into account noise correlations, the ideal observer can track the signal-to-noise ratio available from one stage to the next, allowing one to determine each stage's role in preserving signal quality. A comparison between the ideal performance of the photon flux absorbed from the stimulus and actual performance of a retinal ganglion cell shows that in daylight a ganglion cell and its presynaptic circuit loses a factor of approximately 10-fold in contrast sensitivity, suggesting specific signal-processing roles for synaptic connections and other neural circuit elements. The ideal observer is a powerful tool for characterizing signal processing in single neurons and arrays along a neural pathway.

Impedance control and its relation to precision in orofacial movement

Speech production involves some of the most precise and finely timed patterns of human movement. Here, in the context of jaw movement in speech, we show that spatial precision in speech production is systematically associated with the regulation of impedance and in particular, with jaw stiffness--a measure of resistance to displacement. We estimated stiffness and also variability during movement using a robotic device to apply brief force pulses to the jaw. Estimates of stiffness were obtained using the perturbed position and force trajectory and an estimate of what the trajectory would be in the absence of load. We estimated this "reference trajectory" using a new technique based on Fourier analysis. A moving-average (MA) procedure was used to estimate stiffness by modeling restoring force as the moving average of previous jaw displacements. The stiffness matrix was obtained from the steady state of the MA model. We applied this technique to data from 31 subjects whose jaw movements were perturbed during speech utterances and kinematically matched nonspeech movements. We observed systematic differences in stiffness over the course of jaw-lowering and jaw-raising movements that were correlated with measures of kinematic variability. Jaw stiffness was high and variability was low early and late in the movement when the jaw was elevated. Stiffness was low and variability was high in the middle of movement when the jaw was lowered. Similar patterns were observed for speech and nonspeech conditions. The systematic relationship between stiffness and variability points to the idea that stiffness regulation is integral to the control of orofacial movement variability.

What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations--e.g., random noise--cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being "suboptimal". Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the "neural code". Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise--via stochastic resonance or otherwise--than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing "noise benefits", and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.

Relationship between spontaneous and evoked spike-time correlations in primate visual cortex

Coincident spikes have been implicated in vision-related processes such as feature binding, gain modulation, and long-distance communication. The source of these spike-time correlations is unknown. Although several studies have proposed that cortical spikes are correlated based on stimulus structure, others have suggested that spike-time correlations reflect ongoing cortical activity present even in the absence of a coherent visual stimulus. To examine this issue, we collected single-unit recordings from primary visual cortex (V1) of the anesthetized and paralyzed prosimian bush baby using a 100-electrode array. Spike-time correlations for pairs of cells were compared under three conditions: a moving grating at the cells' preferred orientation, an equiluminant blank screen, and a dark condition with eyes covered. The amplitudes, lags, and widths of cross-correlation histograms (CCHs) were strongly correlated between these conditions although for the blank stimulus and dark condition, the CCHs were broader with peaks lower in amplitude. In both preferred stimulus and blank conditions, the CCH amplitudes were greater when the cells within the pair had overlapping receptive fields and preferred similar orientations rather than nonoverlapping receptive fields and different orientations. These data suggest that spike-time correlations present in evoked activity are generated by mechanisms common to those operating in spontaneous conditions.

Optimality and robustness of a biophysical decision-making model under norepinephrine modulation

The locus ceruleus (LC) can exhibit tonic or phasic activity and release norepinephrine (NE) throughout the cortex, modulating cellular excitability and synaptic efficacy and thus influencing behavioral performance. We study the effects of LC-NE modulation on decision making in two-alternative forced-choice tasks by changing conductances in a biophysical neural network model, and we investigate how it affects performance measured in terms of reward rate. We find that low tonic NE levels result in unmotivated behavior and high levels in impulsive, inaccurate choices, but that near-optimal performance can occur over a broad middle range. Robustness is greatest when pyramidal cells are less strongly modulated than interneurons, and superior performance can be achieved with phasic NE release, provided only glutamatergic synapses are modulated. We also show that network functions such as sensory information accumulation and short-term memory can be modulated by tonic NE levels, and that previously observed diverse evoked cell responses may be due to network effects.

Visual adaptation to convexity in macaque area V4

Aftereffects are perceptual illusions caused by visual adaptation to one or more stimulus attribute, such as orientation, motion, or shape. Neurophysiological studies seeking to understand the basis of visual adaptation have observed firing rate reduction and changes in tuning of stimulus-selective neurons following periods of prolonged visual stimulation. In the domain of shape, recent psychophysical work has shown that adaptation to a convex pattern induces a subsequently seen rectangle to appear slightly concave. In the present study, we investigate the possible contribution of V4 neurons of rhesus monkeys, which are thought to be involved in the coding of convexity, to shape-specific adaptation. Visually responsive neurons were monitored during the brief presentation of simple shapes varying in their convexity level. Each test presentation was preceded by either a blank period or several seconds of adaptation to a convex or concave stimulus, presented in two different sizes. Adaptation consistently shifted the tuning of neurons away from the convex or concave adapter, including shifting response to the neutral rectangle in the direction of the opposite convexity. This repulsive shift resembled the known perceptual distortion associated with adaptation to such stimuli. In addition, adaptation caused a nonspecific response decrease, as well as a specific decrease for repeated stimuli. The latter effects were observed whether or not the adapting and test stimuli matched closely in their size. Taken together, these results provide evidence for shape-specific adaptation of neurons in area V4, which may contribute to the perception of the convexity aftereffect.

Factor-analysis methods for higher-performance neural prostheses

Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. If a system does not properly account for this variability, it may mistakenly interpret such variability as an entirely different intention by the subject. We report here the design and characterization of factor-analysis (FA)-based decoding algorithms that can contend with this confound. We characterize the decoders (classifiers) on experimental data where monkeys performed both a real reach task and a prosthetic cursor task while we recorded from 96 electrodes implanted in dorsal premotor cortex. The decoder attempts to infer the underlying factors that comodulate the neurons' responses and can use this information to substantially lower error rates (one of eight reach endpoint predictions) by <or=75% (e.g., approximately 20% total prediction error using traditional independent Poisson models reduced to approximately 5%). We also examine additional key aspects of these new algorithms: the effect of neural integration window length on performance, an extension of the algorithms to use Poisson statistics, and the effect of training set size on the decoding accuracy of test data. We found that FA-based methods are most effective for integration windows >150 ms, although still advantageous at shorter timescales, that Gaussian-based algorithms performed better than the analogous Poisson-based algorithms and that the FA algorithm is robust even with a limited amount of training data. We propose that FA-based methods are effective in modeling correlated trial-to-trial neural variability and can be used to substantially increase overall prosthetic system performance.

The effect of angle and level of exertion on trunk neuromuscular performance during multidirectional isometric activities

STUDY DESIGN

To quantify trunk muscle capability and controllability in different angles and levels of isometric exertion using a torque tracking system.

OBJECTIVE

To investigate the effect of biaxial isometric exertions on the maximum capability of trunk and to examine the effect of angle and level of isometric exertion on trunk controllability during the tracking task in upright posture.

SUMMARY OF BACKGROUND DATA

Combined motions of trunk at varying exertion levels occur in most daily and occupational activities and are important risk factors of low back pain. Few studies have investigated trunk capability and controllability during multidirectional activities with different exertion levels.

METHODS

Eighteen asymptomatic young male subjects performed isometric contractions of trunk muscles in 8 angles and 3 levels of exertion. The tracking system included a target, which was a thick line with a round endpoint. Subjects were asked to track the target line (path) and match the endpoint while maintaining torque for 3 seconds by exerting isometric contraction against B200 Isostation. The initial part of the tracking task was named path tracking phase and the final part, endpoint matching phase. Trunk capability was determined by measuring peak torque values obtained during maximal voluntary exertions. Trunk controllability was determined by measuring constant error and variable error during tracking tasks. Analysis of variance with repeated measures design was used to test the effects of angle and level of exertion on trunk capability and controllability.

RESULTS

Trunk capability was significantly decreased during biaxial exertions (P < 0.001). Constant error and variable error were significantly affected by angle (P < 0.001) and level (P < 0.001) of exertion during both phases of the tracking task.

CONCLUSION

Trunk capability and controllability were significantly decreased during biaxial exertions. Higher exertion levels had a major negative impact on trunk controllability in both uniaxial and biaxial exertions. The results suggested that combined exertions and more strenuous efforts may impair trunk neuromuscular control, increasing the risk of low back pain.

Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity

Background

Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.

Methodology/Principal Findings

Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time.

Conclusions/Significance

We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns.

Discharges in human muscle receptor afferents during block grasping

Human grasping relies on feedforward control that is monitored and corrected on-line by means of sensory feedback. While much of the sensory mechanisms underpinning hand-object interaction are known, information has been lacking about muscle receptor responses during the phases before and after actual object contact. We therefore let subjects use their thumb and fingers to grasp blocks presented to them while we recorded muscle afferents from the thumb and finger extensor muscles along with wrist and digit kinematics, and electromyographic activity. The kinematics of the task was indistinguishable from "normal" grasping. None of the afferents encoded either object contact or finger apposition. Both primary and secondary afferents were more phase advanced on the parent muscle lengths than expected from previous studies as well as from their responses to imposed length changes of their parent muscles. Thus, the discharges of both primary and secondary afferents were well correlated to the tendon velocity of their parent muscles and that of primary afferents also to acceleration whereas neither appeared to encode muscle length as such. Decoding the velocity of muscle length changes were significantly improved if the discharge of Golgi tendon organ afferents were taken into account along with that of the muscle spindle afferents. We propose that these findings may be explained by the biomechanical properties of contracting muscles. Moreover, we conclude that it seems unlikely that the muscle spindle afferents recorded in this task have any role in providing "proprioceptive" information pertaining to the size of an object grasped.

Spike-time reliability of layered neural oscillator networks

We study the reliability of layered networks of coupled "type I" neural oscillators in response to fluctuating input signals. Reliability means that a signal elicits essentially identical responses upon repeated presentations, regardless of the network's initial condition. We study reliability on two distinct scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within a network, and pooled-response reliability, which concerns the repeatability of total synaptic outputs from a subpopulation of the neurons in a network. We find that neuronal reliability depends strongly both on the overall architecture of a network, such as whether it is arranged into one or two layers, and on the strengths of the synaptic connections. Specifically, for the type of single-neuron dynamics and coupling considered, single-layer networks are found to be very reliable, while two-layer networks lose their reliability with the introduction of even a small amount of feedback. As expected, pooled responses for large enough populations become more reliable, even when individual neurons are not. We also study the effects of noise on reliability, and find that noise that affects all neurons similarly has much greater impact on reliability than noise that affects each neuron differently. Qualitative explanations are proposed for the phenomena observed.

Sensory transmission in cerebellar granule cells relies on similarly coded mossy fiber inputs

The computational principles underlying the processing of sensory-evoked synaptic inputs are understood only rudimentarily. A critical missing factor is knowledge of the activation patterns of the synaptic inputs to the processing neurons. Here we use well-defined, reproducible skin stimulation to describe the specific signal transformations that occur in different parallel mossy fiber pathways and analyze their representation in the synaptic inputs to cerebellar granule cells. We find that mossy fiber input codes are preserved in the synaptic responses of granule cells, suggesting a coding-specific innervation. The computational consequences of this are that it becomes possible for granule cells to also transmit weak sensory inputs in a graded fashion and to preserve the specific activity patterns of the mossy fibers.

Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task

Most behavioral tasks have time constraints for successful completion, such as catching a ball in flight. Many of these tasks require trading off the time allocated to perception and action, especially when only one of the two is possible at any time. In general, the longer we perceive, the smaller the uncertainty in perceptual estimates. However, a longer perception phase leaves less time for action, which results in less precise movements. Here we examine subjects catching a virtual ball. Critically, as soon as subjects began to move, the ball became invisible. We study how subjects trade-off sensory and movement uncertainty by deciding when to initiate their actions. We formulate this task in a probabilistic framework and show that subjects' decisions when to start moving are statistically near optimal given their individual sensory and motor uncertainties. Moreover, we accurately predict individual subject's task performance. Thus we show that subjects in a natural task are quantitatively aware of how sensory and motor variability depend on time and act so as to minimize overall task variability.