Showing papers in "Biological Cybernetics in 2000"
TL;DR: It is shown that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series and a bootstrap procedure is proposed to assess the variability in the estimated spectral quantities.
Abstract: In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.
608 citations
TL;DR: This study demonstrates that a neurophysiologically relevant model can be extended to generate spontaneous EEG signals from multiple coupled neural populations and shows that, through the model, real SEEG signals can be interpreted with the aid of signal processing methods.
Abstract: In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG, intra-cerebral recording) signals with signal processing methods can help to better identify the epileptogenic zone, the area of the brain responsible for triggering seizures, and to better understand its organization. In order to evaluate these methods and to physiologically interpret the results they provide, we developed a model able to produce EEG signals from “organized” networks of neural populations. Starting from a neurophysiologically relevant model initially proposed by Lopes Da Silva et al. [Lopes da Silva FH, Hoek A, Smith H, Zetterberg LH (1974) Kybernetic 15: 27–37] and recently re-designed by Jansen et al. [Jansen BH, Zouridakis G, Brandt ME (1993) Biol Cybern 68: 275–283] the present study demonstrates that this model can be extended to generate spontaneous EEG signals from multiple coupled neural populations. Model parameters related to excitation, inhibition and coupling are then altered to produce epileptiform EEG signals. Results show that the qualitative behavior of the model is realistic; simulated signals resemble those recorded from different brain structures for both interictal and ictal activities. Possible exploitation of simulations in signal processing is illustrated through one example; statistical couplings between both simulated signals and real SEEG signals are estimated using nonlinear regression. Results are compared and show that, through the model, real SEEG signals can be interpreted with the aid of signal processing methods.
382 citations
TL;DR: It is demonstrated that a very simple closed-loop control model of upright stance can generate realistic stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons.
Abstract: Collins and De Luca [Collins JJ. De Luca CJ (1993) Exp Brain Res 95: 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation (error signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs. This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.
380 citations
TL;DR: It is argued that the results can be used as a detailed blueprint for building artificial neural networks with a cortex-like architecture after a new way of obtaining such an estimate was presented.
Abstract: This study provides a detailed quantitative estimate for local synaptic connectivity between neocortical pyramidal neurons. A new way of obtaining such an estimate is presented. In acute slices of the rat visual cortex, four layer 2 and four layer 3 pyramidal neurons were intracellularly injected with biocytin. Axonal and dendritic arborizations were three-dimensionally reconstructed with the aid of a computer-based camera lucida system. In a computer experiment, pairs of pre- and postsynaptic neurons were formed and potential synaptic contacts were calculated. For each pair, the calculations were carried out for a whole range of distances (0 to 500 μm) between the presynaptic and the postsynaptic neuron, in order to estimate cortical connectivity as a function of the spatial separation of neurons. It was also differentiated whether neurons were situated in the same or in different cortical layers. The data thus obtained was used to compute connection probabilities, the average number of contacts between neurons, the frequency of specific numbers of contacts and the total number of contacts a dendritic tree receives from the surrounding cortical volume. Connection probabilities ranged from 50% to 80% for directly adjacent neurons and from 0% to 15% for neurons 500 μm apart. In many cases, connections were mediated by one contact only. However, close neighbors made on average up to 3 contacts with each other. The question as to whether the method employed in this study yields a realistic estimate of synaptic connectivity is discussed. It is argued that the results can be used as a detailed blueprint for building artificial neural networks with a cortex-like architecture.
341 citations
TL;DR: The spatial representation in this model of the rat hippocampus is built on-line during exploration via two processing streams and focuses on the neural pathway connecting the hippocampus to the nucleus accumbens.
Abstract: A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot.
285 citations
TL;DR: It is shown that, during landing, the bee decelerates continuously and in such a way as to keep the projected time to touchdown constant as the surface is approached, which reflects a surprisingly simple and effective strategy for achieving a smooth landing.
Abstract: Freely flying bees were filmed as they landed on a flat, horizontal surface, to investigate the underlying visuomotor control strategies. The results reveal that (1) landing bees approach the surface at a relatively shallow descent angle; (2) they tend to hold the angular velocity of the image of the surface constant as they approach it; and (3) the instantaneous speed of descent is proportional to the instantaneous forward speed. These characteristics reflect a surprisingly simple and effective strategy for achieving a smooth landing, by which the forward and descent speeds are automatically reduced as the surface is approached and are both close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. A model of the control scheme is developed and its predictions are verified. It is also shown that, during landing, the bee decelerates continuously and in such a way as to keep the projected time to touchdown constant as the surface is approached. The feasibility of this landing strategy is demonstrated by implementation in a robotic gantry equipped with vision.
247 citations
TL;DR: In this paper, the authors combine experimental findings on ants and bees, and build on earlier models, to give an account of how these insects navigate using path integration, and how path integration interacts with other modes of navigation.
Abstract: We combine experimental findings on ants and bees, and build on earlier models, to give an account of how these insects navigate using path integration, and how path integration interacts with other modes of navigation. At the core of path integration is an accumulator. This is set to an initial state at the nest and is updated as the insect moves so that it always reports the insect's current position relative to the nest. Navigation that uses path integration requires, in addition, a way of storing states of the accumulator at significant places for subsequent recall as goals, and a means of computing the direction to such goals. We discuss three models of how path integration might be used for this process, which we call vector navigation. Vector navigation is the principal means of navigating over unfamiliar terrain, or when landmarks are unavailable. Under other conditions, insects often navigate by landmarks, and ignore the output of the vector navigation system. Landmark navigation does not interfere with the updating of the accumulator. There is an interesting symmetry in the use of landmarks and path integration. In the short term, vector navigation can be independent of landmarks, and landmark navigation needs no assistance from path integration. In the longer term, visual landmarks help keep path vector navigation calibrated, and the learning of visual landmarks is guided by path integration.
234 citations
TL;DR: Most strikingly, it is shown that mechanics alone can confer asymptotic stability in heading and body orientation in rapidly running cockroaches.
Abstract: We study the dynamics and stability of legged locomotion in the horizontal plane. Motivated by experimental studies of insects, we develop two- and three-degree-of freedom rigid body models with pairs of 'virtual' elastic legs in intermittent contact with the ground. We focus on conservative compliant-legged models, but we also consider prescribed forces, prescribed leg displacements, and combined strategies. The resulting mechanical systems exhibit periodic gaits whose stability characteristics are due to intermittent foot contact, and are largely determined by geometrical criteria. Most strikingly, we show that mechanics alone can confer asymptotic stability in heading and body orientation. In a companion paper, we apply our results to rapidly running cockroaches.
222 citations
TL;DR: Applying the results of this study to rapidly running cockroaches, it is shown that the models' gait and force characteristics match observations reasonably well.
Abstract: We study the dynamics and stability of legged locomotion in the horizontal plane. We discuss the relevance of idealized mechanical models, developed in a companion paper, to recent experiments and simulations on insect running and turning. Applying our results to rapidly running cockroaches, we show that the models' gait and force characteristics match observations reasonably well.
177 citations
TL;DR: It is shown that a special case of the matched filter model is able to predict the local motion sensitivities observed in some VS neurons, which suggests that their receptive field organization enables the VS neurons to maintain a consistent output when the same type of self-motion occurs in different situations.
Abstract: The receptive field organization of a class of visual interneurons in the fly brain (vertical system, or VS neurons) shows a striking similarity to certain self-motion-induced optic flow fields. The present study compares the measured motion sensitivities of the VS neurons (Krapp et al. 1998) to a matched filter model for optic flow fields generated by rotation or translation. The model minimizes the variance of the filter output caused by noise and distance variability between different scenes. To that end, prior knowledge about distance and self-motion statistics is incorporated in the form of a “world model”. We show that a special case of the matched filter model is able to predict the local motion sensitivities observed in some VS neurons. This suggests that their receptive field organization enables the VS neurons to maintain a consistent output when the same type of self-motion occurs in different situations.
159 citations
TL;DR: In this paper, the authors show that the neuronal network may exist in two different dynamical states: one state in which the neuronal networks behave as a non-chaotic deterministic system and another state where the system exhibits large spatio-temporal fluctuations, characteristic of stochastic or chaotic systems.
Abstract: Neuronal networks of dissociated cortical neurons from neonatal rats were cultured over a multielectrode dish with 64 active sites, which were used both for recording the electrical activity and for stimulation. After about 4 weeks of culture, a dense network of neurons had developed and their electrical activity was studied. When a brief voltage pulse was applied to one extracellular electrode, a clear electrical response was evoked over almost the entire network. When a strong voltage pulse was used, the response was composed of an early phase, terminating within 25 ms, and a late phase which could last several hundreds of milliseconds. Action potentials evoked during the early phase occurred with a precise timing with a small jitter and the electrical activity initiated by a localized stimulation diffused significantly over the network. In contrast, the late phase was characterized by the occurrence of clusters of electrical activity with significant spatio-temporal fluctuations. The late phase was suppressed by adding small amounts of d(−)-2-amino-5-phosphonovaleric acid to the extracellular medium, or by increasing the amount of extracellular Mg2+. The electrical activity of the network was substantially increased by the addition of bicuculline to the extracellular medium. The results presented here show that the neuronal network may exist in two different dynamical states: one state in which the neuronal network behaves as a non-chaotic deterministic system and another state where the system exhibits large spatio-temporal fluctuations, characteristic of stochastic or chaotic systems.
TL;DR: A computational model of the lesion and single unit data from navigation in rats is reviewed and includes consideration of the phase of firing of place cells with respect to the theta rhythm of hippocampal EEG.
Abstract: A computational model of the lesion and single unit data from navigation in rats is reviewed. The model uses external (visual) and internal (odometric) information from the environment to drive the firing of simulated hippocampal place cells. Constraints on the functional form of these inputs are drawn from experiments using an environment of modifiable shape. The place cell representation is used to guide navigation via the creation of a representation of goal location via Hebbian modification of synaptic strengths. The model includes consideration of the phase of firing of place cells with respect to the theta rhythm of hippocampal EEG. A series of predictions for behavioural and single-unit data in rats are derived from the input and output representations of the model.
TL;DR: A simple hypothesis regarding the recognition behaviour of crickets for conspecific songs is implemented in a dynamic simulation of spiking neurons and tested on a robot base and a number of properties can be observed in the neural circuit that correspond to cricket neurophysiology including apparent `recognition neurons'.
Abstract: A simple hypothesis regarding the recognition behaviour of crickets for conspecific songs is implemented in a dynamic simulation of spiking neurons and tested on a robot base. The model draws on data from cricket neurophysiology but requires only four neurons to reproduce a wide range of the observed behaviour. The directional response depends on relative latencies in firing onset, and the `recognition' emerges from the implicit filtering properties of leaky-integrate-and-fire neurons. Experimental conditions reproduced include tests of syllable rate preference, song from above with sound from one side, and choice between songs. The robot produces behaviour closely comparable to the cricket in all but a `split-song' condition. A number of properties can be observed in the neural circuit that correspond to cricket neurophysiology including apparent `recognition neurons'. Limitations of the model, extensions and alternative models are discussed.
TL;DR: Impedance characteristics of the human fingertips in the tangential directions to the tip surface are described and it is shown that the average strain rate of the fingertip in theTangential direction to the fingertips surface became slower and converged to a constant value with higher contact forces.
Abstract: The shearing strain of the human fingertip plays an important role in the determination of the optimal grasping force and in the perception of texture. Most research concerned with the mechanical impedance of the human fingertips has treated the orthogonal direction to the tip surface, and little attention has been paid to the tangential direction. This paper describes impedance characteristics of the human fingertips in the tangential directions to the tip surface. In the experiment, step and ramp shearing forces were individually applied to the tips of the thumb, middle finger, and little finger. Dynamics of the fingertips were represented by the Kelvin model. Experimental results show that each fingertip had different properties with respect to the shearing strain versus the applied force, and that the thumb had the strongest shearing stiffness among these three digits. Moreover, the shearing stiffness depended on the direction of the applied force, and the stiffness in the pointing direction was stronger than that in the perpendicular direction. As the contact force in the orthogonal direction to the fingertip surface was increased, the shearing stiffness and viscosity increased without regard to the load speed of the shearing force. Furthermore, it is shown that the average strain rate of the fingertip in the tangential direction to the fingertip surface became slower and converged to a constant value with higher contact forces.
TL;DR: This article provides threefold support for the average landmark vector model by synthetic modeling: first, it was shown that a mobile robot using the ALV model returns to the target location with only small position errors; second, thebehavior of the robot resembled the behavior of bees in some experiments; and third, the ALv model was implemented on the robot in analog hardware.
Abstract: The visual homing abilities of insects can be explained by the snapshot hypothesis. It asserts that an animal is guided to a previously visited location by comparing the current view with a snapshot taken at that location. The average landmark vector (ALV) model is a parsimonious navigation model based on the snapshot hypothesis. According to this model, the target location is unambiguously characterized by a signature vector extracted from the snapshot image. This article provides threefold support for the ALV model by synthetic modeling. First, it was shown that a mobile robot using the ALV model returns to the target location with only small position errors. Second, the behavior of the robot resembled the behavior of bees in some experiments. And third, the ALV model was implemented on the robot in analog hardware. This adds validity to the ALV model, since analog electronic circuits share a number of information-processing principles with biological nervous systems; the analog implementation therefore provides suggestions for how visual homing abilities might be implemented in the insect's brain.
TL;DR: It was shown that the optimal noise level and the magnitude of the SR effect depend on the difficulty of the task and a computational framework based on leaky accumulators that integrate noisy information and provide the output upon reaching a threshold criterion is used to illustrate the observed phenomena.
Abstract: The stochastic resonance (SR) phenomenon in human cognition (memory retrieval speed for arithmetical multiplication rules) is addressed in a behavioral and neurocomputational study. The results of an experiment in which performance was monitored for various magnitudes of acoustic noise are presented. The average response time was found to be minimal for some optimal noise level. Moreover, it was shown that the optimal noise level and the magnitude of the SR effect depend on the difficulty of the task. A computational framework based on leaky accumulators that integrate noisy information and provide the output upon reaching a threshold criterion is used to illustrate the observed phenomena.
TL;DR: In this paper, a simple dynamical model of a hexapod by computer simulations was used to investigate the relationship between the energy loss for torque generation and the sum of positive mechanical work and heat energy loss that is proportional to the square of joint torque.
Abstract: The gait transition in legged animals has attracted many researchers, and its relation to metabolic cost and mechanical work has been discussed in recent decades. We assumed that the energetic cost during locomotion is given by the sum of positive mechanical work and the heat energy loss that is proportional to the square of joint torque and examined the optimal locomotor pattern based on the energetic cost in a simple dynamical model of a hexapod by computer simulations. The obtained results well agree with characteristics in the locomotor patterns in legged animals; for example, the leg protraction time, step length, and the metabolic cost of transport are almost constant for many velocities, the leg cycling period decreases with velocity, and the energetic cost of locomotion induced by carrying loads linearly increases with mass loaded. This correspondence of the results of calculation to experimental results suggest that the heat energy loss for torque generation is proportional to the square of the torque during locomotion, and that the locomotor pattern in legged animals is highly optimized based on the energetic cost.
TL;DR: In this paper, a two-compartment model of a pyloric CPG neuron incorporating previously described membrane conductances together with intracellular Ca2+ dynamics involving the endoplasmic reticulum and the inositol 1,4,5-trisphosphate receptor IP3R was built.
Abstract: Chaotic bursting has been recorded in synaptically isolated neurons of the pyloric central pattern generating (CPG) circuit in the lobster stomatogastric ganglion. Conductance-based models of pyloric neurons typically fail to reproduce the observed irregular behavior in either voltage time series or state-space trajectories. Recent suggestions of Chay [Biol Cybern 75: 419-431] indicate that chaotic bursting patterns can be generated by model neurons that couple membrane currents to the nonlinear dynamics of intracellular calcium storage and release. Accordingly, we have built a two-compartment model of a pyloric CPG neuron incorporating previously described membrane conductances together with intracellular Ca2+ dynamics involving the endoplasmic reticulum and the inositol 1,4,5-trisphosphate receptor IP3R. As judged by qualitative inspection and quantitative, nonlinear analysis, the irregular voltage oscillations of the model neuron resemble those seen in the biological neurons. Chaotic bursting arises from the interaction of fast membrane voltage dynamics with slower intracellular Ca2+ dynamics and, hence, depends on the concentration of IP3. Despite the presence of 12 independent dynamical variables, the model neuron bursts chaotically in a subspace characterized by 3-4 active degrees of freedom. The critical aspect of this model is that chaotic oscillations arise when membrane voltage processes are coupled to another slow dynamic. Here we suggest this slow dynamic to be intracellular Ca2+ handling.
TL;DR: A new, biologically plausible cerebellar model is presented to study how fast arm movements can be executed in spite of long conduction delays in the nervous system to solve the temporal mismatch problem between efferent motor commands and delayed error signals.
Abstract: Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.
TL;DR: Global analysis of the bifurcation structure suggested that generation of these regions is associated with degenerate Hopf biforcations, and identified parameter regions in which either two stable periodic solutions with different amplitudes and periods and a stable equilibrium point or twostable periodic solutions coexist.
Abstract: The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. We explored the dynamics of the HH for a wide range of parameter values in the multiple-parameter space, that is, we examined the global structure of bifurcations of the HH. Results are summarized in various two-parameter bifurcation diagrams with I
ext (externally applied DC current) as the abscissa and one of the other parameters as the ordinate. In each diagram, the parameter plane was divided into several regions according to the qualitative behavior of the equations. In particular, we focused on periodic solutions emerging via Hopf bifurcations and identified parameter regions in which either two stable periodic solutions with different amplitudes and periods and a stable equilibrium point or two stable periodic solutions coexist. Global analysis of the bifurcation structure suggested that generation of these regions is associated with degenerate Hopf bifurcations.
TL;DR: Contrary to the throws and the zeniths, and regardless of juggling speed, consecutive catches of the same hand showed a markedly negative lag-one serial correlation, suggesting that the catches are timed so as to preserve the temporal integrity of the juggling act.
Abstract: To uncover the underlying control structure of three-ball cascade juggling, we studied its spatiotemporal properties in detail. Juggling patterns, performed at fast and preferred speeds, were recorded in the frontal plane and subsequently analyzed using principal component analysis and serial correlation techniques. As was expected on theoretical grounds, the principal component analysis revealed that maximally four instead of the original six dimensions (3 balls × 2 planar coordinates) are sufficient for describing the juggling dynamics. Juggling speed was shown to affect the number of dimensions (four for the fast condition, two for the preferred condition) as well as the smoothness of the time evolution of the eigenvectors of the principal component analysis, particularly around the catches. Contrary to the throws and the zeniths, and regardless of juggling speed, consecutive catches of the same hand showed a markedly negative lag-one serial correlation, suggesting that the catches are timed so as to preserve the temporal integrity of the juggling act.
TL;DR: It is found that spatial perception is geometrically inconsistent across these perceptual tasks, and it is plausible that motor behavior may be distorted in a way consistent with perceptual distortion.
Abstract: This paper considers interaction of the human arm with “virtual” objects simulated mechanically by a planar robot. Haptic perception of spatial properties of objects is distorted. It is reasonable to expect that it may be distorted in a geometrically consistent way. Three experiments were performed to quantify perceptual distortion of length, angle and orientation. We found that spatial perception is geometrically inconsistent across these perceptual tasks. Given that spatial perception is distorted, it is plausible that motor behavior may be distorted in a way consistent with perceptual distortion. In a fourth experiment, subjects were asked to draw circles. The results were geometrically inconsistent with those of the length perception experiment. Interestingly, although the results were inconsistent (statistically different), this difference was not strong (the relative distortion between the observed distributions was small). Some computational implications of this research for haptic perception and motor planning are discussed.
TL;DR: A novel framework for the analysis of time series from dynamical systems that alternate between different operating modes by using predictive models, which has a high temporal resolution and reveals previously unclassified details of the transitions.
Abstract: We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transition between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be useful for the analysis of nonstationary dynamical systems, which are abundant in medicine, chemistry, biology and engineering.
TL;DR: A simplified form of the frontal lobe architecture of cortico-basal ganglia-thalamo-cortical loops is analysed to determine the manner in which they can learn temporal sequences as part of working memory activity and how the temporal duration of activity can arise.
Abstract: We analyse a simplified form of the frontal lobe architecture of cortico-basal ganglia-thalamo-cortical loops to determine the manner in which they can learn temporal sequences as part of working memory activity. In particular, we consider how the temporal duration of activity can arise in this setting. We start from a hard-wired version in which temporally extended activity is created by the `long' loop of cortex → basal ganglia → thalamus → cortex, and show it arises from a near saddle-node bifurcation. The manner in which the transition between patterns occurs is also considered. This is then extended to analyse the temporal sequence storage and regeneration abilities of trained networks with a similar architecture. The temporal dynamics of this activity is also analysed. Implications of this for other working memory activities and for understanding the architecture of the frontal lobes are discussed in conclusion.
TL;DR: A general method for the analysis of the discharge trains of periodically forced noisy leaky integrate-and-fire neuron models that relies on the iterations of a stochastic phase transition operator that generalizes the phase transition function used for the study of periodically Forced deterministic oscillators to noisy systems.
Abstract: We present a general method for the analysis of the discharge trains of periodically forced noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a stochastic phase transition operator that generalizes the phase transition function used for the study of periodically forced deterministic oscillators to noisy systems. The kernel of this operator is defined in terms of the the first passage time probability density function of the Ornstein Uhlenbeck process through a suitable threshold. Numerically, it is computed as the solution of a singular integral equation. It is shown that, for the noisy system, quantities such as the phase distribution (cycle histogram), the interspike interval distribution, the autocorrelation function of the intervals, the autocorrelogram and the power spectrum density of the spike train, as well as the input–output cross-correlation and cross-spectral density can all be computed using the stochastic phase transition operator. A detailed description of the numerical implementation of the method, together with examples, is provided.
TL;DR: Changes in the alpha frequency band during perceptual reversal by using the Necker cube are analyzed and significantly increased delta power and decreased alpha power during the perceptual-reversal-related positivity are found.
Abstract: Since the first observation of perceptual reversal by Necker, many theoretical approaches have been proposed. In a previous study, we showed that a positive wave appeared approximately 250 ms prior to the button press of the subjects, indicating perceptual reversal during the observation of the Necker cube figure. A basic difficulty in this type of study is the possible jitter in the latency of the button press due to the variability of the subjects' reaction time during a recording session. To overcome this difficulty, a pattern selection method based on the wavelet transform was proposed in the previous study. A dominant positive wavelet coefficient in the delta band was found to represent the perceptual-reversal-related positivity. In the present study, we aim to analyze the changes in the alpha frequency band during perceptual reversal by using the Necker cube. The RMS values of the alpha frequency band were measured for two time periods: +/- 3 SD around the mean peak latency of the perceptual-reversal-related positivity and a time window of the same length before the positive wave. We found significantly increased delta power and decreased alpha power during the perceptual-reversal-related positivity.
TL;DR: Assessment of pattern stability for stationary performance and estimating the model parameters (a, b, and Q) for the stochastic extension of the Haken-Kelso-Bunz model, finding no statistically significant differences in stability were observed between the two coordination modes.
Abstract: Various stability features of bimanual rhythmic coordination, including phase transitions, have been modeled successfully by means of a one-dimensional equation of motion for relative phase obeying a gradient dynamics, the Haken-Kelso-Bunz model. The present study aimed at assessing pattern stability for stationary performance and estimating the model parameters (a, b, and Q) for the stochastic extension of this model. Estimates of a and b allowed for reconstruction of the potential defining the gradient dynamics. Two coordination patterns between the forearms (in-phase, anti-phase) were performed at seven different frequencies. Model parameters were estimated on the basis of an exponential decay parameter describing the relaxation behavior of continuous relative phase following a mechanical perturbation. Variability of relative phase and relaxation time provided measures of pattern stability. Although the predicted inverse relation between pattern stability and movement frequency was observed for the lower tempo conditions, it was absent for the higher tempos, reflecting the influence of task constraints. No statistically significant differences in stability were observed between the two coordination modes, indicating the influence of intention. The reconstructed potential reflected the observed stability features, underscoring the adequacy of the parameter estimations. The relaxation process could not be captured adequately by means of a simple exponential decay function but required an additional oscillatory term. In accordance with previous assumptions, noise strength Q did not vary as a function of movement frequency. However, systematic differences in Q were observed between the two coordination modes. The advantages and (potential) pitfalls of using stationary performance of single patterns to examine the stability features of a bistable potential were discussed.
TL;DR: The results suggest that there is a transient decrease of information transmission complexity when brain state changes occur suddenly, and hint that the methods used here might be an approach to observe quick processes in the living brain.
Abstract: Based on a complexity analysis of mutual information transmission of EEG developed by us [Xu J, Liu Z, Liu R, Yang Q (1997) Physica D 106: 363–374], dynamic processes of the complexity of mutual information transmission in human brains were studied. To diminish possible problems due to coarse graining preprocessing, some new measures of complexity were used. The results show that, just before and after generalized seizures, the complexities of almost all information transmission between different brain areas drop significantly; there is also a temporary decrease of complexity when subjects shift their attention. The above facts suggest that there is a transient decrease of information transmission complexity when brain state changes occur suddenly. Mental arithmetic tasks activate the left temporal lobe to exchange more information with other brain areas. The results hint that the methods used here might be an approach to observe quick processes in the living brain.
TL;DR: It is argued that a combination of the exponential decay model (or other models based on the mechano-chemistry of contraction) and structurally based models may be fruitful in explaining this time-dependent contraction behaviour.
Abstract: In recent years, it has been recognised that improvements to classic models of muscle mechanical behaviour are often necessary for properly modelling co-ordinated multi-joint actions. In this respect, the purpose of the present study was to improve on modelling stretch-induced force enhancement and shortening-induced force depression of muscle contraction. For this purpose, two models were used: a modified Hill model and a model based loosely on mechano-chemistry of the cross-bridge cycle (exponential decay model). The models were compared with a classic Hill model and experimental data. Parameter values were based, as much as possible, on experimental findings in the literature, and tested with new experiments on the gastrocnemius of the rat. Both models describe many features of slow-ramp movements well during short contractions (300–500 ms), but long-duration behaviour is described only partly. The exponential decay model does not incorporate a force–velocity curve. Therefore, its good performance indicates that the status of the classic force–velocity characteristic may have to be reconsidered. Like movement-induced force depression and enhancement, it seems a particular manifestation of time-dependent force behaviour of muscle, rather than a fundamental property of muscle (like the length–tension curve). It is argued that a combination of the exponential decay model (or other models based on the mechano-chemistry of contraction) and structurally based models may be fruitful in explaining this time-dependent contraction behaviour. Furthermore, not in the least because of its relative simplicity, the exponential decay model may prove more suitable for modelling multi-joint movements than the Hill model.
TL;DR: A new model of hippocampal place cells that allows the isolation of orienting from storing functions yet also shows how they can be connected may help to reconcile conflicting views on the function of the hippocampus.
Abstract: Arthropods as well as mammals are able to return straight home after a random search excursion under conditions that are designed to exclude all external cues. After a brief clarification of the terminology, two principal systems of information processing that can achieve this performance are introduced and analysed: Polar versus Cartesian path integration. The different demands and achievements of the two systems are confronted with neurophysiological findings on the functioning of the hippocampus, and with a recent comprehensive model of how the hippocampal place cells perform path integration. To connect the neurophysiological findings with the behavior of the animal, a new model is developed. It achieves three functionally diverse performances: maintenance and control of a compass direction, navigation by path integration, and formation of goals by connecting non-spatial features with their location. This is done by three interconnected feedback loops, set by a common reference variable. Their information-processing structure enables the animal not only to home but also to go straight from any stored goal to any other, without explicit representation of the distance between them, and without a topological arrangement of the store. The model explains behaviors not yet understood and predicts still undiscovered performances. Because it allows the isolation of orienting from storing functions yet also shows how they can be connected, the model may help to reconcile conflicting views on the function of the hippocampus.