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PubMed2000

Computational model of dot-pattern selective cells.

A computational model of a dot-pattern selective neuron is proposed. This type of neuron is found in the inferotemporal cortex of monkeys. It responds strongly to groups of dots and spots of light intensity variation but very weakly or not at all to single dots and spots that are not part of a pattern. This non-linear behaviour is quite different from the spatial frequency filtering behaviour exhibited by other neurons that react to spot-shaped stimuli, such as neurons with centre-surround receptive field profiles found in the lateral geniculate nuclei and layer 4Cbeta of V1. It is implemented in the proposed computational model by using an AND-type non-linearity to combine the responses of centre-surround cells. The proposed model is capable of explaining the results of neurophysiological experiments as well as certain psychophysical observations.

Animals↗
PubMed2000

Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings.

A lumped continuum model for electrocortical activity was used to simulate several established experimental findings of synchronous oscillation which have not all been previously embodied in a single explanatory model. Moving-bar visual stimuli of different extension, stimuli moving in different directions, the impact of non-specific cortical activation upon synchronous oscillation, and the frequency content of EEG associated with synchrony were considered. The magnitude of zero lag synchrony was primarily accounted for by the properties of the eigenmodes of the travelling local field potential superposition waves generated by inputs to the cortex, largely independent of the oscillation properties and associated spectral content. Approximation of the differences in cross-correlation observed with differently moving bar stimuli, and of the impact of cortical activation, required added assumptions on (a) spatial coherence of afferent volleys arising from parts of a single stimulus object and (b) the presence of low-amplitude diffuse field noise, with enhancement of cortical signal/noise ratio with respect to the spatially coherent inputs, at higher levels of cortical activation. Synchrony appears to be a ubiquitous property of cortex-like delay networks. Precision in the modelling of synchronous oscillation findings will require detailed description of input pathways, cortical connectivity, cortical stability, and aspects of cortical/subcortical interactions.

Artifacts↗
PubMed2000

A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model.

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.

Action Potentials↗
PubMed2000

Dynamic process of information transmission complexity in human brains.

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.

Adult↗
PubMed2000

Spinal motor control system incorporates an internal model of limb dynamics.

The existence and utilization of an internal representation of the controlled object is one of the most important features of the functioning of neural motor control systems. This study demonstrates that this property already exists at the level of the spinal motor control system (SMCS), which is capable of generating motor patterns for reflex rhythmic movements, such as locomotion and scratching, without the aid of the peripheral afferent feedback, but substantially modifies the generated activity in response to peripheral afferent stimuli. The SMCS is presented as an optimal control system whose optimality requires that it incorporate an internal model (IM) of the controlled object's dynamics. A novel functional mechanism for the integration of peripheral sensory signals with the corresponding predictive output from the IM, the summation of information precision (SIP) is proposed. In contrast to other models in which the correction of the internal representation of the controlled object's state is based on the calculation of a mismatch between the internal and external information sources, the SIP mechanism merges the information from these sources in order to optimize the precision of the controlled object's state estimate. It is demonstrated, based on scratching in decerebrate cats as an example of the spinal control of goal-directed movements, that the results of computer modeling agree with the experimental observations related to the SMCS's reactions to phasic and tonic peripheral afferent stimuli. It is also shown that the functional requirements imposed by the mathematical model of the SMCS comply with the current knowledge about the related properties of spinal neuronal circuitry. The crucial role of the spinal presynaptic inhibition mechanism in the neuronal implementation of SIP is elucidated. Important differences between the IM and a state predictor employed for compensating for a neural reflex time delay are discussed.

Animals↗
PubMed2000

Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals.

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.

Cybernetics↗
PubMed2000

How honeybees make grazing landings on flat surfaces.

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.

Animals↗
PubMed2000

Combined space-variant maps for optical-flow-based navigation.

A robot navigating in an unstructured environment needs to avoid obstacles in its way and determine free spaces through which it can safely pass. We present here a set of optical-flow-based behaviors that allow a robot moving on a ground plane to perform these tasks. The behaviors operate on a purposive representation of the environment called the "virtual corridor" which is computed as follows: the images captured by a forward-facing camera rigidly attached to the robot are first remapped using a space-variant transformation. Then, optical flow is computed from the remapped image stream. Finally, the virtual corridor is extracted from the optical flow by applying simple but robust statistics. The introduction of a space-variant image preprocessing stage is inspired by biological sensory processing, where the projection and remapping of a sensory input field onto higher-level cortical areas represents a central processing mechanism. Such transformations lead to a significant data reduction, making real-time execution possible. Additionally, they serve to "re-present" the sensory data in terms of ecologically relevant features, thereby simplifying the interpretation by subsequent processing stages. In accordance with these biological principles we have designed a space-variant image transformation, called the polar sector map, which is ideally suited to the navigational task. We have validated our design with simulations in synthetic environments and in experiments with real robots.

Animals↗
PubMed2000

The turn-back-and-look behaviour: bee versus robot.

Honeybees and social wasps departing from a novel food source perform stereotype flight manoeuvres, termed the turn-back-and-look behaviour (TBL). Based on results of behavioural studies, it is proposed that the image motion generated by the TBL provides the insect with information about the three-dimensional structure of the goal's surroundings, thus enabling it to select reliable landmarks that will guide it to the goal upon return. The colour, shape, and size of landmarks, on the other hand, are learned mainly during arrival at the food source. However, when bees are prevented from learning these cues on arrival, they learn them during the TBL, despite the fact that this performance does not require the use of image motion. A recently developed model shows that landmark learning can indeed be accomplished during the TBL by exploiting cues others than image motion. A mobile robot equipped with the appropriate software selects, during the TBL, reliable marks and returns to the site of departure from different locations by accomplishing image matching along a two-dimensional vector field.

Animals↗
PubMed2000

How do insects use path integration for their navigation?

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.

Animals↗
PubMed2001

Analysis of kinematically redundant reaching movements using the equilibrium-point hypothesis.

Six subjects performed a planar reaching arm movement to a target while unpredictable perturbations were applied to the endpoint; the perturbations consisted of pulling springs having different stiffness. Two conditions were applied; in the first, subjects had to reach for the target despite the perturbation, in the second condition, the subjects were asked to not correct the motion as a perturbation was applied. We analyzed the kinematics profiles of the three arm segments and, by means of inverse dynamics, calculated the joint torques. The framework of the equilibrium-point (EP) hypothesis, the lambda model, allowed the reconstruction of the control variables, the "equilibrium trajectories", in the "do not correct" condition for the wrist and the elbow joints as well as for the end point final position, while for the other condition, the reconstruction was less reliable. The findings support and extend to a multiple-joint planar movement, the paradigm of the EP hypothesis along with the "do not correct" instruction.

Adult↗
PubMed2001

Frequency sensitivity in Hodgkin-Huxley systems.

The frequency sensitivity of weak periodic signal detection has been studied via numerical simulations for both a single neuron and a neuronal network. The dependence of the critical amplitude of the signal upon its frequency and a resonance between the intrinsic oscillations of a neuron and the signal could account for the frequency sensitivity. In the presence of both a subthreshold periodic signal and noise, the signal-to-noise ratio (SNR) of the output of either a single neuron or a neuronal network present the typical characteristics of stochastic resonance. In particular, there exists a frequency-sensitive range of 30-100 Hz, and for signals with frequencies within this range the SNRs have large values. This implies that the system under consideration (a single neuron or a neuronal network) is more sensitive to the detection of periodic signals, and the frequency sensitivity may be of a functional significance to signal processing.

Artifacts↗
PubMed2001

Reliable short-term memory in the trion model: toward a cortical language and grammar.

It has previously been shown that Hebb learning in a single column in the trion model of cortical organization occurs by selection. Motivated by von Neumann's solution for obtaining reliability and by models of circulating cortical activity, we introduce Hebb intercolumnar couplings to achieve dramatic enhancements in reliability in the firing of connected columns. In order for these enhancements to occur, specific temporal phase differences must exist between the same inherent spatial-temporal memory patterns in connected columns. We then generalize the criteria of large enhancements in the global firing of the entire connected columnar network to investigate the case when different inherent memory patterns are in the columns. The spatial rotations as well as the temporal phases now are crucial. Only certain combinations of inherent memory patterns meet these criteria with the symmetry properties playing a major role. The columnar order of these memory patterns not in the same symmetry family can be extremely important. This yields the first higher-level architecture of a cortical language and grammar within the trion model. The implications of this result with regard to an innate human language and grammar are discussed.

Cerebral Cortex↗
PubMed2001

Principles underlying mammalian neocortical scaling.

The neocortex undergoes a complex transformation from mouse to whale. Whereas synapse density remains the same, neuron density decreases as a function of gray matter volume to the power of around -1/3, total convoluted surface area increases as a function of gray matter volume to the power of around 8/9, and white matter volume disproportionately increases as a function of gray matter volume to the power of around 4/3. These phylogenetic scaling relationships (including others such as neuron number, neocortex thickness, soma radius, and number of cortical areas) are clues to understanding the principles driving neocortex organization, but there is currently no theory that can explain why these neocortical quantities scale as they do. Here I present a two-part model that explains these neocortical allometric scaling laws. The first part of the model is a special case of the physico-mathematical model recently put forward to explain the quarter power scaling laws in biology. It states that the neocortex is a space-filling neural network through which materials are efficiently transported, and that synapse sizes do not vary as a function of gray matter volume. The second part of the model states that the neocortex is economically organized into functionally specialized areas whose extent of area-interconnectedness does not vary as a function of gray matter volume. The model predicts, among other things, that the number of areas and the soma radius increase as a function of gray matter volume to the power of 1/3 and 1/9, respectively, and empirical support is demonstrated for each. Also, the scaling relationships imply that, although the percentage of the total number of neurons to which a neuron connects falls as a function of gray matter volume with exponent -1/3, the network diameter of the neocortex is invariant at around two. Finally, I discuss how a similar approach may have promise in explaining the scaling relationships for the brain and other organs as a function of body mass.

Animals↗
PubMed2001

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike-occurrence time after stimulus onset must capture at least the first, second and third moments of the temporal distribution of error signals.

Action Potentials↗
PubMed2001

A muscle activation model of variable stimulation frequency response and stimulation history, based on positive feedback in calcium dynamics.

Muscle fiber response to a train of variable-frequency pulses includes the potentiation and catch-like effect. For better understanding of these phenomena, we built an activation model with emphasis on the calcium liberation from and re-sequestration into the sarcoplasmic reticulum, including calcium-induced calcium release. The model had two stable equilibrium points in the calcium concentration. Changes from the low to the high equilibrium point could be produced by high-frequency trains of pulses and would account for the potentiation. The model also showed a catch-like effect, as a long-lasting increment of muscle force after the application of a single extra pulse. The increase in force appeared in resting muscle, disappeared when the muscle was potentiated, and reappeared briefly if the stimulation was continued for long periods.

Action Potentials↗
PubMed2001

Mechanisms to synchronize neuronal activity.

Temporal aspects of neuronal activity have received increasing attention in recent years. Oscillatory dynamics and the synchronization of neuronal activity are hypothesized to be of functional relevance to information processing in the brain. Here we review theoretical studies of single neurons at different levels of abstraction, with an emphasis on the implications for properties of networks composed of such units. We then discuss the influence of different types of couplings and choices of parameters to the existence of a stable state of synchronous or oscillatory activity. Finally we relate these theoretical studies to the available experimental data, and suggest future lines of research.

Cortical Synchronization↗