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PubMed2004

A dynamical neural network for hitting an approaching object.

Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.

Biomechanical Phenomena↗

PubMed2005

Dynamic neural network models of the premotoneuronal circuitry controlling wrist movements in primates.

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations. The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

Animals↗

PubMed2005

Neural network dynamics.

Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.

Action Potentials↗

PubMed1999

Hypothesized neural dynamics of working memory: several chunks might be marked simultaneously by harmonic frequencies within an octave band of brain waves.

The capacity of working memory (WM) for up to about seven simple items holds true both for humans and other species, and may depend upon a common characteristic of mammalian brains. This paper develops the conjecture that each WM item is represented by a different brain wave frequency. The binding-by-synchrony hypothesis, now being widely investigated, holds that the attributes of a single cognitive element cohere because electroencephalogram (EEG) synchrony temporarily unifies their substrates, which are distributed among different brain regions. However, thought requires keeping active more than one cognitive element, or WM "chunk," at a time. If there is indeed a brain wave frequency code for cognitive item-representations that are copresent within the same volume of neural tissue, the simple mathematical relationships of harmonies could provide a basis for maintaining distinctness and for orderly changes. Thus, a basic aspect of music may provide a model for an essential characteristic of WM. Music is a communicative phenomenon of "intermediate complexity," more highly organized than the firing patterns of individual neurons but simpler than language. If there is a distinct level of neural processing within which the microscopic physiological activity of neurons self-organizes into the macroscopic psychology of the organism, it might require such moderate complexity. Some of the obvious properties of music--orderly mixing and transitions among limited numbers of signal lines-are suggestive of properties that a dynamic neural process might need in order to organize and reorganize WM markers, but there are a number of additional, nonobvious advantageous properties of summating sinusoids in music-like relationships. In particular, harmonies register a stable periodic signal in the briefest possible time. Thus, the regularity of summating sinusoids whose frequencies bear harmony ratios suggests a particular kind of tradeoff between parallel and serial processing. When there are few copresent waves, at EEG frequencies, this sort of parallel coding retains behaviorally meaningful brief periods. A necessary companion hypothesis is that the brain wave frequencies underlying WM are confined to a single octave; that is, the upper and lower bounds of the band are in the ratio of 2:1. This hypothesized restriction, suggested by an empirical property of EEG bands that has been widely reported but rarely commented upon, has the important property of precluding spurious difference rhythms. A restriction to an octave, of "harmonious" frequency-markers for WM items, also seems consistent with a great deal of behavioral data suggesting that WM comprises a rapidly fading trace process in which only up to three or four item-representations are strongly activated simultaneously. There is also an additional, sequential renewal-or-revision process, within which up to another three or four items are being actively refreshed by rehearsal or replaced. Such serial processing may involve a less stringent octave band crowding problem.

Animals↗

PubMed1997

Abstract genetic representation of dynamical neural networks using Kauffman networks.

Abstract (developmental) genetic representations are schemes where each genotype encodes a program for the construction of a phenotype. A new method for contriving abstract genetic representations is presented, based upon Kauffman's ideas regarding biological development [13-16]. Phenogenesis is controlled by genotype via cell replication and differentiation. Comparison is made with the earlier published methods of Gruau [9] and Kitano [18]. It is argued that greater expressive power is obtained using Kauffman networks. The new method was tested in the artificial evolution of morphology and finally successfully applied to the synthesis of structure in dynamical neural networks.

Evolution, Molecular↗

PubMedDate not supplied

Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing.

Activity in the mammalian thalamocortical system is often accompanied by a synchronous discharge of cortical and thalamic neurons. Although many functions have been attributed to such synchronous firing, it is not known whether or how synchrony of firing per se affects thalamocortical operations. Direct experimental tests of the consequences of neuronal synchronization in vivo are hard to carry out, whereas theoretical studies based on single-neuron models cannot reveal the effects of synchrony at the system level. To overcome these limitations, we have used a perturbational approach to test the causal efficacy of synchrony per se in large-scale simulations of the thalamocortical system. The test consists of selectively disrupting firing synchrony by 'jittering' the timing of action potentials in the simulations and determining whether firing rates are modified by this perturbation. The simulations are based in detail on the known anatomy and physiology of the thalamocortical-visual system of the cat, and have been shown in a companion paper to produce episodes of fast synchronous activity at multiple levels. By carrying out the perturbation analysis, we established that neurons can have long membrane time constants (8-16 ms) and balanced synaptic activations, and yet function collectively in such a way that synchrony within a time window of 4 ms significantly affects the rates and selectivity of the responses to visual stimuli. The simulations also revealed a complex interplay, at the network level, between synchrony of firing and rate of firing. The dynamic consequences of firing synchrony were most evident when spike jittering was applied to specific polysynaptic loops involving corticocortical and corticothalamic connections. These results support the view that firing synchrony within thalamocortical and corticocortical loops plays a causal role in the cooperative and competitive neural interactions that produce pattern-selective responses in the cortex.

Action Potentials↗

PubMed1996

A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements.

We propose a new approach based on dynamic recurrent neural networks (DRNN) to identify, in human, the relationship between the muscle electromyographic (EMG) activity and the arm kinematics during the drawing of the figure eight using an extended arm. After learning, the DRNN simulations showed the efficiency of the model. We demonstrated its generalization ability to draw unlearned movements. We developed a test of its physiological plausibility by computing the error velocity vectors when small artificial lesions in the EMG signals were created. These lesion experiments demonstrated that the DRNN has identified the preferential direction of the physiological action of the studied muscles. The network also identified neural constraints such as the covariation between geometrical and kinematics parameters of the movement. This suggests that the information of raw EMG signals is largely representative of the kinematics stored in the central motor pattern. Moreover, the DRNN approach will allow one to dissociate the feedforward command (central motor pattern) and the feedback effects from muscles, skin and joints.

Adult↗

PubMed2006

[Stochastic nonlinear evolutional model of the large-scaled neuronal population and dynamic neural coding subject to stimulation].

In this paper, we investigate how the population of neuronal oscillators deals with information, and analyze the dynamic evolution of neural coding when the outer stimulation acts on it on the base of our former work. By numerically computing for the model, we obtain the figure of average number density, which is used to describe the action potential of the neurons within population in three-dimensional space, namely the dynamic evolution of neural coding. The result of numerical analysis indicates that the model in this paper can be used to describe the evolutional process of abundant mutual interactional neurons acted by outer stimulation. The numerical result also proves that only the suitable stimulation can change the coupling structure of neurons. And the evolution model given in this paper incarnates the neural plasticity.

Action Potentials↗

PubMed1998

Neural dynamics of motion processing and speed discrimination.

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-turned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1-->MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.

Form Perception↗

PubMed2025

Learning neural dynamics through instructive signals.

Rapid learning is essential for flexible behavior, but its basis in the brain remains unknown. Here we introduce the PRISM plasticity rule, a unifying mechanistic model of three well-established, fast-acting synaptic plasticity rules-in hippocampus, cerebellum and mushroom body-which relies exclusively on pre-synaptic activity and an "instructive signal" from another brain area. Using a multi-region network model we show that guiding PRISM plasticity with instructive signals enables the network to quickly learn extremely flexible nonlinear dynamics underlying behaviorally relevant computations, as well as to emulate unknown external system dynamics from real-time error signals, which we demonstrate with comprehensive simulations supported by exact mathematical theory. Thus, PRISM plasticity guided by instructive signals is well-suited to rapidly learn general-purpose neural computations-in contrast to canonical Hebbian rules. Finally, we show how including this plasticity rule in artificial learning algorithms can solve long-range temporal credit assignment, a long-standing challenge in machine learning.

cerebellum↗

PubMed1999

Anaerobic threshold measurement using dynamic neural network models.

This paper deals with the subject of anaerobic threshold measurements for athletes involved in aerobic or aerobic/anaerobic sports. Traditionally, anaerobic threshold has been determined using invasive tests or using a non-invasive technique using steady-state heart-rate/work rate data. Non-invasive tests have the advantage of not requiring specialised equipment, but the acquisition of steady-state information can be problematic. This paper demonstrates how dynamical data can be used to accurately determine the steady-state heart-rate/work-rate curve (SSHW curve) using neural network dynamic models.

Anaerobic Threshold↗

PubMed2003

Functional nodes in dynamic neural networks for bioprocess modelling.

This contribution presents a novel method for the direct integration of a-priori knowledge in a neural network and its application for the online determination of a secondary metabolite during industrial yeast fermentation. Hereby, existing system knowledge is integrated in an artificial neural network (ANN) by means of 'functional nodes'. A generalized backpropagation algorithm is presented. For illustration, a set of ordinary differential equations describing the diacetyl formation and degradation during the cultivation is incorporated in a functional node and integrated in a dynamic feedforward neural network in a hybrid manner. The results show that a hybrid modelling approach exploiting available a-priori knowledge and experimental data can considerably outperform a pure data-based modelling approach with respect to robustness, generalization and necessary amount of training data. The number of training sets were decreased by 50%, obtaining the same accuracy as in a conventional approach. All incorrect decisions, according to defined cost criteria obtained with the conventional ANN, were avoided.

Journal Article↗

PubMed1997

Neural dynamics of variable-rate speech categorization.

What is the neural representation of a speech code as it evolves in time? A neural model simulates data concerning segregation and integration of phonetic percepts. Hearing two phonetically related stops in a VC-CV pair (V = vowel; C = consonant) requires 150 ms more closure time than hearing two phonetically different stops in a VC1-C2V pair. Closure time also varies with long-term stimulus rate. The model simulates rate-dependent category boundaries that emerge from feedback interactions between a working memory for short-term storage of phonetic items and a list categorization network for grouping sequences of items. The conscious speech code is a resonant wave. It emerges after bottom-up signals from the working memory select list chunks which read out top-down expectations that amplify and focus attention on consistent working memory items. In VC1-C2V pairs, resonance is reset by mismatch of C2 with the C1 expectation. In VC-CV pairs, resonance prolongs a repeated C.

Computer Simulation↗

PubMedDate not supplied

Neural dynamics of psychotherapy: what modeling might tell us about us.

A neural network theory is proposed for some of the effects of verbal psychotherapy in individuals who are not seriously disturbed but seeking to function more effectively. The network theories are built on a combination of the supervised ARTMAP network and competitive attractor dynamics. The modeling exercise leads to some guidelines for psychotherapists that involve both cognitive and emotional reinforcement in a climate closer to skill learning than to medical treatment.

Humans↗

PubMed1993

Neural dynamics of motion perception: direction fields, apertures, and resonant grouping.

A neural network model of global motion segmentation by visual cortex is described. Called the motion boundary contour system (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively reorganized into a coherent global motion signal. Unlike many previous researchers, we analyze how a coherent motion signal is imparted to all regions of a moving figure, not only to regions at which unambiguous motion signals exist. The model hereby suggests a solution to the global aperture problem. The motion BCS describes how preprocessing of motion signals by a motion oriented contrast (MOC) filter is joined to long-range cooperative grouping mechanisms in a motion cooperative-competitive (MOCC) loop to control phenomena such as motion capture. The motion BCS is computed in parallel with the static BCS of Grossberg and Mingolla (1985a, 1985b, 1987). Homologous properties of the motion BCS and the static BCS, specialized to process motion directions and static orientations, respectively, support a unified explanation of many data about static form perception and motion form perception that have heretofore been unexplained or treated separately. Predictions about microscopic computational differences of the parallel cortical streams V1-->MT and V1-->V2-->MT are made--notably, the magnocellular thick stripe and parvocellular interstripe streams. It is shown how the motion BCS can compute motion directions that may be synthesized from multiple orientations with opposite directions of contrast. Interactions of model simple cells, complex cells, hyper-complex cells, and bipole cells are described, with special emphasis given to new functional roles in direction disambiguation for endstopping at multiple processing stages and to the dynamic interplay of spatially short-range and long-range interactions.

Animals↗

PubMed2001

Neural dynamics of motion integration and segmentation within and across apertures.

A neural model is developed of how motion integration and segmentation processes, both within and across apertures, compute global motion percepts. Figure-ground properties, such as occlusion, influence which motion signals determine the percept. For visible apertures, a line's terminators do not specify true line motion. For invisible apertures, a line's intrinsic terminators create veridical feature-tracking signals. Sparse feature-tracking signals can be amplified before they propagate across position and are integrated with ambiguous motion signals within line interiors. This integration process determines the global percept. It is the result of several processing stages: directional transient cells respond to image transients and input to a directional short-range filter that selectively boosts feature-tracking signals with the help of competitive signals. Then, a long-range filter inputs to directional cells that pool signals over multiple orientations, opposite contrast polarities, and depths. This all happens no later than cortical area MT. The directional cells activate a directional grouping network, proposed to occur within cortical area MST, within which directions compete to determine a local winner. Enhanced feature-tracking signals typically win over ambiguous motion signals. Model MST cells that encode the winning direction feed back to model MT cells, where they boost directionally consistent cell activities and suppress inconsistent activities over the spatial region to which they project. This feedback accomplishes directional and depthful motion capture within that region. Model simulations include the barberpole illusion, motion capture, the spotted barberpole, the triple barberpole, the occluded translating square illusion, motion transparency and the chopsticks illusion. Qualitative explanations of illusory contours from translating terminators and plaid adaptation are also given.

Computer Simulation↗

PubMed2005

Dynamic neural field with local inhibition.

A lateral-inhibition type neural field model with restricted connections is presented here and represents an experimental extension of the continuum neural field theory (CNFT) by suppression of the global inhibition. A modified CNFT equation is introduced and allows for a locally defined inhibition to spatially expand within the network and results in a global competition extending far beyond the range of local connections by virtue of diffusion of inhibition. The resulting model is able to attend to a moving stimulus in the presence of a very high level of noise, several distractors or a mixture of both.

Models, Neurological↗

PubMed1996

The importance of noise for segmentation and binding in dynamical neural systems.

Segmentation and binding are cognitive operations that underlie the process of perception. They can be understood as taking place in the temporal domain. In models of nonlinear oscillatory neurons and neuronal cell assemblies, we represent binding by phase locking of assemblies in different networks, and segmentation by phase separation of assemblies in the same network, leading to waveforms of staggered oscillations. Both processes can be facilitated if the inputs to the system, representing simultaneously activated memories, possess noisy components. In the binding problem they serve as a tagging device, driving phase locking between assemblies that belong to different networks but carry the same inputs. In the segmentation problem they allow us to overcome an inherent limitation on segmentation that, otherwise, cannot accommodate more than a few commonly excited memories.

Artifacts↗