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At least 109 records · Page 6Linked to original sources
PubMed2006

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model.

Early human motor development has the nature of spontaneous exploration and boot-strap learning, leading to open-ended acquisition of versatile flexible motor skills. Since dexterous motor skills often exploit body-environment dynamics, we formulate the developmental principle as the spontaneous exploration of consistent dynamical patterns of the neural-body-environment system. We propose that partially ordered dynamical patterns emergent from chaotic oscillators coupled through embodiment serve as the core driving mechanism of such exploration. A model of neuro-musculo-skeletal system is constructed capturing essential features of biological systems. It consists of a skeleton, muscles, spindles, tendon organs, spinal circuits, medullar circuits (CPGs), and a basic cortical model. Through a series of experiments with a minimally simple body model, it is shown that the model has the capability of generating partially ordered behavior, a mixture of chaotic exploration and ordered entrained patterns. Models of self-organizing cortical areas for primary somatosensory and motor areas are introduced. They participate in the explorative learning by simultaneously learning and controlling the movement patterns. A scaled up version of the model, a human infant model, is constructed and put through preliminary experiments. Some meaningful motor behavior emerged including rolling over and crawling-like motion. The results show the possibility that a rich variety of meaningful behavior can be discovered and acquired by the neural-body dynamics without pre-defined coordinated control circuits.

Behavior↗
PubMed2006

Singing-related activity of identified HVC neurons in the zebra finch.

High vocal center (HVC) is part of the premotor pathway necessary for song production and is also a primary source of input to the anterior forebrain pathway (AFP), a basal ganglia-related circuit essential for vocal learning. We have examined the activity of identified HVC neurons of zebra finches during singing. Antidromic activation was used to identify three classes of HVC cells: neurons projecting to the premotor nucleus RA, neurons projecting to area X in the AFP, and putative HVC interneurons. HVC interneurons are active throughout the song and display tonic patterns of activity. Projection neurons exhibit highly phasic stereotyped firing patterns. X-projecting (HVC((X))) neurons burst zero to four times per motif, whereas RA-projecting neurons burst extremely sparsely--at most once per motif. The bursts of HVC projection neurons are tightly locked to the song and typically have a jitter of <1 ms. Population activity of interneurons, but not projection neurons, was significantly correlated with syllable patterns. Consistent with the idea that HVC codes for the temporal order in the song rather than for sound, the vocal dynamics and neural dynamics in HVC occur on different and uncorrelated time scales. We test whether HVC((X)) neurons are auditory sensitive during singing. We recorded the activity of these neurons in juvenile birds during singing and found that firing patterns of these neurons are not altered by distorted auditory feedback, which is known to disrupt learning or to cause degradation of song already learned.

Acoustic Stimulation↗
PubMed1994

Role of chaotic dynamics in neural plasticity.

Mathematical models are essential for the study of complex neural systems at all levels of the hierarchy from macromolecules through neurons to brain systems. ANN are readily available, but most of them are inappropriate for modeling brain function in normal behavior, because they stem from studies of neural systems in anesthetized or paralyzed animals which are capable only of reflux output. That class of models lacks the goal-directed, self-organizing properties of neural systems in behaving animals. In contrast to the stability of ANN and their reliance on asymptotic convergence to steady states (point attractors) and periodic oscillations (limit cycle attractors), BNN are intrinsically unstable. They continuously generate 'spontaneous' aperiodic activity that manifests the operation of chaotic dynamics undergoing repeated state transitions. Observations on the activity patterns of sensory cortex reveal that the perceptual outputs of BNN are by construction of spatial patterns and dynamic trajectories and not by computation using symbolic representations. Chaotic dynamics plays essential roles both in the construction of perceptions and in the continuing update of cortical populations, which requires selective synaptic modification during associative learning and habituation. Simultaneous multichannel recording from the olfactory bulb and cortex has given the following experimental results. (1) The cortical activity that relates to the perception of a sensory stimulus is carried macroscopically by a smaller number of single neurons e.g. 'units', 'feature detectors'. (2) The macroscopic activity reflects the meaning and significance of the stimulus for the experimental subject and not the stimulus as it is known to the observer. (3) The activity carries the meaning in spatial patterns, not in time series (the difference between a phonograph or radio and movie or TV). (4) The spatial patterns of activity that accompany previously learned stimuli or responses are changed by the introduction of new stimuli and also by modifications in reinforcement contingencies. There is no invariance in the memory store within the populations. (5) The patterns of activity are created by dynamic neural interactions in sensory cortex, not by registration or filtering of stimuli. There is no evidence for storage, retrieval, cross-correlation or logical tree search. (6) The dynamics is chaotic, not merely noisy, so that each act of perception involves a new construction by the cortex not by mere information processing. From these findings we infer that chaotic dynamics plays a crucial role in the construction of the associational contexts comprising the memory systems of experimental subjects.

Animals↗
PubMed2004

Gap junctions: their importance for the dynamics of neural circuits.

Electrical coupling through gap junctions constitutes a mode of signal transmission between neurons (electrical synaptic transmission). Originally discovered in invertebrates and in lower vertebrates, electrical synapses have recently been reported in immature and adult mammalian nervous systems. This has renewed the interest in understanding the role of electrical synapses in neural circuit function and signal processing. The present review focuses on the role of gap junctions in shaping the dynamics of neural networks by forming electrical synapses between neurons. Electrical synapses have been shown to be important elements in coincidence detection mechanisms and they can produce complex input-output functions when arranged in combination with chemical synapses. We postulate that these synapses may also be important in redefining neuronal compartments, associating anatomically distinct cellular structures into functional units. The original view of electrical synapses as static connecting elements in neural circuits has been revised and a considerable amount of evidence suggests that electrical synapses substantially affect the dynamics of neural circuits.

Animals↗
PubMed2005

Ndn, volume transmission, and self-organization in brain dynamics.

Fields of neural activity are seen in synchronized oscillations that are detected at mesoscopic scales in syntheses of multicellular recordings of action potentials and electroencephalograms (EEGs) over broad areas of cerebral cortex. The waves often have large-scale, highly textured spatial patterns of cortical activity, formed in the context of associative learning under classical and operant conditioning in rabbits. The patterns show spatial amplitude modulation of shared oscillations of carrier waves in the beta and gamma ranges of the EEG, with recurrence at frame rates in the alpha and theta ranges. The frames also show spatial phase modulation that is inconsistent with driving of the oscillations by focal pacemakers. The hypothesis is developed that the synchronization manifests continuous distributions of activity in cortical neuropil that modulate firings of selected neural networks embedded in the neuropil. Five interactive agencies have been postulated to explain the mechanism for the field synchrony: electric fields; magnetic fields; electromagnetic fields (radio waves); diffusion chemical gradients; and order parameters that control self-organization of large populations of neurons by widespread synaptic interaction constituting negative and positive feedback. Only the last interactive agency fits the data. The points are emphasized that these field patterns in frames require interactive neural dynamics that is modulated in respect to global operations mediating arousal, attention, selective emotional stance, wake, sleep, learn, habituate, dishabituate, etc., and that these operations require differing but complementary fields that form by massive parallel feed-forward architectures of brainstem neuromodulatory nuclei. An example is given using histamine of the neural discharges of brainstem nuclei that do not require fine spatiotemporal texturing of their firing; they operate by nonsynaptic release of neuromodulators that effect changes in background state, such that textured patterns of cortical activity can form and update in flexible adaptations of brains to their environments. These systems instantiate volume transmission by nonsynaptic diffusion transmission, in concert with the self-organization of the textured neural activity that supports cognition.

Animals↗
PubMed1996

A new type of self-organization associated with chaotic dynamics in neural networks.

A new type of self-organized dynamics is presented, in relation with chaos in neural networks. One is chaotic itinerancy and the other is chaos-driven contraction dynamics. The former is addressed as a universal behavior in high-dimensional dynamical systems. In particular, it can be viewed as one possible form of memory dynamics in brain. The latter gives rise to singular-continuous nowhere-differentiable attractors. These dynamics can be related to each other in the context of dimensionality and of chaotic information processings. Possible roles of these complex dynamics in brain are also discussed.

Animals↗
PubMed2004

Classification of mammographic masses using generalized dynamic fuzzy neural networks.

In this article, computer-aided classification of mammographic masses using generalized dynamic fuzzy neural networks (GDFNN) is presented. The texture parameters, derived from first-order gradient distribution and gray-level co-occurrence matrices, were computed from the regions of interest. A total of 343 images containing 180 benign masses and 163 malignant masses from the Digital Database for Screening Mammography were analyzed. A fast approach of automatically generating fuzzy rules from training samples was implemented to classify tumors. This work is novel in that it alleviates the problem of requiring a designer to examine all the input-output relationships of a training database in order to obtain the most appropriate structure for the classifier in a conventional computer-aided diagnosis. In this approach, not only the connection weights can be adjusted, but also the structure can be self-adaptive during the learning process. By virtue of the automatic generation of the classifier by the GDFNN learning algorithm, the area under the receiver-operating characteristic curve, Az, attains 0.868 +/- 0.020, which corresponds to a true-positive fraction of 95.0% at a false positive fraction of 52.8%. The corresponding accuracy is 70.0%, the positive predictive value is 62.0%, and the negative predictive value is 91.4%.

Adult↗
PubMed2026

Histone H3K9 methyltransferases regulate cortical growth by coordinating heterochromatin formation and neural progenitor dynamics.

DNA packaging into heterochromatin is a fundamental mechanism of transcriptional silencing, yet its role in regulating neural progenitor behavior during brain development remains poorly understood. Trimethylation of histone H3 lysine 9 (H3K9me3), catalyzed by the methyltransferases SETDB1, SUV39H1, and SUV39H2, is a defining feature of heterochromatin, but functional redundancy among these enzymes has obscured their developmental roles. Here, we generated a cortex-specific triple knockout mouse model lacking Setdb1, Suv39h1, and Suv39h2 to directly interrogate H3K9me3 function during corticogenesis. Combined loss of H3K9 methyltransferases caused genome-wide depletion of H3K9me3, disruption of neural progenitor cell-cycle progression, and impaired cortical neurogenesis, resulting in microcephaly. H3K9 methyltransferases preserve neural progenitor identity and function by silencing clustered protocadherins, meiosis-associated genes, and a cell-cycle restraint program through H3K9me3 deposition. Loss of H3K9me3 promoted local chromatin opening and increased transcription factor occupancy, enabling transposable elements to acquire cryptic enhancer activity and modulate proximal gene expression. Together, these findings establish H3K9me3 heterochromatin as an active regulator of neural progenitor dynamics and lineage fidelity, revealing a central epigenetic mechanism that restricts aberrant transcriptional programs to ensure cortical growth.

Cerebral Cortex↗
PubMed1999

Short-term memory in olfactory network dynamics.

Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.

Animals↗
PubMed2006

A few important points about dopamine's role in neural network dynamics.

Dopamine modifies a large variety of cellular and synaptic biophysical parameters in a seemingly paradoxical manner. Predicting their implications for neural network dynamics and computation seems difficult. Here I briefly review experimental and simulation data which illustrate that the diverse ionic consequences may combine such that dopamine's overall effect strongly depends on the activity state of the postsynaptic network, potentially with a temporal profile. These properties could be crucial for understanding dopamine's functional role.

Brain↗
PubMed2001

Macroscopic dynamics in separable neural networks.

Parallel dynamics of neural networks with separable coupling is given starting from Coolen-Sherrington theory. Away from saturation, it is shown that this parallel retrieve dynamics is equivalent to the sequential dynamics for finite temperature. But the finite-size effects were found to be governed by a homogeneous Markov process, not by a time-dependent Ornstein-Uhlenbeck process in sequential dynamics.

Biophysics↗
PubMed2002

Sufficient conditions for error backflow convergence in dynamical recurrent neural networks.

This article extends previous analysis of the gradient decay to a class of discrete-time fully recurrent networks, called dynamical recurrent neural networks, obtained by modeling synapses as finite impulse response (FIR) filters instead of multiplicative scalars. Using elementary matrix manipulations, we provide an upper bound on the norm of the weight matrix, ensuring that the gradient vector, when propagated in a reverse manner in time through the error-propagation network, decays exponentially to zero. This bound applies to all recurrent FIR architecture proposals, as well as fixed-point recurrent networks, regardless of delay and connectivity. In addition, we show that the computational overhead of the learning algorithm can be reduced drastically by taking advantage of the exponential decay of the gradient.

Journal Article↗
PubMed1998

Learning persistent dynamics with neural networks.

Persistence is one of the most common characteristics of real-world time series. In this work we investigate the process of learning persistent dynamics by neural networks. We show that for chaotic times series the network can get stuck for long training periods in a trivial minimum of the error function related to the long-term autocorrelation in the series. Remarkably, in these cases the transition to the trained phase is quite abrupt. For noisy dynamics the training process is smooth. We also consider the effectiveness of two of the most frequently used decorrelation methods in avoiding the problems related to persistence. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMed2003

A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion.

This paper describes the use of a dynamic recurrent neural network (DRNN) for simulating lower limb coordination in human locomotion. The method is based on mapping between the electromyographic signals (EMG) from six muscles and the elevation angles of the three main lower limb segments (thigh, shank and foot). The DRNN is a fully connected network of 35 hidden units taking into account the temporal relationships history between EMG and lower limb kinematics. Each EMG signal is sent to all 35 units, which converge to three outputs. Each output neurone provides the kinematics of one lower limb segment. The training is supervised, involving learning rule adaptations of synaptic weights and time constant of each unit. Kinematics of the locomotor movements were recorded and analysed using the opto-electronic ELITE system. Comparative analysis of the learning performance with different types of output (position, velocity and acceleration) showed that for common gait mapping velocity data should be used as output, as it is the best compromise between asymptotic error curve, rapid convergence and avoidance of bifurcation. Reproducibility of the identification process and biological plausibility were high, indicating that the DRNN may be used for understanding functional relationships between multiple EMG and locomotion. The DRNN might also be of benefit for prosthetic control.

Adult↗
PubMed2000

Multi-step-ahead prediction using dynamic recurrent neural networks.

A method for the development of empirical predictive models for complex processes is presented. The models are capable of performing accurate multi-step-ahead (MS) predictions, while maintaining acceptable single-step-ahead (SS) prediction accuracy. Such predictors find applications in model predictive controllers and in fault diagnosis systems. The proposed method makes use of dynamic recurrent neural networks in the form of a nonlinear infinite impulse response (IIR) filter. A learning algorithm is presented, which is based on a dynamic gradient descent approach. The effectiveness of the method for accurate MS prediction is tested on an artificial problem and on a complex, open-loop unstable process. Comparative results are presented with polynomial Nonlinear AutoRegressive with eXogeneous (NARX) predictors, and with recurrent networks trained using teacher forcing. Validation studies indicate that excellent generalization is obtained for the range of operational dynamics studied. The research demonstrates that the proposed network architecture and the associated learning algorithm are quite effective in modeling the dynamics of complex processes and performing accurate MS predictions.

Algorithms↗
PubMed2005

A dynamic fuzzy neural filter for separation of discontinuous adventitious sounds from vesicular sounds.

This paper presents a recurrent filter that performs real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two Dynamic Fuzzy Neural Networks, operating in parallel, to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. Extensive experimental results, including fine/coarse crackles and squawks, are given, and a performance comparison with a series of other models is conducted, underlining the separation capabilities of the proposed filter and its improved performance with respect to its competing rivals.

Algorithms↗
PubMed2005

Dynamics of parietal neural activity during spatial cognitive processing.

Dynamic neural processing unrelated to changes in sensory input or motor output is likely to be a hallmark of cognitive operations. Here we show that neural representations of space in parietal cortex are dynamic while monkeys perform a spatial cognitive operation on a static visual stimulus. We recorded neural activity in area 7a during a visual maze task in which monkeys mentally followed a path without moving their eyes. We found that the direction of the followed path could be recovered from neuronal population activity. When the monkeys covertly processed a path that turned, the population representation of path direction shifted in the direction of the turn. This neural population dynamic took place during a period of unchanging visual input and showed characteristics of both serial and parallel processing. The data suggest that the dynamic evolution of parietal neuronal activity is associated with the progression of spatial cognitive operations.

Action Potentials↗