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This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.
The Biocomputation Center at NASA Ames Research Center is dedicated to a union between computational, experimental and theoretical approaches to the study of neuroscience and of life sciences in general. The current emphasis is on computer reconstruction and visualization of vestibular macular architecture in three-dimensions (3-D), and on mathematical modeling and computer simulation of neural activity in the functioning system. Our methods are being used to interpret the influence of spaceflight on mammalian vestibular maculas in a model system, that of the adult Sprague-Dawley rat. More than twenty 3-D reconstructions of type I and type II hair cells and their afferents have been completed by digitization of contours traced from serial sections photographed in a transmission electron microscope. This labor-intensive method has now been replace d by a semiautomated method developed in the Biocomputation Center in which conventional photography is eliminated. All viewing, storage and manipulation of original data is done using Silicon Graphics workstations. Recent improvements to the software include a new mesh generation method for connecting contours. This method will permit the investigator to describe any surface, regardless of complexity, including highly branched structures such as are routinely found in neurons. This same mesh can be used for 3-D, finite volume simulation of synapse activation and voltage spread on neuronal surfaces visualized via the reconstruction process. These simulations help the investigator interpret the relationship between neuroarchitecture and physiology, and are of assistance in determining which experiments will best test theoretical interpretations. Data are also used to develop abstract, 3-D models that dynamically display neuronal activity ongoing in the system. Finally, the same data can be used to visualize the neural tissue in a virtual environment. Our exhibit will depict capabilities of our computational approaches and some of our findings from their application. For example, our research has demonstrated that maculas of adult mammals retain the property of synaptic plasticity. Ribbon synapses increase numerically and undergo changes in type and distribution (p<0.0001) in type II hair cells after exposure to microgravity for as few as nine days. The finding of macular synaptic plasticity is pertinent to the clinic, and may help explain some. balance disorders in humans. The software used in our investigations will be demonstrated for those interested in applying it in their own research.
Transient linear acceleration stimuli have been shown to elicit eighth nerve vestibular compound action potentials in birds and mammals. The present study was undertaken to better define the nature of the adequate stimulus for neurons generating the response in the chicken (Gallus domesticus). In particular, the study evaluated the question of whether the neurons studied are most sensitive to the maximum level of linear acceleration achieved or to the rate of change in acceleration (da/dt, or jerk). To do this, vestibular response thresholds were measured as a function of stimulus onset slope. Traditional computer signal averaging was used to record responses to pulsed linear acceleration stimuli. Stimulus onset slope was systematically varied. Acceleration thresholds decreased with increasing stimulus onset slope (decreasing stimulus rise time). When stimuli were expressed in units of jerk (g/ms), thresholds were virtually constant for all stimulus rise times. Moreover, stimuli having identical jerk magnitudes but widely varying peak acceleration levels produced virtually identical responses. Vestibular response thresholds, latencies and amplitudes appear to be determined strictly by stimulus jerk magnitudes. Stimulus attributes such as peak acceleration or rise time alone do not provide sufficient information to predict response parameter quantities. Indeed, the major response parameters were shown to be virtually independent of peak acceleration levels or rise time when these stimulus features were isolated and considered separately. It is concluded that the neurons generating short latency vestibular evoked potentials do so as "jerk encoders" in the chicken. Primary afferents classified as "irregular", and which traditionally fall into the broad category of "dynamic" or "phasic" neurons, would seem to be the most likely candidates for the neural generators of short latency vestibular compound action potentials.
Spatiotemporal convergence and two-dimensional (2-D) neural tuning have been proposed as a major neural mechanism in the signal processing of linear acceleration. To examine this hypothesis, we studied the firing properties of primary otolith afferents and central otolith neurons that respond exclusively to horizontal linear accelerations of the head (0.16-10 Hz) in alert rhesus monkeys. Unlike primary afferents, the majority of central otolith neurons exhibited 2-D spatial tuning to linear acceleration. As a result, central otolith dynamics vary as a function of movement direction. During movement along the maximum sensitivity direction, the dynamics of all central otolith neurons differed significantly from those observed for the primary afferent population. Specifically at low frequencies (</=0.5 Hz), the firing rate of the majority of central otolith neurons peaked in phase with linear velocity, in contrast to primary afferents that peaked in phase with linear acceleration. At least three different groups of central response dynamics were described according to the properties observed for motion along the maximum sensitivity direction. "High-pass" neurons exhibited increasing gains and phase values as a function of frequency. "Flat" neurons were characterized by relatively flat gains and constant phase lags (approximately 20-55 degrees ). A few neurons ("low-pass") were characterized by decreasing gain and phase as a function of frequency. The response dynamics of central otolith neurons suggest that the approximately 90 degrees phase lags observed at low frequencies are not the result of a neural integration but rather the effect of nonminimum phase behavior, which could arise at least partly through spatiotemporal convergence. Neither afferent nor central otolith neurons discriminated between gravitational and inertial components of linear acceleration. Thus response sensitivity was indistinguishable during 0.5-Hz pitch oscillations and fore-aft movements. The fact that otolith-only central neurons with "high-pass" filter properties exhibit semicircular canal-like dynamics during head tilts might have important consequences for the conclusions of previous studies of sensory convergence and sensorimotor transformations in central vestibular neurons.
Accurate measurement of single-trial responses is key to a definitive use of complex electromagnetic and hemodynamic measurements in the investigation of brain dynamics. We developed the multiple component, Event-Related Potential (mcERP) approach to single-trial response estimation. To improve our resolution of dynamic interactions between neuronal ensembles located in different layers within a cortical region and/or in different cortical regions. The mcERP model assets that multiple components defined as stereotypic waveforms comprise the stimulus-evoked response and that these components may vary in amplitude and latency from trial to trial. Maximum a posteriori (MAP) solutions for the model are obtained by iterating a set of equations derived from the posterior probability. Our first goal was to use the ANTWERP algorithm to analyze interactions (specifically latency and amplitude correlation) between responses in different layers within a cortical region. Thus, we evaluated the model by applying the algorithm to synthetic data containing two correlated local components and one independent far-field component. Three cases were considered: the local components were correlated by an interaction in their single-trial amplitudes, by an interaction in their single-trial latencies, or by an interaction in both amplitude and latency. We then analyzed the accuracy with which the algorithm estimated the component waveshapes and the single-trial parameters as a function of the linearity of each of these relationships. Extensions of these analyses to real data are discussed as well as ongoing work to incorporate more detailed prior information.
Quantitative study of the static and dynamic response properties of some otolith-sensitive neurons has been difficult in the past partly because their responses to different linear acceleration vectors exhibited no "null" plane and a dependence of phase on stimulus orientation. The theoretical formulation of the response ellipse provides a quantitative way to estimate the spatio-temporal properties of such neurons. Its semi-major axis gives the direction of the polarization vector (i.e., direction of maximal sensitivity) and it estimates the neuronal response for stimulation along that direction. In addition, the semi-minor axis of the ellipse provides an estimate of the neuron's maximal sensitivity in the "null" plane. In this paper, extracellular recordings from otolith-sensitive vestibular nuclei neurons in decerebrate rats were used to demonstrate the practical application of the method. The experimentally observed gain and phase dependence on the orientation angle of the acceleration vector in a head-horizontal plane was described and satisfactorily fit by the response ellipse model. In addition, the model satisfactorily fits neuronal responses in three-dimensions and unequivocally demonstrates that the response ellipse formulation is the general approach to describe quantitatively the spatial properties of vestibular neurons.
Three classes of vestibular-related neurons were found in and near the prepositus and medial vestibular nuclei of alert or decerebrate gerbils, those responding to: horizontal translational motion, horizontal head rotation, or both. Their distribution ratios were 1:2:2, respectively. Many cells responsive to translational motion exhibited spatiotemporal characteristics with both response gain and phase varying as a function of the stimulus vector angle. Rotationally sensitive neurons were distributed as Type I, II, or III responses (sensitive to ipsilateral, contralateral, or both directions, respectively) in the ratios of 4:6:1. Four tested factors shaped the response dynamics of the sampled neurons: canal-otolith convergence, oculomotor-related activity, rotational Type (I or II), and the phase of the maximum response. Type I nonconvergent cells displayed increasing gains with increasing rotational stimulus frequency (0.1-2.0 Hz, 60 degrees /s), whereas Type II neurons with convergent inputs had response gains that markedly decreased with increasing translational stimulus frequency (0.25-2.0 Hz, +/-0.1 g). Type I convergent and Type II nonconvergent neurons exhibited essentially flat gains across the stimulus frequency range. Oculomotor-related activity was noted in 30% of the cells across all functional types, appearing as burst/pause discharge patterns related to the fast phase of nystagmus during head rotation. Oculomotor-related activity was correlated with enhanced dynamic range compared with the same category that had no oculomotor-related response. Finally, responses that were in-phase with head velocity during rotation exhibited greater gains with stimulus frequency increments than neurons with out-of-phase responses. In contrast, for translational motion, neurons out of phase with head acceleration exhibited low-pass characteristics, whereas in-phase neurons did not. Data from decerebrate preparations revealed that although similar response types could be detected, the sampled cells generally had lower background discharge rates, on average one-third lower response gains, and convergent properties that differed from those found in the alert animals. On the basis of the dynamic response of identified cell types, we propose a pair of models in which inhibitory input from vestibular-related neurons converges on oculomotor neurons with excitatory inputs from the vestibular nuclei. Simple signal convergence and combinations of different types of vestibular labyrinth information can enrich the dynamic characteristics of the rotational and translational vestibuloocular responses.
The p38 stress-activated protein kinase pathway is involved in regulation of phosphorylation of Hsp25, which in turn regulates actin filament dynamic in non-neuronal cells. We report that p38, Hsp25 and Akt signaling pathways were specifically activated in spinal motor neurons after sciatic nerve axotomy. The activation of the p38 kinase was required for induction of Hsp25 expression. Furthermore, Hsp25 formed a complex with Akt, a member of PI-3 kinase pathway that prevents neuronal cell death. Together, our observations implicate Hsp25 as a central player in a complex system of signaling that may both promote regeneration of nerve fibers and prevent neuronal cell death in the injured spinal cord.
The major goal of this study was to determine the patterns of convergence of non-labyrinthine inputs from the limbs and viscera onto vestibular nucleus neurons receiving signals from vertical semicircular canals or otolith organs. A secondary aim was to ascertain whether the effects of non-labyrinthine inputs on the activity of vestibular nucleus neurons is affected by bilateral peripheral vestibular lesions. The majority (72%) of vestibular nucleus neurons in labyrinth-intact animals whose firing was modulated by vertical rotations responded to electrical stimulation of limb and/or visceral nerves. The activity of even more vestibular nucleus neurons (93%) was affected by limb or visceral nerve stimulation in chronically labyrinthectomized preparations. Some neurons received non-labyrinthine inputs from a variety of peripheral sources, including antagonist muscles acting at the same joint, whereas others received inputs from more limited sources. There was no apparent relationship between the spatial and dynamic properties of a neuron's responses to tilts in vertical planes and the non-labyrinthine inputs that it received. These data suggest that non-labyrinthine inputs elicited during movement will modulate the processing of information by the central vestibular system, and may contribute to the recovery of spontaneous activity of vestibular nucleus neurons following peripheral vestibular lesions. Furthermore, some vestibular nucleus neurons with non-labyrinthine inputs may be activated only during particular behaviors that elicit a specific combination of limb and visceral inputs.
A bilateral projection from the vestibular efferent neurons, located dorsal to the genu of the facial nerve, to the cerebellar flocculus and ventral paraflocculus was demonstrated. Efferent neurons were double-labeled by the unilateral injections of separate retrograde tracers into the labyrinth and into the floccular and ventral parafloccular lobules. Efferent neurons were found with double retrograde tracer labeling both ipsilateral and contralateral to the sites of injection. No double labeling was found when using a fluorescent tracer with non-fluorescent tracers such as horseradish peroxidase (HRP) or biotinylated dextran amine (BDA), but large percentages of efferent neurons were found to be double labeled when using two fluorescent substances including: fluorogold, microruby dextran amine, or rhodamine labeled latex beads. These data suggest a potential role for vestibular efferent neurons in modulating the dynamics of the vestibulo-ocular reflex (VOR) during normal and adaptive conditions.
Sensory processing in the visual, auditory, and somatosensory systems is often studied by recording electrical activity in response to a stimulus of interest. Typically, multiple trial responses to the stimulus are averaged to isolate the stereotypic response from noise. However, averaging ignores dynamic variability in the neuronal response, which is potentially critical to understanding stimulus-processing schemes. Thus, we developed the multiple component, Event-Related Potential (mcERP) model. This model asserts that multiple components, defined as stereotypic waveforms, comprise the stimulus-evoked response and that these components may vary in amplitude and latency from trial to trial. Application of this model to data recorded simultaneously from all six laminae of V1 in an awake, behaving monkey performing a visual discrimination yielded three components. The first component localized to granular V1, the second was located in supragranular V1, and the final component displayed a multi-laminar distribution. These modeling results, which take into account single-trial response dynamics, illustrated that the initial activation of VI occurs in the granular layer followed by activation in the supragranular layers. This finding is expected because the average response in those layers demonstrates the same progression and because anatomical evidence suggests that the feedforward input in V1 enters the granular layer and progresses to supragranular layers. In addition to these findings, the granular component of the model displayed several interesting trial-to-trial characteristics including (1) a bimodal latency distribution, (2) a latency-related variation in response amplitude, (3) a latency correlation with the supragranular component, and (4) an amplitude and latency association with the multi-laminar component. Direct analyses of the single-trial data were consistent with these model predictions. These findings suggest that V1 has at least 2 transmission states, which may be modulated by various effects such as attention, dynamics in local EEG rhythm, or variation in sensory inputs.
We measured human ocular torsion (OT) monocularly (using video) and binocularly (using search coils) while sinusoidally accelerating (0.7 g) five human subjects along an earth-horizontal axis at five frequencies (0.35, 0.4, 0.5, 0.75, and 1.0 Hz). The compensatory nature of OT was investigated by changing the relative orientation of the dynamic (linear acceleration) and static (gravitational) cues. Four subject orientations were investigated: (1) Y-upright-acceleration along the interaural (y) axis while upright; (2) Y-supine-acceleration along the y-axis while supine; (3) Z-RED-acceleration along the dorsoventral (z) axis with right ear down; (4) Z-supine-acceleration along the z-axis while supine. Linear acceleration in the Y-upright, Y-supine and Z-RED orientations elicited conjugate OT. The smaller response in the Z-supine orientation appeared disconjugate. The amplitude of the response decreased and the phase lag increased with increasing frequency for each orientation. This frequency dependence does not match the frequency response of the regular or irregular afferent otolith neurons; therefore the response dynamics cannot be explained by simple peripheral mechanisms. The Y-upright responses were larger than the Y-supine responses (P < 0.05). This difference indicates that OT must be more complicated than a simple low-pass filtered response to interaural shear force, since the dynamic shear force along the interaural axis was identical in these two orientations. The Y-supine responses were, in turn, larger than the Z-RED responses (P < 0.01). Interestingly, the vector sum of the Y-supine responses plus Z-RED responses was not significantly different (P = 0.99) from the Y-upright responses. This suggests that, in this frequency range, the conjugate OT response during Y-upright stimulation might be composed of two components: (1) a response to shear force along the y-axis (as in Y-supine stimulation), and (2) a response to roll tilt of gravitoinertial force (as in Z-RED stimulation).
Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.
In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.
Receptive fields are commonly used to describe spatial characteristics of sensory neuron responses. They can be extended to characterize temporal or dynamical aspects by mapping neural responses in dynamical state spaces. The state-space receptive field of a neuron is the probability distribution of the dynamical state of the stimulus-generating system conditioned upon the occurrence of a spike. We have computed state-space receptive fields for semicircular canal afferent neurons in the bullfrog (Rana catesbeiana). We recorded spike times during broad-band Gaussian noise rotational velocity stimuli, computed the frequency distribution of head states at spike times, and normalized these to obtain conditional pdfs for the state. These state-space receptive fields quantify what the brain can deduce about the dynamical state of the head when a single spike arrives from the periphery. c2001 Elsevier Science B.V. All rights reserved.
1. To investigate the neural substrate of vestibulospinal reflexes in decerebrate cats, we studied the responses of pontomedullary reticulospinal neurons to natural stimulation of the labyrinth in vertical planes. Our principal aim was to determine whether reticulospinal neurons that terminate in, or are likely to give off collaterals to, the upper cervical segments had properties similar to those of the vestibulocollic reflex (VCR). 2. Antidromic stimulation was used to determine whether the neurons projected to the neck, lower cervical, thoracic, or lumbar levels. Dynamics of the responses of spontaneously firing neurons were studied with sinusoidal stimuli delivered at 0.05-1 Hz and aligned to the plane of body rotation, that produced maximal modulation of the neuron (response vector orientation). Each neuron was assigned a vestibular input classification of otolith, vertical canal, otolith + canal, or spatial-temporal convergence (STC). 3. We found, in agreement with previous studies, that the largest fraction of pontomedullary reticulospinal neurons projected to the lumbar cord, and that only a small number ended in the neck segments. Neurons projecting to all levels of the spinal cord had similar responses to labyrinth stimulation. 4. Reticulospinal neurons that received only vertical canal inputs were rare (1 of 67 units). Most reticulospinal neurons (48%) received predominant otolith inputs, 18% received otolith + canal input, and only 9% had STC behavior. These data are in sharp contrast to the results of our previous studies of vestibulospinal neurons. A considerable portion of vestibulospinal neurons receives vertical canal input (38%), fewer receive predominantly otolith input (22%), whereas the proportion that have otolith + canal input or STC behavior is similar to our present reticulospinal data. 5. The response vector orientations of our reticulospinal neurons, particularly those with canal inputs (canal, otolith + canal, STC) were predominantly in the roll quadrants. There was no evidence of convergence of inputs from like canals across the midline (e.g., right anterior + left anterior). 6. Two characteristics of the VCR, STC behavior and bilateral input from symmetric vertical canals (in some muscles), cannot be accounted for by the reticulospinal neurons that we studied. Because these characteristics are also not seen in vestibulocollic neurons, they are likely to be the result of the appropriate convergence of vestibular signals in the spinal cord. 7. Pontomedullary reticulospinal neurons seem particularly well suited to play a role in gravity-dependent postural reflexes of neck and limbs.
A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.