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PubMed1990

Evolutionary optimization and neural network models of behavior.

One of the main challenges to the adaptionist program in general and the use of optimization models in behavioral and evolutionary ecology, in particular, is that organisms are so constrained by ontogeny and phylogeny that they may not be able to attain optimal solutions, however those are defined. This paper responds to the challenge through the comparison of optimality and neural network models for the behavior of an individual polychaete worm. The evolutionary optimization model is used to compute behaviors (movement in and out of a tube) that maximize a measure of Darwinian fitness based on individual survival and reproduction. The neural network involves motor, sensory, energetic reserve and clock neuronal groups. Ontogeny of the neural network is the change of connections of a single individual in response to its experiences in the environment. Evolution of the neural network is the natural selection of initial values of connections between groups and learning rules for changing connections. Taken together, these can be viewed as "design parameters". The best neural networks have fitnesses between 85% and 99% of the fitness of the evolutionary optimization model. More complicated models for polychaete worms are discussed. Formulation of a neural network model for host acceptance decisions by tephritid fruit flies leads to predictions about the neurobiology of the flies. The general conclusion is that neural networks appear to be sufficiently rich and plastic that even weak evolution of design parameters may be sufficient for organisms to achieve behaviors that give fitnesses close to the evolutionary optimal fitness, particularly if the behaviors are relatively simple.

Animals↗

PubMed1996

Adaptive computational models of fast learning of motion direction discrimination.

In a previous study, we found that subjects' performance in a task of direction discrimination in stochastic motion stimuli shows fast improvement in the absence of feedback and the learned ability is retained over a period of time. We model this learning using two unsupervised approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Extensive simulations with the models show performance similar to psychophysical results.

Animals↗

PubMed1996

A biologically plausible model of early visual motion processing. I: theory and implementation.

A model of local image encoding is described which explicitly incorporates quantitative data about the number density, bandwidth and receptive field organisation of neurons involved in motion detection. The model solves the problem of extracting local velocity on the basis of inputs tuned to spatiotemporal frequency and sensitive to contrast. The spatiotemporally tuned, opponent motion filters are followed by a compressive non-linearity and comprise a first stage. The inter-stage signals are interpreted as those from single neurons and the second stage is modelled as a neural-network layer. The second stage uses semilinear units and models the effect of lateral, on-centre off-surround, intra-layer connections. Characterisation of the first stage leads to a clarification of the concept of the psychophysical 'channel' and its relation to physiological data. The quantitative parametrisation of the model allows the simulation of several psychophysical phenomena which are reported in a companion paper.

Fourier Analysis↗

PubMed1996

A biologically plausible model of early visual motion processing. II: psychophysical application.

We test the model of early visual processing introduced in the companion paper by simulating a range of psychophysical phenomena. We present new data concerning our ability to discriminate the speed of drifting gratings when spatiotemporally apertured in a variety of ways. We shall investigate the role played by the aperture in modifying the grating's behaviour from its idealisation as a pure Fourier component and show that this is not negligible. Other phenomena which we simulate and explain relate to the way perceived velocity is influenced by contrast and spatial frequency. Many of our explanations are couched in terms of the relative number of cells occurring within each locale of the Fourier domain. This use of the cell density map is a unifying concept and avoids the necessity for a range of separate mechanisms. We argue that a neurophysiologically detailed model is necessary in order to explain psychophysical data (Weber fractions) which vary over less than an order of magnitude, and small deviations from veridical encoding of velocity.

Artifacts↗

PubMed1990

Extraction of motion parallax structure in the visual system. I.

We present a paradigm to estimate local affine motion parallax structure from a varying image irradiance pattern. The method is based on a matching of jets of irradiance rather than the local image irradiance pattern or features of it. It does not put any constraints on the structure of the image irradiance pattern. Moreover, the aperture problem does not arise and additional ad hoc constraints on the velocity field like the "smoothness constraint" are superfluous. An implementation is designed such that the affine structure of the local motion parallax field is represented through simple combinations of the outputs of physiologically plausible receptive fields and their temporal derivatives.

Cybernetics↗

PubMed1990

White noise analysis of temporal properties in simple receptive fields of cat cortex.

We studied the linear and nonlinear temporal response properties of simple cells in cat visual cortex by presenting at single positions in the receptive field an optimally oriented bar stimulus whose luminance was modulated in a random, binary fashion. By crosscorrelating a cell's response with the input it was possible to obtain the zeroth-, first-, and second-order Wiener kernels at each RF location. Simple cells showed pronounced nonlinear temporal properties as revealed by the presence of prominent second-order kernels. A more conventional type of response histogram was also calculated by time-locking a histogram on the occurrence of the desired stimulus in the random sequence. A comparison of the time course of this time-locked response with that of the kernel prediction indicated that nonlinear temporal effects of order higher than two are unimportant. The temporal properties of simple cells were well represented by a cascade model composed of a linear filter followed by a static nonlinearity. These modelling results suggested that for simple cells, the nonlinearity occurs late and probably is a soft threshold associated with the spike generating mechanism of the cortical cell itself. This result is surprising in view of the known threshold nonlinearities in preceding lateral geniculate and retinal neurons. It suggests that geniculocortical connectivity cancels the earlier nonlinearities to create a highly linear representation inside cortical simple cells.

Animals↗

PubMed1991

The spectral dynamics and its applications in EEG.

The method of spectral dynamics is introduced to process slow changes in EEG. This method is based on evaluation of distance between EEG spectra. The typical application field is pharmaco-EEG after single dose drug administration, where distances between pre and post drug EEG spectra are computed. The distance between two spectra may be, however, defined in several different ways. The paper deals with the well known Lp metrics and with the metrics alpha that plays an important role in discriminating stationary processes with different spectra. The Lp and alpha metrics are non-equivalent and their properties, consistency and robustness, are studied in a simple statistical model.

Animals↗

PubMed1991

Local adaptations of two naturally occurring neuronal conductances, gK + (A) and gK + (Ca), allow for associative conditioning and contiguity judgements in artificial neural networks.

Features of two potassium conductances implicated in the acquisition of conditioned reflexes, the slow calcium dependent conductance (gK + (Ca] and the fast transient conductance (gK + (A], were incorporated into a 6 x 6 element artificial neural network. Adaptive algorithms derived from observations of cortical neurons during associative learning changed gK + (A) in proportion to the product of this current and an EPSP-induced second messenger concentration, and changed gK + (Ca) as a function of a spike-induced second messenger concentration. This network concurrently acquired two distinct representations in response to presentation of stimuli: one resembled associative conditioning (defined in terms of its sensitivity to forward pairing vs. simultaneous or backward pairing); the other reflected contiguous pairings of stimuli. The acquisition of one representation did not markedly interfere with acquisition of the other. This network may accordingly serve as an example of a self-organizing system which minimizes the postulated inherent cross talk between functionally dissiminar representations (Minsky and Papert 1988).

Acclimatization↗

PubMed1995

On deriving analyser characteristics from summation-at-threshold data.

It has been proved that a detection process may be accounted for by a simple two-state model consisting of a collection of linear analysers followed by a maximum-output decision rule provided that a set of all threshold stimuli is convex. A non-parametrical method to identify the analysers constituting such a model is proposed.

Humans↗

PubMed1991

Terminal chaos for information processing in neurodynamics.

New nonlinear phenomenon-terminal chaos caused by failure of the Lipschitz condition at equilibrium points of dynamical systems is introduced. It is shown that terminal chaos has a well organized probabilistic structure which can be predicted and controlled. This gives an opportunity to exploit this phenomenon for information processing. It appears that chaotic states of neurons activity are associated with higher level of cognitive processes such as generalization and abstraction.

Animals↗

PubMed1994

Chaos in percepts?

Multistability in perceptual tasks has suggested that the mechanisms underlying our percepts might be modeled as nonlinear, deterministic systems that exhibit chaotic behavior. We present evidence supporting this view, obtaining an estimate of 3.5 for the dimensionality of such a system. A surprising result is that this estimate applies for a rather diverse range of perceptual tasks.

Eye Movements↗

PubMed1993

A theory of cerebral learning regulated by the reward system. I. Hypotheses and mathematical description.

Hypothetical mechanisms of the neocorticohippocampal system are presented. Neurophysiological and neuroanatomical findings concerning the system are integrated to demonstrate how animals associate sensory stimuli with rewarding actions: (1) cortical plasticity regulated by cholinergic/noradrenergic inputs from the hypothalamic reward system reinforces association connections between the most activated columns in the cortex; (2) the repetitive reinforcement forms association pathways connecting sensory cortical columns activated by the stimuli with motor cortical columns producing the rewarding actions; (3) after the pathways are formed, the cortex is capable of temporarily memorizing the stimuli by producing long-term potentiation through the cortico-hippocampal circuits; and (4) the memory allows the cortex to extend correct association pathways even in an environment where sensory stimuli rapidly change. A mathematical model of parts of the nervous system is presented to quantitatively examine the mechanisms. Membrane characteristics of single neurons are given by the Hodgkin-Huxley electric circuit. According to anatomical data, neural circuits of the neocortico-hippocampal system are composed by connecting populations of the model neurons. Computer simulation using physiological data concerning ion channels demonstrates how the mechanisms work and how to test the hypotheses presented.

Animals↗

PubMed1992

Evidence for a generalized Laguerre transform of temporal events by the visual system.

It is generally assumed that the early visual processing is constituted by a set of filters operating in parallel. In this respect the visual system performs a transform, generating a code of the characteristics of the input signal. Recently, it has been suggested that the coding of the spatial characteristics by the visual system can be described by a Hermite transform (Martens, 1990a, b). It was also suggested that a three-dimensional Hermite transform can be used to code spatiotemporal events. In contrast to this latter suggestion, we argue that the coding of temporal events takes the form of a generalized Laguerre transform. We review psychophysical evidence supporting this hypothesis.

Algorithms↗

PubMed1993

Associative learning in a network model of Hermissenda crassicornis. II. Experiments.

A companion paper in a previous issue of this journal presented a resistance-capacitance circuit computer model of the four-neuron visual-vestibular network of the invertebrate marine mollusk Hermissenda crassicornis. In the present paper, we demonstrate that changes in the model's output in response to simulated associative training is quantitatively similar to behavioral and electrophysiological changes in response to associative training of Hermissenda crassicornis. Specifically, the model demonstrates many characteristics of conditioning: sensitivity to stimulus contingency, stimulus specificity, extinction, and savings. The model's learning features also are shown to be devoid of non-associative components. Thus, this computational model is an excellent tool for examining the information flow and dynamics of biological associative learning and for uncovering insights concerning associative learning, memory, and recall that can be applied to the development of artificial neural networks.

Animals↗

PubMed1992

A computational model of four regions of the cerebellum based on feedback-error learning.

We propose a computationally coherent model of cerebellar motor learning based on the feedback-error-learning scheme. We assume that climbing fiber responses represent motor-command errors generated by some of the premotor networks such as the feedback controllers at the spinal-, brain stem- and cerebral levels. Thus, in our model, climbing fiber responses are considered to convey motor errors in the motor-command coordinates rather than in the sensory coordinates. Based on the long-term depression in Purkinje cells each corticonuclear microcomplex in different regions of the cerebellum learns to execute predictive and coordinative control of different types of movements. Ultimately, it acquires an inverse model of a specific controlled object and complements crude control by the premotor networks. This general model is developed in detail as a specific neural circuit model for the lateral hemisphere. A new experiment is suggested to elucidate the coordinate frame in which climbing fiber responses are represented.

Animals↗

PubMed1992

Adaptive feedback control models of the vestibulocerebellum and spinocerebellum.

We extend the cerebellar learning model proposed by Kawato and Gomi (1992) to the case where a specific region of the cerebellum executes adaptive feedback control as well as feedforward control. The model is still based on the feedback-error-learning scheme. The proposed adaptive feedback control model is developed in detail as a specific neural circuit model for three different regions of the cerebellum and the learning of the corresponding representative movements: (i) the flocculus and adaptive modification of the vestibulo-ocular reflex and optokinetic eye-movement responses, (ii) the vermis and adaptive posture control, and (iii) the intermediate zones of the hemisphere and adaptive control of locomotion. As a representative example, simultaneous adaptation of the vestibulo-ocular reflex and the optokinetic eye-movement response was successfully simulated while the Purkinje cells receive copies of motor commands through recurrent neural connections as well as vestibular and retinal-slip parallel-fiber inputs.

Animals↗

PubMed1992

The relationship between short-term memory capacity and EEG power spectral density.

Multiplying memory span by mental speed, we obtain the information entropy of short-term memory capacity, which is rate-limiting for cognitive functions and corresponds with EEG power spectral density. The number of EEG harmonics (n = 1, 2,..., 9) is identical with memory span, and the eigenvalues of the EEG impulse response are represented by the zero-crossings up to the convolved fundamental, the P300. In analogy to quantum mechanics the brain seems to be an ideal detector simply measuring the energy of wave forms. No matter what the stimulus is and how the brain behaves, the metric of signal and memory can always be understood as a superposition of n pi states of different energy and their eigenvalues.

Brain↗

PubMed1992

A self-organising neural network model of image velocity encoding.

A self-organising neural network has been developed which maps the image velocities of rigid objects, moving in the fronto-parallel plane, topologically over a neural layer. The input is information in the Fourier domain about the spatial components of the image. The computation performed by the network may be viewed as a neural instantiation of the Intersection of Constraints solution to the aperture problem. The model has biological plausibility in that the connectivity develops simply as a result of exposure to inputs derived from rigid translation of textures and its overall organisation is consistent with psychophysical evidence.

Animals↗