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    Peter Rowat

    ABSTRACT
    ... 2.1 B asicapproach 13 2.2 The simulated world 16 2.3 Robbie's model of the world 17 2.3.1 The ring representation of objects 17 ... 25 2.4 E xploration 29 2.5 Summary of Robbie's world 31 Chapter 3 - ROSS: a robot simulation... more
    ... 2.1 B asicapproach 13 2.2 The simulated world 16 2.3 Robbie's model of the world 17 2.3.1 The ring representation of objects 17 ... 25 2.4 E xploration 29 2.5 Summary of Robbie's world 31 Chapter 3 - ROSS: a robot simulation system 3.1 How to use 33 3.1.1 G eneraldescription ...
    The phase relationships between motorneuron firing in invertebrate central pattern generators (CPGs) determine the sequence of muscle contractions and therefore the behavior of the animal. In the lobster stomatogastric ganglion, all of... more
    The phase relationships between motorneuron firing in invertebrate central pattern generators (CPGs) determine the sequence of muscle contractions and therefore the behavior of the animal. In the lobster stomatogastric ganglion, all of the neurons comprising two CPGs were identified. Physiologically determined parameters from identified neurons were incorporated in a new biophysically based model. To study the role of reciprocal inhibition the most common type of synaptic interaction in the CPGs, chains of neurons and 5×5 matrices, both with reciprocal inhibitory connections, were modeled. Oscillatory neurons at the ends of long odd-numbered chains could be brought into synchronization very rapidly. Pairs of oscillatory neurons embedded in the matrix, but bursting at different frequencies, became entrained at intermediate frequencies and phase relationships. The results suggest a mechanism for producing CPG motor patterns
    ABSTRACT
    Mutual excitation between two neurons is generally thought to raise the excitation level of each neuron or, if they are both bursty, to act to synchronize their bursts. If only one is bursty, it can induce synchronized bursts in the other... more
    Mutual excitation between two neurons is generally thought to raise the excitation level of each neuron or, if they are both bursty, to act to synchronize their bursts. If only one is bursty, it can induce synchronized bursts in the other cell. Here we show that two nonbursty cells can be induced to burst in synchrony by mutual excitatory synaptic connections, provided the presynaptic threshold for graded synaptic transmission at each synapse is at a different level. This mechanism may operate in a recently discovered network in the lobster Homarus gammarus. By a duality between presynaptic threshold and injected current, we also show that two identical, nonbursty, mutual excitatory cells could be induced to burst in synchrony by injecting differing amounts of current in the two cells. Finally we show that differential oscillations between two mutual excitatory cells could be stopped by a slow-tailed hyperpolarizing current pulse into one cell or a slow-tailed depolarizing pulse int...
    We study dynamical mechanisms underlying oscillatory behavior in reciprocal inhibitory pairs of neurons, using a two-dimensional cell model. We introduce one-and-two dimensional phase portraits to illustrate the behaviors, thus reducing... more
    We study dynamical mechanisms underlying oscillatory behavior in reciprocal inhibitory pairs of neurons, using a two-dimensional cell model. We introduce one-and-two dimensional phase portraits to illustrate the behaviors, thus reducing the study of dynamical mechanisms to planar geometrical properties. We examined whether other mechanisms besides the escape and release mechanisms (Wang and Rinzel, 1992) might be needed for some cases of reciprocal inhibition, and show that, within the confines of a simple two-dimensional cell model, escape and release are sufficient for all cases. We divided the behaviors of a single cell into six different types and examined the joint behaviors arising from every combination of pairs of cells with behaviors drawn from these six types. For the case of two quiescent cells or two cells each having plateau potentials, bifurcation diagrams demonstrate the relations between synaptic threshold and synaptic strength necessary for oscillations by escape, o...
    1. The gastric mill central pattern generator (CPG) controls the chewing movements of teeth in the gastric mill of the lobster. This CPG has been extensively studied, but the precise mechanism underlying pattern generation is not well... more
    1. The gastric mill central pattern generator (CPG) controls the chewing movements of teeth in the gastric mill of the lobster. This CPG has been extensively studied, but the precise mechanism underlying pattern generation is not well understood. The goal of this research was to develop a simplified model that captures the principle, biologically significant features of this CPG. We introduce a simplified neuron model that embodies approximations of well-known membrane currents, and is able to reproduce several global characteristics of gastric mill neurons. A network built with these neurons, using graded synaptic transmission and having the synaptic connections of the biological circuit, is sufficient to explain much of the network's behavior. 2. The cell model is a generalization and extension of the Van der Pol relaxation oscillator equations. It is described by two differential equations, one for current conservation and one for slow current activation. The model has a fast...
    The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all... more
    The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then a...
    ABSTRACT
    ... He can only occupy blank squares. A square marked 'B' or 'H' blocks his way. In general a square marked 'M' also blocks his way. If, however, he is facing an 'M' square he can pick up the whole... more
    ... He can only occupy blank squares. A square marked 'B' or 'H' blocks his way. In general a square marked 'M' also blocks his way. If, however, he is facing an 'M' square he can pick up the whole movable object, OBJA say, of which that square is a part. ...