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Material clusters of different sizes are known to exist in high-temperature plasmas due to plasma-wall interactions. The facts that these clusters, ranging from sub-microns to above mm in size, can move from one location to another quickly and that there are a lot of them make high-speed imaging and tracking one of the best, effective, and sometimes only diagnostic. An unsupervised machine learning technique based on deconvolutional neural networks is developed to analyze two-camera videos of high-temperature microparticles generated from exploding wires. The neural network utilizes a locally competitive algorithm to infer representations and optimize a dictionary composed of kernels, or basis vectors, for image analysis. Our primary goal is to use this method for feature recognition and prediction of the time-dependent three-dimensional (or "4D") microparticle motion. Features equivalent to local velocity vectors have been identified as the dictionary kernels or "building blocks" of the scene. The dictionary elements from the left and right camera views are found to be strongly correlated and satisfy the projection geometrical constraints. The results show that unsupervised machine learning techniques are promising approaches to process large sets of images for high-temperature plasmas and other scientific experiments. Machine learning techniques can be useful to handle the large amount of data and therefore aid the understanding of plasma-wall interaction.

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Brisk Y-type ganglion cells in the cat retina exhibit a high frequency resonance (HFR) in their responses to large, rapidly modulated stimuli. We used a computer model to test whether negative feedback mediated by axon-bearing amacrine cells onto ganglion cells could account for the experimentally observed properties of HFRs. Temporal modulation transfer functions (tMTFs) recorded from model ganglion cells exhibited HFR peaks whose amplitude, width, and locations were qualitatively consistent with experimental data. Moreover, the wide spatial distribution of axon-mediated feedback accounted for the observed increase in HFR amplitude with stimulus size. Model phase plots were qualitatively similar to those recorded from Y ganglion cells, including an anomalous phase advance that in our model coincided with the amplification of low-order harmonics that overlapped the HFR peak. When axon-mediated feedback in the model was directed primarily to bipolar cells, whose synaptic output was graded, or else when the model was replaced with a simple cascade of linear filters, it was possible to produce large HFR peaks but the region of anomalous phase advance was always eliminated, suggesting the critical involvement of strongly non-linear feedback loops. To investigate whether HFRs might contribute to visual processing, we simulated high frequency ocular tremor by rapidly modulating a naturalistic image. Visual signals riding on top of the imposed jitter conveyed an enhanced representation of large objects. We conclude that by amplifying responses to ocular tremor, HFRs may selectively enhance the processing of large image features.

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In primates, one type of retinal ganglion cell, the parasol cell, makes gap junctions with amacrine cells, the inhibitory, local circuit neurons. To study the effects of these gap junctions, we developed a linear, mathematical model of the retinal circuitry providing input to parasol cells. Electrophysiological studies have indicated that gap junctions do not enlarge the receptive field centres of parasol cells, but our results suggest that they make other contributions to their light responses. According to our model, the coupled amacrine cells enhance the responses of parasol cells to luminance contrast by disinhibition. We also show how a mixed chemical and electrical synapse between two sets of amacrine cells presynaptic to the parasol cells might make the responses of parasol cells more transient and, therefore, more sensitive to motion. Finally, we show how coupling via amacrine cells can synchronize the firing of parasol cells. An action potential in a model parasol cell can excite neighbouring parasol cells, but only when the coupled amacrine cells also fire action potentials. Passive conduction was ineffective due to low-pass temporal filtering. Inhibition from the axons of the coupled amacrine cells also produced oscillations that might synchronize the firing of more distant ganglion cells.

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We use a mathematical model to investigate how climbing fiber-dependent plasticity at granule cell to Purkinje cell (gr-->Pkj) synapses in the cerebellar cortex is influenced by the synaptic organization of the cerebellar-olivary system. Based on empirical studies, gr-->Pkj synapses are assumed to decrease in strength when active during a climbing fiber input (LTD) and increase in strength when active without a climbing fiber input (LTP). Results suggest that the inhibition of climbing fibers by cerebellar output combines with LTD/P to self-regulate spontaneous climbing fiber activity to an equilibrium level at which LTP and LTD balance and the expected net change in gr-->Pkj synaptic weights is zero. The synaptic weight vector is asymptotically confined to an equilibrium hyperplane defining the set of all possible combinations of synaptic weights consistent with climbing fiber equilibrium. Results also suggest restrictions on LTP/D at gr-->Pkj synapses required to produce synaptic weights that do not drift spontaneously.

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The implications for motor learning of the model developed in the previous article are analyzed using idealized Pavlovian eyelid conditioning trials, a simple example of cerebellar motor learning. Results suggest that changes in gr-->Pkj synapses produced by a training trial disrupt equilibrium and lead to subsequent changes in the opposite direction that restore equilibrium. We show that these opposing phases would make the net plasticity at each gr-->Pkj synapse proportional to the change in its activity during the training trial, as influenced by a factor that precludes plasticity when changes in activity are inconsistent. This yields an expression for the component of granule cell activity that supports learning, the across-trials consistency vector, the square of which determines the expected rate of learning. These results suggest that the equilibrium maintained by the cerebellar-olivary system must be disrupted in a specific and systematic manner to promote cerebellar-mediated motor learning.

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By evoking changes in climbing fiber activity, movement errors are thought to modify synapses from parallel fibers onto Purkinje cells (pf*Pkj) so as to improve subsequent motor performance. Theoretical arguments suggest there is an intrinsic tradeoff, however, between motor adaptation and long-term storage. Assuming a baseline rate of motor errors is always present, then repeated performance of any learned movement will generate a series of climbing fiber-mediated corrections. By reshuffling the synaptic weights responsible for any given movement, such corrections will degrade the memories for other learned movements stored in overlapping sets of synapses. The present paper shows that long-term storage can be accomplished by a second site of plasticity at synapses from parallel fibers onto stellate/basket interneurons (pf*St/Bk). Plasticity at pf*St/Bk synapses can be insulated from ongoing fluctuations in climbing fiber activity by assuming that changes in pf*St/Bk synapses occur only after changes in pf*Pkj synapses have built up to a threshold level. Although climbing fiber-dependent plasticity at pf*Pkj synapses allows for the exploration of novel motor strategies in response to changing environmental conditions, plasticity at pf*St/Bk synapses transfers successful strategies to stable long-term storage. To quantify this hypothesis, both sites of plasticity are incorporated into a dynamical model of the cerebellar cortex and its interactions with the inferior olive. When used to simulate idealized motor conditioning trials, the model predicts that plasticity develops first at pf*Pkj synapses, but with additional training is transferred to pf*St/Bk synapses for long-term storage.

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We describe a general diffusion model for analyzing the efficacy of individual synaptic inputs to threshold neurons. A formal expression is obtained for the system propagator which, when given an arbitrary initial state for the cell, yields the conditional probability distribution for the state at all later times. The propagator for a cell with a finite threshold is written as a series expansion, such that each term in the series depends only on the infinite threshold propagator, which in the diffusion limit reduces to a Gaussian form. This procedure admits a graphical representation in terms of an infinite sequence of diagrams. To connect the theory to experiment, we construct an analytical expression for the primary correlation kernel (PCK) which profiles the change in the instantaneous firing rate produced by a single postsynaptic potential (PSP). Explicit solutions are obtained in the diffusion limit to first order in perturbation theory. Our approximate expression resembles the PCK obtained by computer simulation, with the accuracy depending strongly on the mode of firing. The theory is most accurate when the synaptic input drives the membrane potential to a mean level more than one standard deviation below the firing threshold, making such cells highly sensitive to synchronous synaptic input.

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