We investigate the dynamics of large arrays of coupled phase oscillators driven by random intrinsic frequencies under a variety of coupling schemes, by computing the time-dependent cross-correlation function numerical...
We investigate the dynamics of large arrays of coupled phase oscillators driven by random intrinsic frequencies under a variety of coupling schemes, by computing the time-dependent cross-correlation function numerically for a two-dimensional array consisting of 128×128 oscillators as well as analytically for a simpler model. Our analysis shows that for overall equal interaction strength, a sparse-coupling scheme in which each oscillator is coupled to a small, randomly selected subset of its neighbors leads to a more rapid and robust phase locking than nearest-neighbor coupling or locally dense connection schemes.
Single nerve cells with static properties have traditionally been viewed as the building blocks for networks that show emergent phenomena. In contrast to this approach, we study here how the overall network activity c...
ISBN:
(纸本)9781558602229
Single nerve cells with static properties have traditionally been viewed as the building blocks for networks that show emergent phenomena. In contrast to this approach, we study here how the overall network activity can control single cell parameters such as input resistance, as well as time and space constants, parameters that are crucial for excitability and spatio-temporal integration. Using detailed computer simulations of neocortical pyramidal cells, we show that the spontaneous background firing of the network provides a means for setting these parameters. The mechanism for this control is through the large conductance change of the membrane that is induced by both non-NMDA and NMDA excitatory and inhibitory synapses activated by the spontaneous background activity.
The authors have designed, built and tested a number of analog CMOS VLSI circuits for computing 1D motion from the time-varying intensity values provided by an array of on-chip phototransistors. The authors present ex...
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The authors have designed, built and tested a number of analog CMOS VLSI circuits for computing 1D motion from the time-varying intensity values provided by an array of on-chip phototransistors. The authors present experimental data for three such circuits and discuss their relative performance. One circuit approximates the correlation model, one the gradient model, while a third chip uses resistive grids to compute zerocrossings to be tracked over time by a separate digital processor. All circuits integrate image acquisition with image processing functions and compute velocity in real time. Finally, for comparison, the authors also describe the performance of a simple motion algorithm using off-the-shelf components.< >
The dynamic behavior of a network model consisting of all-to-all excitatory coupled binary neurons with global inhibition is studied analytically and numerically. We prove that for random input signals, the output of ...
ISBN:
(纸本)9781558602229
The dynamic behavior of a network model consisting of all-to-all excitatory coupled binary neurons with global inhibition is studied analytically and numerically. We prove that for random input signals, the output of the network consists of synchronized bursts with apparently random intermissions of noisy activity. Our results suggest that synchronous bursts can be generated by a simple neuronal architecture which amplifies incoming coincident signals. This synchronization process is accompanied by dampened oscillations which, by themselves, however, do not play any constructive role in this and can therefore be considered to be an epiphenomenon.
A method for transforming performance evaluation signals distal both in space and time into proximal signals usable by supervised learning algorithms, presented in [Jordan & Jacobs 90], is examined. A simple obser...
ISBN:
(纸本)9781558602229
A method for transforming performance evaluation signals distal both in space and time into proximal signals usable by supervised learning algorithms, presented in [Jordan & Jacobs 90], is examined. A simple observation concerning differentiation through models trained with redundant inputs (as one of their networks is) explains a weakness in the original architecture and suggests a modification: an internal world model that encodes action-space exploration and, crucially, cancels input redundancy to the forward model is added. Learning time on an example task, cartpole balancing, is thereby reduced about 50 to 100 times.
The accuracy of optical flow estimation depends on the spatio-temporal discretization used in the computation. The authors propose an adaptive multiscale method, where the discretization scale is chosen locally accord...
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The accuracy of optical flow estimation depends on the spatio-temporal discretization used in the computation. The authors propose an adaptive multiscale method, where the discretization scale is chosen locally according to an estimate of the relative error in the velocity measurements. They show that their coarse-to-fine method provides substantially better results of optical flow than conventional algorithms. The authors map this multiscale strategy onto their model of motion computation in primate area MT. The model consists of two stages: (1) local velocities are measured across multiple spatio-temporal channels, while (2) the optical flow field is computed by a network of direction-selective neurons at multiple spatial resolutions. Their model neurons show the same nonclassical receptive field properties as Allman's type I MT neurons and lead to a novel interpretation of some aspect of the motion capture illusion.< >
Tactile responses in the granule cell layers of the cerebellar hemispheres of rats are topographically arranged as a series of patches each representing a different region of the body surface. Previous observations ha...
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Tactile responses in the granule cell layers of the cerebellar hemispheres of rats are topographically arranged as a series of patches each representing a different region of the body surface. Previous observations had suggested that patches representing specific body parts recur in similar folial positions in different individuals;however, these relationships were not quantified. In this study we make inter-animal comparisons of the detailed distribution of receptive fields in the granule cell layer of the crown of crus IIa by using physiological mapping techniques. The results suggest that maps from different individuals do, in fact, share several topological features. These include the regions of the body surface represented, the general proportions of these representations, the relative position of patches representing the same body parts, and the organization of receptive fields within patches located in similar positions. The principal variability seen in these comparisons was in the detailed neighborhood relations between different patches. As a result of the analysis of the consistent and variable features of these maps, we propose and discuss a new role for these cerebellar regions in coordinating the acquisition of tactile sensory information.
We have designed and tested a one-dimensional 64 pixel, analog CMOS VLSI chip which localizes intensity edges in real-time. This device exploits on-chip photoreceptors and the natural filtering properties of resistive...
ISBN:
(纸本)9781558601840
We have designed and tested a one-dimensional 64 pixel, analog CMOS VLSI chip which localizes intensity edges in real-time. This device exploits on-chip photoreceptors and the natural filtering properties of resistive networks to implement a scheme similar to and motivated by the Difference of Gaussians (DOG) operator proposed by Marr and Hildreth (1980). Our chip computes the zero-crossings associated with the difference of two exponential weighting functions. If the derivative across this zero-crossing is above a threshold, an edge is reported. Simulations indicate that this technique will extend well to two dimensions.
A large number of computer vision algorithms for finding intensity edges, computing motion, depth, and color, and recovering the three-dimensional shape of objects have been developed within the framework of minimizin...
A large number of computer vision algorithms for finding intensity edges, computing motion, depth, and color, and recovering the three-dimensional shape of objects have been developed within the framework of minimizing an associated "energy" or "cost" functional. Particularly successful has been the introduction of binary variables coding for discontinuities in intensity, optical flow field, depth, and other variables, allowing image segmentation to occur in these modalities. The associated nonconvex variational functionals can be mapped onto analog, resistive networks, such that the stationary voltage distribution in the network corresponds to a minimum of the functional. The performance of an experimental analog very-large-scale integration (VLSI) circuit implementing the nonlinear resistive network for the problem of two-dimensional surface interpolation in the presence of discontinuities is demonstrated;this circuit is implemented in complementary metal oxide semiconductor technology.
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