The long-term goal of our laboratory is the development of analog resistive network-based VLSI implementations of early and intermediate vision algorithms. We demonstrate an experimental circuit for smoothing and segm...
The long-term goal of our laboratory is the development of analog resistive network-based VLSI implementations of early and intermediate vision algorithms. We demonstrate an experimental circuit for smoothing and segmenting noisy and sparse depth data using the resistive fuse and a 1-D edge-detection circuit for computing zero-crossings using two resistive grids with different space-constants. To demonstrate the robustness of our algorithms and of the fabricated analog CMOS VLSI chips, we are mounting these circuits onto small mobile vehicles operating in a real-time, laboratory environment.
The goal in this work has been to identify the neuronal elements of the cortical column that are most likely to support the learning of nonlinear associative maps. We show that a particular style of network learning a...
The goal in this work has been to identify the neuronal elements of the cortical column that are most likely to support the learning of nonlinear associative maps. We show that a particular style of network learning algorithm based on locally-tuned receptive fields maps naturally onto cortical hardware, and gives coherence to a variety of features of cortical anatomy, physiology, and biophysics whose relations to learning remain poorly understood.
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.
This paper presents the results of a simulation of the spatial relationship between the inferior olivary nucleus and folium crus IIA of the lateral hemisphere of the rat cerebellum. The principal objective of this mod...
This paper presents the results of a simulation of the spatial relationship between the inferior olivary nucleus and folium crus IIA of the lateral hemisphere of the rat cerebellum. The principal objective of this modeling effort was to resolve an apparent conflict between a proposed zonal organization of olivary projections to cerebellar cortex suggested by anatomical tract-tracing experiments (Brodal & Kawamura 1980; Campbell & Armstrong 1983) and a more patchy organization apparent with physiological mapping (Robertson 1987). The results suggest that several unique features of the olivocerebellar circuit may contribute to the appearance of zonal organization using anatomical techniques, but that the detailed patterns of patchy tactile projections seen with physiological techniques are a more accurate representation of the afferent organization of this region of cortex.
Stochastic fluctuations of voltage-gated ion channels generate current and voltage noise in neuronal membranes. This noise may be a critical determinant of the efficacy of information processing within neuralsystems....
Stochastic fluctuations of voltage-gated ion channels generate current and voltage noise in neuronal membranes. This noise may be a critical determinant of the efficacy of information processing within neuralsystems. Using Monte-Carlo simulations, we carry out a systematic investigation of the relationship between channel kinetics and the resulting membrane voltage noise using a stochastic Markov version of the Mainen-Sejnowski model of dendritic excitability in cortical neurons. Our simulations show that kinetic parameters which lead to an increase in membrane excitability (increasing channel densities, decreasing temperature) also lead to an increase in the magnitude of the subthreshold voltage noise. Noise also increases as the membrane is depolarized from rest towards threshold. This suggests that channel fluctuations may interfere with a neuron's ability to function as an integrator of its synaptic inputs and may limit the reliability and precision of neural information processing.
The real time computation of motion from real images using a single chip with integrated sensors is a hard problem. We present two analog VLSI schemes that use pulse domain neuromorphic circuits to compute motion. Pul...
ISBN:
(纸本)9781558602748
The real time computation of motion from real images using a single chip with integrated sensors is a hard problem. We present two analog VLSI schemes that use pulse domain neuromorphic circuits to compute motion. Pulses of variable width, rather than graded potentials, represent a natural medium for evaluating temporal relationships. Both algorithms measure speed by timing a moving edge in the image. Our first model is inspired by Reichardt's algorithm in the fly and yields a non-monotonic response vs. velocity curve. We present data from a chip that implements this model. Our second algorithm yields a monotonic response vs. velocity curve and is currently being translated into silicon.
It is well-known that neural responses in particular brain regions are spatially organized, but no general principles have been developed that relate the structure of a brain map to the nature of the associated comput...
It is well-known that neural responses in particular brain regions are spatially organized, but no general principles have been developed that relate the structure of a brain map to the nature of the associated computation. On parallel computers, maps of a sort quite similar to brain maps arise when a computation is distributed across multiple processors. In this paper we will discuss the relationship between maps and computations on these computers and suggest how similar considerations might also apply to maps in the brain.
Biological retinas extract spatial and temporal features in an attempt to reduce the complexity of performing visual tasks. We have built and tested a silicon retina which encodes several useful temporal features foun...
ISBN:
(纸本)9781558602229
Biological retinas extract spatial and temporal features in an attempt to reduce the complexity of performing visual tasks. We have built and tested a silicon retina which encodes several useful temporal features found in vertebrate retinas. The cells in our silicon retina are selective to direction, highly sensitive to positive contrast changes around an ambient light level, and tuned to a particular velocity. Inhibitory connections in the null direction perform the direction selectivity we desire. This silicon retina is on a 4.6 × 6.8mm die and consists of a 47 × 41 array of photoreceptors.
It has been known for many years that specific regions of the working cerebral cortex display periodic variations in correlated cellular activity. While the olfactory system has been the focus of much of this work, si...
It has been known for many years that specific regions of the working cerebral cortex display periodic variations in correlated cellular activity. While the olfactory system has been the focus of much of this work, similar behavior has recently been observed in primary visual cortex. We have developed models of both the olfactory and visual cortex which replicate the observed oscillatory properties of these networks. Using these models we have examined the dependence of oscillatory behavior on single cell properties and network architectures. We discuss the idea that the oscillatory events recorded from cerebral cortex may be intrinsic to the architecture of cerebral cortex as a whole, and that these rhythmic patterns may be important in coordinating neuronal activity during sensory processing.
Signal processing and classification algorithms often have limited applicability resulting from an inaccurate model of the signal's underlying structure. We present here an efficient, Bayesian algorithm for modeli...
Signal processing and classification algorithms often have limited applicability resulting from an inaccurate model of the signal's underlying structure. We present here an efficient, Bayesian algorithm for modeling a signal composed of the superposition of brief, Poisson-distributed functions. This methodology is applied to the specific problem of modeling and classifying extracellular neural waveforms which are composed of a superposition of an unknown number of action potentials (APs). Previous approaches have had limited success due largely to the problems of determining the spike shapes, deciding how many are shapes distinct, and decomposing overlapping APs. A Bayesian solution to each of these problems is obtained by inferring a probabilistic model of the waveform. This approach quantifies the uncertainty of the form and number of the inferred AP shapes and is used to obtain an efficient method for decomposing complex overlaps. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.
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