Nonlinear AND interactions between frequency components and the selective processing of intrinsically two-dimensional signals by cortical neurons

2001 ◽  
Author(s):  
Christoph Zetzsche ◽  
Gerhard Krieger ◽  
Gerd Mayer
Keyword(s):  
Cortical Neurons ◽  
Two Dimensional ◽  
Frequency Components ◽  
Selective Processing

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

Two-dimensional receptive-field organization in striate cortical neurons of the cat

1994 ◽  
Vol 11 (4) ◽  
pp. 703-720 ◽  
Author(s):  
Ming Sun ◽  
A. B. Bonds
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Ocular Dominance ◽  
Receptive Fields ◽  
Field Size ◽  
Two Dimensional ◽  
Cortical Layers ◽  
Field Organization ◽  
Receptive Field Organization ◽  
Cortical Receptive Fields

AbstractThe two-dimensional organization of receptive fields (RFs) of 44 cells in the cat visual cortex and four cells from the cat LGN was measured by stimulation with either dots or bars of light. The light bars were presented in different positions and orientations centered on the RFs. The RFs found were arbitrarily divided into four general types: Punctate, resembling DOG filters (11%); those resembling Gabor filters (9%); elongate (36%); and multipeaked-type (44%). Elongate RFs, usually found in simple cells, could show more than one excitatory band or bifurcation of excitatory regions. Although regions inhibitory to a given stimulus transition (e.g. ON) often coincided with regions excitatory to the opposite transition (e.g. OFF), this was by no means the rule. Measurements were highly repeatable and stable over periods of at least 1 h. A comparison between measurements made with dots and with bars showed reasonable matches in about 40% of the cases. In general, bar-based measurements revealed larger RFs with more structure, especially with respect to inhibitory regions. Inactivation of lower cortical layers (V-VI) by local GABA injection was found to reduce sharpness of detail and to increase both receptive-field size and noise in upper layer cells, suggesting vertically organized RF mechanisms. Across the population, some cells bore close resemblance to theoretically proposed filters, while others had a complexity that was clearly not generalizable, to the extent that they seemed more suited to detection of specific structures. We would speculate that the broadly varying forms of cat cortical receptive fields result from developmental processes akin to those that form ocular-dominance columns, but on a smaller scale.

scholarly journals Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1405 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Computational Time ◽  
Support Vector ◽  
Two Dimensional ◽  
Significant Information ◽  
Dct Domain ◽  
Frequency Components

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

Prediction of Complex Two-Dimensional Trajectories by a Cerebellar Model of Smooth Pursuit Eye Movement

1997 ◽  
Vol 77 (4) ◽  
pp. 2115-2130 ◽  
Author(s):  
R. E. Kettner ◽  
S. Mahamud ◽  
H.-C. Leung ◽  
N. Sitkoff ◽  
J. C. Houk ◽  
...  
Keyword(s):  
Eye Movement ◽  
Visual Feedback ◽  
Smooth Pursuit ◽  
Low Frequency ◽  
Learning Rule ◽  
Two Dimensional ◽  
Circular Trajectory ◽  
Smooth Pursuit Eye Movement ◽  
Small Phase ◽  
Frequency Components

Kettner, R. E., S. Mahamud, H.-C. Leung, N. Sitkoff, J. C. Houk, B. W. Peterson, and A. G. Barto. Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement. J. Neurophysiol. 77: 2115–2130, 1997. A neural network model based on the anatomy and physiology of the cerebellum is presented that can generate both simple and complex predictive pursuit, while also responding in a feedback mode to visual perturbations from an ongoing trajectory. The model allows the prediction of complex movements by adding two features that are not present in other pursuit models: an array of inputs distributed over a range of physiologically justified delays, and a novel, biologically plausible learning rule that generated changes in synaptic strengths in response to retinal slip errors that arrive after long delays. To directly test the model, its output was compared with the behavior of monkeys tracking the same trajectories. There was a close correspondence between model and monkey performance. Complex target trajectories were created by summing two or three sinusoidal components of different frequencies along horizontal and/or vertical axes. Both the model and the monkeys were able to track these complex sum-of-sines trajectories with small phase delays that averaged 8 and 20 ms in magnitude, respectively. Both the model and the monkeys showed a consistent relationship between the high- and low-frequency components of pursuit: high-frequency components were tracked with small phase lags, whereas low-frequency components were tracked with phase leads. The model was also trained to track targets moving along a circular trajectory with infrequent right-angle perturbations that moved the target along a circle meridian. Before the perturbation, the model tracked the target with very small phase differences that averaged 5 ms. After the perturbation, the model overshot the target while continuing along the expected nonperturbed circular trajectory for 80 ms, before it moved toward the new perturbed trajectory. Monkeys showed similar behaviors with an average phase difference of 3 ms during circular pursuit, followed by a perturbation response after 90 ms. In both cases, the delays required to process visual information were much longer than delays associated with nonperturbed circular and sum-of-sines pursuit. This suggests that both the model and the eye make short-term predictions about future events to compensate for visual feedback delays in receiving information about the direction of a target moving along a changing trajectory. In addition, both the eye and the model can adjust to abrupt changes in target direction on the basis of visual feedback, but do so after significant processing delays.

Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. I. Stimulus-response relations

1990 ◽  
Vol 64 (2) ◽  
pp. 351-369 ◽  
Author(s):  
B. J. Richmond ◽  
L. M. Optican ◽  
H. Spitzer
Keyword(s):  
Visual Processing ◽  
Cortical Neurons ◽  
Two Dimensional ◽  
Temporal Modulation ◽  
Gray Background ◽  
Complex Cells ◽  
Stimulus Response ◽  
Black And White ◽  
Stimulus Set ◽  
Simple And Complex Cells

1. Previously we developed a new approach for investigating visual system neuronal activity in which single neurons are considered to be communication channels transmitting stimulus-dependent codes in their responses. Application of this approach to the stimulus-response relations of inferior temporal (IT) neurons showed that these carry stimulus-dependent information in the temporal modulation as well as in the strength of their responses. IT cortex is a late station in the visual processing stream. Presumably the neuronal properties arise from the properties of the inputs. However, the discovery that IT neuronal spike trains transmit information in stimulus-dependent temporally modulated codes could not be assumed to be true for those earlier stations, so the techniques used in the earlier study were applied to single-striate cortical neurons in the studies reported here. 2. Single-striate cortical neurons were recorded from three awake, fixating rhesus monkeys. The neurons were stimulated by two sets of patterns. The first set was made up of 128 black-and-white patterns based on a complete, orthogonal set of two-dimensional Walsh-Hadamard functions. These stimuli appear as combinations of black-and-white rectangles and squares, and they fully span the range of all possible black-and-white pictures that can be constructed in an 8 x 8 grid. Except for the stimulus that appeared as an all-white or all-black square, each stimulus had equal areas of white and black. The second stimulus set was made up of single bars constructed in the same 8 x 8 grid as the Walsh stimuli. These were presented both as black against a gray background and white against a gray background. The stimuli were centered on the receptive field, and each member of the stimulus set was presented once before any stimulus appeared again. 3. The responses of 21 striate cortical neurons were recorded and analyzed. Two were identified as simple cells and the other 19 as complex cells according to the criteria originally used by Hubel and Wiesel. The stimulus set elicited a wide variety of response strengths and patterns from each neuron. The responses from both the bars and the Walsh set could be used to differentiate and classify simple and complex cells. 4. The responses of both simple and complex cells showed striking stimulus-related strength and temporal modulation. For all of the complex cells there were instances where the responses to a stimulus and its contrast-reversed mate were substantially different in response strength or pattern, or both.(ABSTRACT TRUNCATED AT 400 WORDS)

A Numerical Analysis of Phonation Using a Two-Dimensional Flexible Channel Model of the Vocal Folds

2001 ◽  
Vol 123 (6) ◽  
pp. 571-579 ◽  
Author(s):  
Tadashige Ikeda ◽  
Yuji Matsuzaki ◽  
Tatsuya Aomatsu
Keyword(s):  
Channel Model ◽  
Flow Direction ◽  
Speech Sound ◽  
Three Dimensional ◽  
Vocal Folds ◽  
Two Dimensional ◽  
Threshold Values ◽  
One Dimensional ◽  
Frequency Components ◽  
Main Flow Direction

A two-dimensional flexible channel model of the vocal folds coupled with an unsteady one-dimensional flow model is presented for an analysis of the mechanism of phonation. The vocal fold is approximated by springs and dampers distributed in the main flow direction that are enveloped with an elastic cover. In order to approximate three-dimensional collision of the vocal folds using the two-dimensional model, threshold values for the glottal width are introduced. The numerical results show that the collision plays an important role in speech sound, especially for higher resonant frequency components, because it causes the source sound to include high-frequency components.

SPATIAL, TEMPORAL, AND GLOBAL MODE ENTROPY IN A THALAMO–CORTICAL NETWORK

1993 ◽  
Vol 03 (06) ◽  
pp. 1487-1501 ◽  
Author(s):  
GENE V. WALLENSTEIN
Keyword(s):  
Cortical Neurons ◽  
Space Time ◽  
Time Behavior ◽  
Two Dimensional ◽  
Van Der Pol ◽  
Cortical Cells ◽  
Global Mode ◽  
Time Symmetry ◽  
Symmetry Properties ◽  
Cortical System

We employ an orthogonal decomposition technique with unique space–time symmetry properties to analyze a network of thalamo–cortical oscillators. A small number of thalamic "cells" are used to drive a network of cortical cells structured on a two-dimensional lattice. Two Bonhoeffer–van der Pol (BVP) based models of cortical neurons are compared at the network level in an attempt to reproduce some features of the thalamo–cortical system. It is shown that with the addition of a slowly varying term to the classical two-dimensional BVP model, the network can exhibit both periodic and irregular space–time behavior, along with changes in the temporal coherence and spatial frequency resembling the 8–12 Hz alpha rhythm.

scholarly journals The posterior vermis of the cerebellum selectively inhibits 10-Hz sympathetic nerve discharge in anesthetized cats

2009 ◽  
Vol 297 (1) ◽  
pp. R210-R217 ◽  
Author(s):  
Susan M. Barman ◽  
Gerard L. Gebber
Keyword(s):  
Sympathetic Nerve ◽  
Cortical Neurons ◽  
Fold Increase ◽  
Total Power ◽  
Chemical Inactivation ◽  
Naturally Occurring ◽  
Functional Implications ◽  
Frequency Components ◽  
Nerve Discharge

We studied the changes in inferior cardiac sympathetic nerve discharge (SND) and mean arterial pressure (MAP) produced by aspiration or chemical inactivation (muscimol microinjection) of lobule IX (uvula) of the posterior vermis of the cerebellum in baroreceptor-denervated and baroreceptor-innervated cats anesthetized with urethane. Autospectral analysis was used to decompose SND into its frequency components. Special attention was paid to the question of whether the experimental procedures affected the rhythmic (10-Hz and cardiac-related) components of SND. Aspiration or chemical inactivation of lobule IX produced an approximately three-fold increase in the 10-Hz rhythmic component of SND ( P ≤ 0.05) in baroreceptor-denervated cats. Total power (0- to 20-Hz band) was unchanged. Despite the absence of a change in total power in SND, there was a statistically significant increase in MAP. In baroreceptor-innervated cats, neither aspiration nor chemical inactivation of the uvula caused a significant change in cardiac-related or total power in SND or MAP. These results are the first to demonstrate a role of cerebellar cortical neurons of the posterior vermis in regulating the frequency composition of naturally occurring SND. Specifically, these neurons selectively inhibit the 10-Hz rhythm-generating network in baroreceptor-denervated, urethane-anesthetized cats. The functional implications of these findings are discussed.

INTEGRATING NEUROMORPHIC ACTION-ORIENTED PERCEPTUAL INPUTS TO GENERATE A NAVIGATION BEHAVIOUR FOR A ROBOT

1999 ◽  
Vol 09 (05) ◽  
pp. 411-416 ◽  
Author(s):  
R. MUDRA ◽  
R. HAHNLOSER ◽  
R. J. DOUGLAS
Keyword(s):  
Neural Networks ◽  
Feedback Inhibition ◽  
Ccd Camera ◽  
Two Dimensional ◽  
Infrared Sensors ◽  
Attentional Processing ◽  
One Dimensional ◽  
Control Signals ◽  
Selective Processing ◽  
Salient Stimulus

We use neural networks with pointer map architectures to provide simple attentional processing in a robotic task. A pointer map comprises a map of neurons that encode a stimulus. Besides global feedback inhibition, the map receives feedback excitation via a smalls group of pointer neurons that encode the location of a salient stimulus on the map as a vectorial representation. The pointer neurons are able to apply selective processing to a particular region of the network. The robot uses these properties to manoeuver in relation to an attended object. We implemented a controller composed of two pointer maps, and a motor map. The first pointer map reports the direction of a salient obstacle in a one-dimensional map of distance derived from infrared sensors. The second pointer map reports the direction to potential obstacles in a two-dimensional edge-enhanced image derived from a forward looking CCD-camera. These outputs are applied to a motor map, where they bias the motor control signals issued to the robots wheels, according to navigational intentions.

scholarly journals Resonating neurons stabilize heterogeneous grid-cell networks

2020 ◽  
Author(s):  
Divyansh Mittal ◽  
Rishikesh Narayanan
Keyword(s):  
Neural Activity ◽  
Biological Networks ◽  
Spatial Information ◽  
Physiological Mechanism ◽  
Low Frequency ◽  
Two Dimensional ◽  
Attractor Network ◽  
Network Function ◽  
Frequency Components ◽  
The Impact

ABSTRACTGrid cells in the medial entorhinal cortex manifest multiple firing fields, patterned to tessellate external space with triangles. Although two-dimensional continuous attractor network (CAN) models have offered remarkable insights about grid-patterned activity generation, their functional stability in the presence of biological heterogeneities remains unexplored. In this study, we systematically incorporated three distinct forms of intrinsic and synaptic heterogeneities into a rate-based CAN model driven by virtual trajectories, developed here to mimic animal traversals and improve computational efficiency. We found that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in neural activity. Quantitatively, grid score and spatial information associated with neural activity reduced progressively with increasing degree of heterogeneities, and perturbations were primarily confined to low-frequency neural activity. We postulated that suppressing low-frequency perturbations could ameliorate the disruptive impact of heterogeneities on grid-patterned activity. To test this, we formulated a strategy to introduce intrinsic neuronal resonance, a physiological mechanism to suppress low-frequency activity, in our rate-based neuronal model by incorporating filters that mimicked resonating conductances. We confirmed the emergence of grid-patterned activity in homogeneous CAN models built with resonating neurons and assessed the impact of heterogeneities on these models. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing, through suppression of low-frequency components in neural activity. Our analyses suggest a universal role for intrinsic neuronal resonance, an established mechanism in biological neurons to suppress low-frequency neural activity, in stabilizing heterogeneous network physiology.SIGNIFICANCE STATEMENTA central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. However, several theoretical and modeling frameworks employ unnatural homogeneous networks in assessing network function owing to the enormous analytical or computational costs involved in assessing heterogeneous networks. Here, we investigate the impact of biological heterogeneities on a powerful two-dimensional continuous attractor network implicated in the emergence of patterned neural activity. We show that network function is disrupted by biological heterogeneities, but is stabilized by intrinsic neuronal resonance, a physiological mechanism that suppresses low-frequency perturbations. As low-frequency perturbations are pervasive across biological systems, mechanisms that suppress low-frequency components could form a generalized route to stabilize heterogeneous biological networks.

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