scholarly journals Learning simple and complex cells-like receptive fields from natural images: a plausibility proof

2010 ◽  
Vol 7 (9) ◽  
pp. 81-81 ◽  
Author(s):  
T. Masquelier ◽  
T. Serre ◽  
S. Thorpe ◽  
T. Poggio
Keyword(s):  
Receptive Fields ◽  
Natural Images ◽  
Complex Cells ◽  
Simple And Complex Cells

scholarly journals Spatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17

2010 ◽  
Vol 103 (2) ◽  
pp. 677-697 ◽  
Author(s):  
Lionel G. Nowak ◽  
Maria V. Sanchez-Vives ◽  
David A. McCormick
Keyword(s):  
Spatial Frequency ◽  
Response Latency ◽  
Frequency Tuning ◽  
Receptive Fields ◽  
Width Ratio ◽  
Area 17 ◽  
Simple Cells ◽  
Complex Cells ◽  
Temporal Features ◽  
Simple And Complex Cells

The aim of the present study was to characterize the spatial and temporal features of synaptic and discharge receptive fields (RFs), and to quantify their relationships, in cat area 17. For this purpose, neurons were recorded intracellularly while high-frequency flashing bars were used to generate RFs maps for synaptic and spiking responses. Comparison of the maps shows that some features of the discharge RFs depended strongly on those of the synaptic RFs, whereas others were less dependent. Spiking RF duration depended poorly and spiking RF amplitude depended moderately on those of the underlying synaptic RFs. At the other extreme, the optimal spatial frequency and phase of the discharge RFs in simple cells were almost entirely inherited from those of the synaptic RFs. Subfield width, in both simple and complex cells, was less for spiking responses compared with synaptic responses, but synaptic to discharge width ratio was relatively variable from cell to cell. When considering the whole RF of simple cells, additional variability in width ratio resulted from the presence of additional synaptic subfields that remained subthreshold. Due to these additional, subthreshold subfields, spatial frequency tuning predicted from synaptic RFs appears sharper than that predicted from spiking RFs. Excitatory subfield overlap in spiking RFs was well predicted by subfield overlap at the synaptic level. When examined in different regions of the RF, latencies appeared to be quite variable, but this variability showed negligible dependence on distance from the RF center. Nevertheless, spiking response latency faithfully reflected synaptic response latency.

scholarly journals Substructure of Direction-Selective Receptive Fields in Macaque V1

2003 ◽  
Vol 89 (5) ◽  
pp. 2743-2759 ◽  
Author(s):  
Margaret S. Livingstone ◽  
Bevil R. Conway
Keyword(s):  
Receptive Fields ◽  
Direction Selectivity ◽  
Positive Interactions ◽  
Null Direction ◽  
Simple Cells ◽  
Complex Cells ◽  
Preferred Direction ◽  
Orientation Axis ◽  
Interaction Regions ◽  
Simple And Complex Cells

We used two-dimensional (2-D) sparse noise to map simultaneous and sequential two-spot interactions in simple and complex direction-selective cells in macaque V1. Sequential-interaction maps for both simple and complex cells showed preferred-direction facilitation and null-direction suppression for same-contrast stimulus sequences and the reverse for inverting-contrast sequences, although the magnitudes of the interactions were weaker for the simple cells. Contrast-sign selectivity in complex cells indicates that direction-selective interactions in these cells must occur in antecedent simple cells or in simple-cell-like dendritic compartments. Our maps suggest that direction selectivity, and on andoff segregation perpendicular to the orientation axis, can occur prior to receptive-field elongation along the orientation axis. 2-D interaction maps for some complex cells showed elongated alternating facilitatory and suppressive interactions as predicted if their inputs were orientation-selective simple cells. The negative interactions, however, were less elongated than the positive interactions, and there was an inflection at the origin in the positive interactions, so the interactions were chevron-shaped rather than band-like. Other complex cells showed only two round interaction regions, one negative and one positive. Several explanations for the map shapes are considered, including the possibility that directional interactions are generated directly from unoriented inputs.

Cone Inputs to Simple and Complex Cells in V1 of Awake Macaque

2007 ◽  
Vol 97 (4) ◽  
pp. 3070-3081 ◽  
Author(s):  
Gregory D. Horwitz ◽  
E. J. Chichilnisky ◽  
Thomas D. Albright
Keyword(s):  
Linear Models ◽  
Noise Analysis ◽  
Receptive Fields ◽  
Light Responses ◽  
Simple Cells ◽  
Complex Cells ◽  
V1 Neurons ◽  
Spike Triggered Averaging ◽  
Simple And Complex Cells ◽  
Simple And Complex Cell

Rules by which V1 neurons combine signals originating in the cone photoreceptors are poorly understood. We measured cone inputs to V1 neurons in awake, fixating monkeys with white-noise analysis techniques that reveal properties of light responses not revealed by purely linear models used in previous studies. Simple cells were studied by spike-triggered averaging that is robust to static nonlinearities in spike generation. This analysis revealed, among heterogeneously tuned neurons, two relatively discrete categories: one with opponent L- and M-cone weights and another with nonopponent cone weights. Complex cells were studied by spike-triggered covariance, which identifies features in the stimulus sequence that trigger spikes in neurons with receptive fields containing multiple linear subunits that combine nonlinearly. All complex cells responded to nonopponent stimulus modulations. Although some complex cells responded to cone-opponent stimulus modulations too, none exhibited the pure opponent sensitivity observed in many simple cells. These results extend the findings on distinctions between simple and complex cell chromatic tuning observed in previous studies in anesthetized monkeys.

Complex Cells Increase Their Phase Sensitivity at Low Contrasts and Following Adaptation

2007 ◽  
Vol 98 (3) ◽  
pp. 1155-1166 ◽  
Author(s):  
N. A. Crowder ◽  
J. van Kleef ◽  
B. Dreher ◽  
M. R. Ibbotson
Keyword(s):  
Receptive Fields ◽  
Phase Sensitivity ◽  
Complex Cells ◽  
Spike Threshold ◽  
Cell Responses ◽  
Layer 4 ◽  
A Cell ◽  
Phase Sensitive ◽  
Simple And Complex Cells ◽  
Mean Time

One of the best-known dichotomies in neuroscience is the division of neurons in the mammalian primary visual cortex into simple and complex cells. Simple cells have receptive fields with separate on and off subregions and give phase-sensitive responses to moving gratings, whereas complex cells have uniform receptive fields and are phase invariant. The phase sensitivity of a cell is calculated as the ratio of the first Fourier coefficient ( F1) to the mean time-average ( F0) of the response to moving sinusoidal gratings at 100% contrast. Cells are then classified as simple ( F1/ F0>1) or complex ( F1/ F0<1). We manipulated cell responses by changing the stimulus contrast or through adaptation. The F1/ F0ratios of cells defined as complex at 100% contrast increased at low contrasts and following adaptation. Conversely, the F1/ F0ratios remained constant for cells defined as simple at 100% contrast. The latter cell type was primarily located in thalamorecipient layers 4 and 6. Many cells initially classified as complex exhibit F1/ F0>1 at low contrasts and after adaptation (particularly in layer 4). The results are consistent with the spike-threshold hypothesis, which suggests that the division of cells into two types arises from the nonlinear interaction of spike threshold with membrane potential responses.

scholarly journals Replicating Receptive Fields of Simple and Complex Cells in Primary Visual Cortex in a Neuronal Network Model with Temporal and Population Sparseness and Reliability

2012 ◽  
Vol 24 (10) ◽  
pp. 2700-2725 ◽  
Author(s):  
Takuma Tanaka ◽  
Toshio Aoyagi ◽  
Takeshi Kaneko
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Learning Rule ◽  
Simple Cell ◽  
Complex Cells ◽  
Receptive Field Properties ◽  
Output Neurons ◽  
Simple And Complex Cells

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

Learning Nonlinear Statistical Regularities in Natural Images by Modeling the Outer Product of Image Intensities

2014 ◽  
Vol 26 (4) ◽  
pp. 693-711 ◽  
Author(s):  
Peng Qi ◽  
Xiaolin Hu
Keyword(s):  
Visual Cortex ◽  
Layer Structure ◽  
Receptive Fields ◽  
Natural Images ◽  
Masking Effect ◽  
Covariance Model ◽  
Outer Product ◽  
Complex Cells ◽  
Statistical Regularities ◽  
Efficient Learning

It is well known that there exist nonlinear statistical regularities in natural images. Existing approaches for capturing such regularities always model the image intensities by assuming a parameterized distribution for the intensities and learn the parameters. In the letter, we propose to model the outer product of image intensities by assuming a gaussian distribution for it. A two-layer structure is presented, where the first layer is nonlinear and the second layer is linear. Trained on natural images, the first-layer bases resemble the receptive fields of simple cells in the primary visual cortex (V1), while the second-layer units exhibit some properties of the complex cells in V1, including phase invariance and masking effect. The model can be seen as an approximation of the covariance model proposed in Karklin and Lewicki ( 2009 ) but has more robust and efficient learning algorithms.

Quantum-like signal-tuned approach to V1 modeling

2019 ◽  
Vol 30 (05) ◽  
pp. 1950032
Author(s):  
José R. A. Torreão ◽  
Marcos S. Amaral
Keyword(s):  
Receptive Fields ◽  
Spatial Summation ◽  
Cortical Cells ◽  
Complex Cells ◽  
Cell Responses ◽  
Gabor Functions ◽  
Half Wave ◽  
Plausible Model ◽  
Simple And Complex Cells ◽  
Carry Over

Signal-tuned Gabor functions — Gaussian-modulated sinusoids whose parameters are determined by a spatial or spectral “tuning signal” — have previously been shown to provide a plausible model for the stimulus-dependent receptive fields and responses of the simple and complex cells of the primary visual cortex (V1). The signal-tuned responses obey Schrödinger equations, which has led to the proposal of a quantum-like model for V1 cells: by considering the squared magnitude of a particular signal-tuned wave function as a probability density, one arrives at a Poisson spiking process which appears consistent with the neurophysiological findings. Here, by incorporating Hermite-polynomial factors to the signal-tuned Gabor functions, we obtain a generalized quantum-like signal-tuned model for which further relevant properties are demonstrated, such as receptive-field coding of the stimulus and its derivatives, saturating spatial summation curves and half-wave rectification of the simple cell responses. Although only a one-dimensional approach is considered here, such properties will carry over to a two-dimensional model, in which case, as our preliminary analysis indicates, end-stopping — another important feature of cortical cells — can also be accommodated.

Signal-Tuned Gabor Functions as Models for Stimulus-Dependent Cortical Receptive Fields

2014 ◽  
Vol 26 (5) ◽  
pp. 920-952 ◽  
Author(s):  
José R. A. Torreão ◽  
Silvia M. C. Victer ◽  
Marcos S. Amaral
Keyword(s):  
Receptive Fields ◽  
Gabor Transform ◽  
Spectral Tuning ◽  
Complex Cells ◽  
Model Based ◽  
Accurate Detection ◽  
Gabor Functions ◽  
Neurophysiological Studies ◽  
Simple And Complex Cells ◽  
Cortical Receptive Fields

We propose and analyze a model, based on signal-tuned Gabor functions, for the receptive fields and responses of V1 cells. Signal-tuned Gabor functions are gaussian-modulated sinusoids whose parameters are obtained from a given, spatial, or spectral “tuning” signal. These functions can be proven to yield exact representations of their tuning signals and have recently been proposed as the kernels of a variant Gabor transform—the signal-tuned Gabor transform (STGT)—which allows the accurate detection of spatial and spectral events. Here we show that by modeling the receptive fields of simple and complex cells as signal-tuned Gabor functions and expressing their responses as STGTs, we are able to replicate the properties of these cells when tested with standard grating and slit inputs, at the same time emulating their stimulus-dependent character as revealed by recent neurophysiological studies.

scholarly journals Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces

2000 ◽  
Vol 12 (7) ◽  
pp. 1705-1720 ◽  
Author(s):  
Aapo Hyvärinen ◽  
Patrik Hoyer
Keyword(s):  
Sparse Coding ◽  
Receptive Fields ◽  
Natural Images ◽  
Simple Cell ◽  
Linear Filter ◽  
Linear Filters ◽  
Complex Cells ◽  
Linear Subspaces ◽  
Invariant Features ◽  
Gabor Functions

Olshausen and Field (1996) applied the principle of independence maximization by sparse coding to extract features from natural images. This leads to the emergence of oriented linear filters that have simultaneous localization in space and in frequency, thus resembling Gabor functions and simple cell receptive fields. In this article, we show that the same principle of independence maximization can explain the emergence of phase- and shift-invariant features, similar to those found in complex cells. This new kind of emergence is obtained by maximizing the independence between norms of projections on linear subspaces (instead of the independence of simple linear filter outputs). The norms of the projections on such “independent feature subspaces” then indicate the values of invariant features.

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