scholarly journals Learning receptive field properties of complex cells in V1

2021 ◽  
Vol 17 (3) ◽  
pp. e1007957
Author(s):  
Yanbo Lian ◽  
Ali Almasi ◽  
David B. Grayden ◽  
Tatiana Kameneva ◽  
Anthony N. Burkitt ◽  
...  
Keyword(s):  
Cell Model ◽  
Classical Model ◽  
Experimental Studies ◽  
Learning Rule ◽  
Energy Model ◽  
Diverse Range ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Wide Range

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.

scholarly journals Learning receptive field properties of complex cells in V1

2020 ◽  
Author(s):  
Yanbo Lian ◽  
Ali Almasi ◽  
David B. Grayden ◽  
Tatiana Kameneva ◽  
Anthony N. Burkitt ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Experimental Studies ◽  
Learning Rule ◽  
Energy Model ◽  
Learning Ability ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Model Complex ◽  
The Brain

AbstractThere are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.Author summaryMany cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.

Spatial Phase and the Temporal Structure of the Response to Gratings in V1

1998 ◽  
Vol 80 (2) ◽  
pp. 554-571 ◽  
Author(s):  
Jonathan D. Victor ◽  
Keith P. Purpura
Keyword(s):  
Spatial Frequency ◽  
Spike Train ◽  
Temporal Structure ◽  
Spike Timing ◽  
Dependent Manner ◽  
Data Set ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Contrast Information

Victor, Jonathan D. and Keith P. Purpura. Spatial phase and the temporal structure of the response to gratings in V1. J. Neurophysiol. 80: 554–571, 1998. We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25–50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the “pooled phases” approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index H pooled. In the “individual phases” approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index H indiv. H pooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas H indiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.

Half-squaring in responses of cat striate cells

1992 ◽  
Vol 9 (5) ◽  
pp. 427-443 ◽  
Author(s):  
David J. Heeger
Keyword(s):  
Linear Operator ◽  
Firing Rate ◽  
Striate Cortex ◽  
Energy Model ◽  
Linear Operators ◽  
Linear Stage ◽  
Simple Cells ◽  
Complex Cells ◽  
Cell Responses

AbstractSimple cells in striate cortex have been depicted as rectified linear operators, and complex cells have been depicted as energy mechanisms (constructed from the squared sums of linear operator outputs). This paper discusses two essential hypotheses of the linear/energy model: (1) that a cell's selectivity is due to an underlying (spatiotemporal and binocular) linear stage; and (2) that a cell's firing rate depends on the squared output of the underlying linear stage. This paper reviews physiological measurements of cat striate cell responses, and concludes that both of these hypotheses are supported by the data.

scholarly journals Comparison Among Some Models of Orientation Selectivity

2006 ◽  
Vol 96 (1) ◽  
pp. 404-419 ◽  
Author(s):  
Andrew F. Teich ◽  
Ning Qian
Keyword(s):  
Peak Shift ◽  
Orientation Tuning ◽  
Complex Spectrum ◽  
Complex Cell ◽  
Phase Information ◽  
Simple Complex ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Opposite Contrast

Several models exist for explaining primary visual cortex (V1) orientation tuning. The modified feedforward model (MFM) and the recurrent model (RM) are major examples. We have implemented these two models, at the same level of detail, alongside a few newer variations, and thoroughly compared their receptive-field structures. We found that antiphase inhibition in the MFM enhances both spatial phase information and orientation tuning, producing well-tuned simple cells. This remains true for a newer version of the MFM that incorporates untuned complex-cell inhibition. In contrast, when the recurrent connections in the RM are strong enough to produce typical V1 orientation tuning, they also eliminate spatial phase information, making the cells complex. Introducing phase specificity into the connections of the RM (as done in an original version of the RM) can make the cells phase sensitive, but the cells show an incorrect 90° peak shift of orientation tuning under opposite contrast signs. An inhibition-dominant version of the RM can generate well-tuned cells across the simple–complex spectrum, but it predicts that the net effect of cortical interactions is to suppress feedforward excitation across all orientations in simple cells. Finally, adding antiphase inhibition used in the MFM into the RM produces a most general model. We call this new model the modified recurrent model (MRM) and show that this model can also produce well-tuned cells throughout the simple–complex spectrum. Unlike the inhibition-dominant RM, the MRM is consistent with data from cat V1, suggesting that the net effect of cortical interactions is to boost simple cell responses at the preferred orientation. These results suggest that the MFM is well suited for explaining orientation tuning in simple cells, whereas the standard RM is for complex cells. The assignment of the RM to complex cells also avoids conflicts between the RM and the experiments of cortical inactivation (done on simple cells) and the spatial-frequency dependency of orientation tuning (found in simple cells). Because orientation-tuned V1 cells show a continuum of simple- to complex-cell behavior, the MRM provides the best description of V1 data.

scholarly journals New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

2021 ◽  
Vol 19 (3) ◽  
pp. 50-60
Author(s):  
A. V. Kugaevskikh
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gabor Filter ◽  
Cell Model ◽  
Complex Cell ◽  
Simple Cells ◽  
Complex Cells ◽  
Accurate Model ◽  
Proposed Model ◽  
Simple And Complex Cells

This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.

scholarly journals Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

2015 ◽  
Vol 114 (6) ◽  
pp. 3326-3338 ◽  
Author(s):  
H. Meffin ◽  
M. A. Hietanen ◽  
S. L. Cloherty ◽  
M. R. Ibbotson
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Phase Sensitivity ◽  
Simple Cells ◽  
Low Contrast ◽  
Complex Cells ◽  
Spatial Phase ◽  
Phase Sensitive

Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434–444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155–1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457–3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.

scholarly journals Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Brian Gardner ◽  
André Grüning
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Experimental Studies ◽  
Learning Rule ◽  
Image Data ◽  
Classification Performance ◽  
Spiking Neural Networks ◽  
Classification Problems ◽  
Sensory Stimuli ◽  
Wide Range

Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. Accordingly, it would be desirable to apply spike-based computation to tackling real-world challenges, and in particular transferring such theory to neuromorphic systems for low-power embedded applications. Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy. The proposed learning rule supports multiple spikes fired by stochastic hidden neurons, and yet is stable by relying on first-spike responses generated by a deterministic output layer. In addition to this, we also explore several distinct, spike-based encoding strategies in order to form compact representations of presented input data. We demonstrate the classification performance of the learning rule as applied to several benchmark datasets, including MNIST. The learning rule is capable of generalizing from the data, and is successful even when used with constrained network architectures containing few input and hidden layer neurons. Furthermore, we highlight a novel encoding strategy, termed “scanline encoding,” that can transform image data into compact spatiotemporal patterns for subsequent network processing. Designing constrained, but optimized, network structures and performing input dimensionality reduction has strong implications for neuromorphic applications.

scholarly journals Mechanistic underpinning of nonuniform collective motions in swarming bacteria

2021 ◽  
Author(s):  
Palash Bera ◽  
Abdul Wasim ◽  
Jagannath Mondal ◽  
Pushpita Ghosh
Keyword(s):  
Aspect Ratio ◽  
Cell Size ◽  
Joint Venture ◽  
Experimental Studies ◽  
Diverse Range ◽  
Agent Based ◽  
Monotonic Trend ◽  
Self Organized ◽  
Wide Range ◽  
Collective Motions

AbstractSelf-propelled bacteria can exhibit a large variety of non-equilibrium self-organized phenomena. Swarming is one such fascinating dynamical scenario where a number of motile individuals grouped into clusters and move in synchronized flows and vortices. While precedent investigations in rod-like particles confirm that increased aspect-ratio promotes alignment and order, recent experimental studies in bacteria Bacillus subtilis show a non-monotonic dependence of cell-aspect ratio on their swarming motion. Here, by computer simulations of an agent-based model of selfpropelled, mechanically interacting, rod-shaped bacteria in overdamped condition, we explore the collective dynamics of bacterial swarm subjected to a variation of cell-aspect ratio. When modeled with an identical self-propulsion speed across a diverse range of cell aspect ratio, simulations demonstrate that both shorter and longer bacteria exhibit slow dynamics whereas the fastest speed is obtained at an intermediate aspect ratio. Our investigation highlights that the origin of this observed non-monotonic trend of bacterial speed and vorticity with cell-aspect ratio is rooted in the cell-size dependence of motility force. The swarming features remain robust for a wide range of surface density of the cells, whereas asymmetry in friction attributes a distinct effect. Our analysis identifies that at an intermediate aspect ratio, an optimum cell size and motility force promote alignment, which reinforces the mechanical interactions among neighboring cells leading to the overall fastest motion. Mechanistic underpinning of the collective motions reveals that it is a joint venture of the short-range repulsive and the size-dependent motility forces, which determines the characteristics of swarming.

scholarly journals Binocular Spatial Phase Tuning Characteristics of Neurons in the Macaque Striate Cortex

1997 ◽  
Vol 78 (1) ◽  
pp. 351-365 ◽  
Author(s):  
Earl L. Smith ◽  
Yuzo M. Chino ◽  
Jinren Ni ◽  
William H. Ridder ◽  
M.L.J. Crawford
Keyword(s):  
Spatial Frequency ◽  
Striate Cortex ◽  
Orientation Tuning ◽  
Absolute Position ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Binocular Interactions ◽  
Phase Tuning ◽  
Phase Sensitive

Smith, Earl L., III, Yuzo M. Chino, Jinren Ni, William H. Ridder III, and M.L.J. Crawford. Binocular spatial phase tuning characteristics of neurons in the macaque striate cortex. J. Neurophysiol. 78: 351–365, 1997. We employed microelectrode recording techniques to study the sensitivity of individual neurons in the striate cortex of anesthetized and paralyzed monkeys to relative interocular image disparities and to determine the effects of basic stimulus parameters on these cortical binocular interactions. The visual stimuli were drifting sine wave gratings. After the optimal stimulus orientation, spatial frequency, and direction of stimulus movement were found, the cells' disparity tuning characteristics were determined by measuring responses as a function of the relative interocular spatial phase of dichoptic grating pairs. No attempts were made to assess absolute position disparities or horizontal disparities relative to the horopter. The majority (∼70%) of simple cells were highly sensitive to interocular spatial phase disparities, particularly neurons with balanced ocular dominances. Simple cells typically demonstrated binocular facilitation at the optimal phase disparity and binocular suppression for disparities 180° away. Fewer complex cells were phase selective (∼40%); however, the range of disparity selectivity in phase-sensitive complex cells was comparable with that for simple cells. Binocular interactions in non-phase-sensitive complex cells were evidenced by binocular response amplitudes that differed from responses to monocular stimulation. The degree of disparity tuning was independent of a cell's optimal orientation or the degree of direction tuning. However, disparity-sensitive cells tended to have narrow orientation tuning functions and the degree of disparity tuning was greatest for the optimal stimulus orientations. Rotating the stimulus for one eye 90° from the optimal orientation usually eliminated binocular interactions. The effects of phase disparities on the binocular response amplitude were also greatest at the optimal spatial frequency. Thus a cell's sensitivity to absolute position disparities reflects its spatial tuning characteristics, with cells sensitive to high spatial frequencies being capable of signaling very small changes in image disparity. On the other hand, stimulus contrast had relatively little effect on a cell's disparity tuning, because response saturation occurred at the same contrast level for all relative interocular phase disparities. Thus, as with orientation tuning, a cell's optimal disparity and the degree of disparity selectivity were invariant with contrast. Overall, the results show that sensitivity to interocular spatial phase disparities is a common property of striate neurons. A cell's disparity tuning characteristics appear to largely reflect its monocular receptive field properties and the interocular balance between excitatory and inhibitory inputs. However, distinct functional classes of cortical neurons could not be discriminated on the basis of disparity sensitivity alone.

Nine Centuries of Man

2017 ◽  
Keyword(s):  
Twentieth Century ◽  
Diverse Range ◽  
Gender Expectations ◽  
Court Records ◽  
The Past ◽  
Wide Range ◽  
Police Reports ◽  
Late Twentieth ◽  
Scottish History ◽  
Late Twentieth Century

What did it mean to be a man in Scotland over the past nine centuries? Scotland, with its stereotypes of the kilted warrior and the industrial ‘hard man’, has long been characterised in masculine terms, but there has been little historical exploration of masculinity in a wider context. This interdisciplinary collection examines a diverse range of the multiple and changing forms of masculinities from the late eleventh to the late twentieth century, exploring the ways in which Scottish society through the ages defined expectations for men and their behaviour. How men reacted to those expectations is examined through sources such as documentary materials, medieval seals, romances, poetry, begging letters, police reports and court records, charity records, oral histories and personal correspondence. Focusing upon the wide range of activities and roles undertaken by men – work, fatherhood and play, violence and war, sex and commerce – the book also illustrates the range of masculinities that affected or were internalised by men. Together, the chapters illustrate some of the ways Scotland’s gender expectations have changed over the centuries and how, more generally, masculinities have informed the path of Scottish history

Sign in / Sign up

Export Citation Format

Share Document