Selective Receptive Fields Research Articles

Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex.

Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

Defocus deblurring: a designed deep model based on CNN

With the flourishing development of deep learning in the field of computer vision, research of defocus deblurring based on it has gradually become a hotspot. However, most of the research focuses on defocus region detection or defocus map estimation, and algorithms for directly generating restoration images are less studied. Stressing on the problems of defocus deblurring, we propose a defocus deblurring deep model based on multi-scale information and convolution neural network. Concretely, we first perform an efficient and concise multi-scale information fusion by the selective receptive field module, thus the model can adapt to the scale sensitivity of the image defocusing region. We then use the residual channel attention module in the bottleneck module to extract the correlation features between channels, which enhances the effective channels and suppress the useless ones. Finally, a fusion objective function of edge loss and mean square loss is proposed to enhance the edge details of the image. Experimental results on a large-scale defocus deblurring dual-pixel dataset demonstrate that the proposed model has better performance than the traditional and existing deep-based methods. Comparing with the methods of the state of the art, the proposed model has a 0.44-DB improvement in PSNR metric.

SRFNet: selective receptive field network for human pose estimation

SRFNet: selective receptive field network for human pose estimation

Spatial clustering of orientation preference in primary visual cortex of the large rodent agouti.

SummaryAll rodents investigated so far possess orientation-selective neurons in the primary visual cortex (V1) but – in contrast to carnivores and primates – no evidence of periodic maps with pinwheel-like structures. Theoretical studies debating whether phylogeny or universal principles determine development of pinwheels point to V1 size as a critical constraint. Thus, we set out to study maps of agouti, a big diurnal rodent with a V1 size comparable to cats'. In electrophysiology, we detected interspersed orientation and direction-selective neurons with a bias for horizontal contours, corroborated by homogeneous activation in optical imaging. Compatible with spatial clustering at short distance, nearby neurons tended to exhibit similar orientation preference. Our results argue against V1 size as a key parameter in determining the presence of periodic orientation maps. They are consistent with a phylogenetic influence on the map layout and development, potentially reflecting distinct retinal traits or interspecies differences in cortical circuitry.

Convergent evolution of face spaces across human face-selective neuronal\xa0groups and deep\xa0convolutional networks

The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pair-wise activation similarities of face-selective neuronal groups recorded intracranially in 33 patients, significantly matches that of a DCNN having human-level face recognition capabilities. This convergent evolution of pattern similarities across biological and artificial networks highlights the significance of face-space geometry in face perception. Furthermore, the nature of the neuronal to DCNN match suggests a role of human face areas in pictorial aspects of face perception. First, the match was confined to intermediate DCNN layers. Second, presenting identity-preserving image manipulations to the DCNN abolished its correlation to neuronal responses. Finally, DCNN units matching human neuronal group tuning displayed view-point selective receptive fields. Our results demonstrate the importance of face-space geometry in the pictorial aspects of human face perception.

Auto-Selecting Receptive Field Network for Visual Tracking

Recently, Convolutional Neural Networks (CNNs) have shown tremendous potential in the visual tracking community. It is well-known that the receptive field is a critical factor for CNN affecting performance. However, standard CNNs based tracking methods design the receptive fields of artificial neurons in each layer that have the same size. We identify the main bottleneck of affecting the tracking accuracy as regular receptive fields. To settle the problem, we propose an Auto-Selecting Receptive Field Network (ASRF) to select receptive field information and effective clues dynamically. In particular, a Selective Receptive Field Block (SRFB) is designed to adaptively adjust receptive field size for each neuron according to multiple scales of input information. Additionally, we develop a Multi-Scale Receptive Field module (MSRF) that marks a further step in selecting effective clues from different scale receptive fields. The proposed ASRF method performs favorably against state-of-the-art trackers on five benchmarks, including OTB-2013, OTB-2015, UAV-123, VOT-2015, and VOT-2017 while running beyond real-time tracking speed.

Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction

Information processing within neural systems often depends upon selective amplification of relevant signals and suppression of irrelevant signals. This has been shown many times by studies of contextual effects but there is as yet no consensus on how to interpret such studies. Some researchers interpret the effects of context as contributing to the selective receptive field (RF) input about which neurons transmit information. Others interpret context effects as affecting transmission of information about RF input without becoming part of the RF information transmitted. Here we use partial information decomposition (PID) and entropic information decomposition (EID) to study the properties of a form of modulation previously used in neurobiologically plausible neural nets. PID shows that this form of modulation can affect transmission of information in the RF input without the binary output transmitting any information unique to the modulator. EID produces similar decompositions, except that information unique to the modulator and the mechanistic shared component can be negative when modulating and modulated signals are correlated. Synergistic and source shared components were never negative in the conditions studied. Thus, both PID and EID show that modulatory inputs to a local processor can affect the transmission of information from other inputs. Contrary to what was previously assumed, this transmission can occur without the modulatory inputs becoming part of the information transmitted, as shown by the use of PID with the model we consider. Decompositions of psychophysical data from a visual contrast detection task with surrounding context suggest that a similar form of modulation may also occur in real neural systems.

Age and gender classification in the wild with unsupervised feature learning

Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

Functional Specialization of Seven Mouse Visual Cortical Areas

To establish the mouse as a genetically tractable model for high-order visual processing, we characterized fine-scale retinotopic organization of visual cortex and determined functional specialization of layer 2/3 neuronal populations in seven retinotopically identified areas. Each area contains a distinct visuotopic representation and encodes a unique combination of spatiotemporal features. Areas LM, AL, RL, and AM prefer up to three times faster temporal frequencies and significantly lower spatial frequencies than V1, while V1 and PM prefer high spatial and low temporal frequencies. LI prefers both high spatial and temporal frequencies. All extrastriate areas except LI increase orientation selectivity compared to V1, and three areas are significantly more direction selective (AL, RL, and AM). Specific combinations of spatiotemporal representations further distinguish areas. These results reveal that mouse higher visual areas are functionally distinct, and separate groups of areas may be specialized for motion-related versus pattern-related computations, perhaps forming pathways analogous to dorsal and ventral streams in other species.

Temporally selective processing of communication signals by auditory midbrain neurons

Perception of the temporal structure of acoustic signals contributes critically to vocal signaling. In the aquatic clawed frog Xenopus laevis, calls differ primarily in the temporal parameter of click rate, which conveys sexual identity and reproductive state. We show here that an ensemble of auditory neurons in the laminar nucleus of the torus semicircularis (TS) of X. laevis specializes in encoding vocalization click rates. We recorded single TS units while pure tones, natural calls, and synthetic clicks were presented directly to the tympanum via a vibration-stimulation probe. Synthesized click rates ranged from 4 to 50 Hz, the rate at which the clicks begin to overlap. Frequency selectivity and temporal processing were characterized using response-intensity curves, temporal-discharge patterns, and autocorrelations of reduplicated responses to click trains. Characteristic frequencies ranged from 140 to 3,250 Hz, with minimum thresholds of -90 dB re 1 mm/s at 500 Hz and -76 dB at 1,100 Hz near the dominant frequency of female clicks. Unlike units in the auditory nerve and dorsal medullary nucleus, most toral units respond selectively to the behaviorally relevant temporal feature of the rate of clicks in calls. The majority of neurons (85%) were selective for click rates, and this selectivity remained unchanged over sound levels 10 to 20 dB above threshold. Selective neurons give phasic, tonic, or adapting responses to tone bursts and click trains. Some algorithms that could compute temporally selective receptive fields are described.

Eye-centered encoding of visual space in scene-selective regions

We used functional magnetic resonance imaging (fMRI) to investigate the reference frames used to encode visual information in scene-responsive cortical regions. At early levels of the cortical visual hierarchy, neurons possess spatially selective receptive fields (RFs) that are yoked to specific locations on the retina. In lieu of this eye-centered organization, we speculated that visual areas implicated in scene processing, such as the parahippocampal place area (PPA), the retrosplenial complex (RSC), and transverse occipital sulcus (TOS) might instead possess RFs defined in head-, body-, or world-centered reference frames. To test this, we scanned subjects while they viewed objects and scenes presented at four screen locations while they maintained fixation at one of three possible gaze positions. We then examined response profiles as a function of either fixation-referenced or screen-referenced position. Contrary to our prediction, the PPA and TOS exhibited position-response curves that moved with the fixation point rather than being anchored to the screen, a pattern indicative of eye-centered encoding. RSC, on the other hand, did not exhibit a position-response curve in either reference frame. By showing an important commonality between the PPA/TOS and other visually responsive regions, the results emphasize the critical involvement of these regions in the visual analysis of scenes.

Pattern motion representation in primary visual cortex is mediated by transcortical feedback

A highly important question in visual neuroscience is to identify where in the visual system information from different processing channels is integrated to form the complex scenery we perceive. A common view to this question is that information is processed hierarchically because small and selective receptive fields in lower visual areas melt into larger receptive fields in specialized higher visual areas. However, a higher order area in which all incoming signals ultimately converge has not yet been identified. Rather, modulation of subthreshold influences from outside the classical receptive field related to contextual integration occurs already in early visual areas. So far it is unclear how these influences are mediated (Gilbert, 1998; Angelucci and Bullier, 2003; Gilbert and Sigman, 2007). In the present study, we show that feedback connections from a higher motion processing area critically influence the integration of subthreshold global motion cues in early visual areas. Global motion cues are theoretically not discernible for a local motion detector in V1, however, imprints of pattern motion have been observed in this area (Guo et al., 2004; Schmidt et al., 2006). By combining reversible thermal deactivation and optical imaging of intrinsic signals we demonstrate that feedback signals from the posteromedial suprasylvian sulcus are critical for the discrimination between global and local motions already in early visual areas. These results suggest that global features of the visual scenery are fed back to lower visual processing units in order to facilitate the integration of local cues into a global construct.

Induction of selective plasticity in the frequency tuning of auditory cortex and auditory thalamus neurons by locus coeruleus stimulation

Neurons in primary sensory cortices display selective receptive field plasticity in behavioral situations ranging from classical conditioning to attentional tasks, and it is generally assumed that neuromodulators promote this plasticity. Studies have shown that pairing a pure-tone and a stimulation of the nucleus basalis magnocellularis mimics the selective receptive field facilitations described after classical conditioning. Here, we evaluated the consequences of repeated pairings between a particular sound frequency and a phasic stimulation of locus coeruleus (LC) on the frequency tuning of auditory thalamus and auditory cortex neurons. Selective alterations for the paired frequency were observed for more than 30% of the cells recorded both in cortex and in thalamus. There were as much selective increases as selective decreases at the cortical level, whereas selective increases were prevailing at the thalamic level. Selective changes usually persisted 15 min after pairing in cortex; they dissipated in thalamus, and so did the general increases in both structures. In animals with stimulation sites outside the LC, pairing induced either general changes or no effect. These results indicate that the selective plasticity induced in the frequency tuning of auditory cortex neurons by LC stimulation is bidirectional, thereby suggesting that noradrenergic activation can contribute to the different forms of plasticity observed after distinct behavioral paradigms.

Modelling the substructure of direction selective receptive fields in macaque V1

Modelling the substructure of direction selective receptive fields in macaque V1

Detection of the movement direction by the cells with directional receptive fields in the primary visual cortex of the cat

The study was performed on neurons with direction selective (DS) receptive fields (RFs) in the primary visual cortex of the cat. Preferred directions (PDs) of these cells to a single light spot and a system of two identical light spots moving across the RF with a given angle between them were compared. Directional interactions appeared when the angles between the directions of the two moving spots were 30o or 60o. PD for 56% of the cells coincided with bisectors of these angles. These cells responded to a combination of the two moving stimuli as if only one stimulus moved in the RF in an intermediate direction. This direction coincided with PD of the DS neuron to a single spot. Also, the investigation revealed that DS neurons responded to stimuli moving at such angles as 180o (to preferred and opposite directions simultaneously). In the further experiment we investigated responses of the DS cells in the primary visual cortex of RF. The angle between the directions of the two moving spots was 60o. These cells responded to a combination of the two moving stimuli as if only one stimulus moved in RF in an intermediate direction. The more relative luminance of one of spots in pair was, the closer the intermediate direction approached to the direction of this spot).

Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity.

Event Abstract Back to Event Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity. Pamela M. Baker1* and Wyeth Bair1 1 University of Oxford, Department of Physiology, Anatomy and Genetics, United Kingdom Cross-correlation of spike trains from pairs of neurons has proved valuable for investigating functional connectivity in the visual system. It has revealed the specific functional connections between the lateral geniculate nucleus (LGN) and simple cells in primary visual cortex (V1) [1], and exposed properties of simple cells that drive complex cells [2]. Direction selectivity is a fundamental physiological property that arises from V1 circuitry yet basic questions of how direction selective (DS) receptive fields are constructed remain unanswered. To guide experiments for uncovering cortical mechanisms of direction selectivity, we implemented a set of plausible network models of DS neurons. We demonstrate how cross-correlation analysis can reveal the circuitry and mechanisms generating DS cells, including the nature of the DS nonlinearity and the stage in the visual pathway where the time delay, inherent to motion detection, originates. Our models consisted of LGN cells, V1 simple and complex DS cells that were spiking, conductance-based integrate and fire units with physiologically-realistic intrinsic currents connected by AMPA and GABA-A synapses. We tested model architectures where DS neurons are built directly from LGN inputs or indirectly via orientation-tuned simple cells. In some models, the time delay was implemented via temporal diversity in LGN inputs, in others it was ascribed to dendritic mechanisms in the DS neuron. Both have received recent attention experimentally [3,4]. We also varied whether the DS interaction was facilitatory or suppressive. We computed shuffle-corrected cross-correlograms (CCGs) of spike trains from pairs of units that are accessible to extracellular recording in vivo. We tested the models with visual stimuli employed experimentally, including sinusoidal gratings and flashed dots. We found that cross-correlation is well-suited to determine where the DS time delay arises. In models where temporally-offset inputs synapse onto the DS unit, the peak in the CCG between any non-DS input and the DS cell occurred at the time lag typical of monosynaptically connected cells. In models where the delay was generated in dendritic subunits within the DS cell, the CCG peak shifted systematically to reflect the postsynaptic delays. Cross-correlation revealed the nature of the DS nonlinearity, with facilitatory and suppressive DS mechanisms generating distinct shapes in CCGs. We also identified key factors that determine whether connections between non-DS and DS cells are revealed in CCGs. For example, suppressive mechanisms may only become apparent in CCGs if stimuli that drive the cell in the anti-preferred direction are used. This stimulus dependence reflects the selectivity of the DS mechanism, not the input cells themselves, and is thus independent of network architecture. We explored how the magnitude of peaks and dips in the CCG change as strength and number of inputs to the DS cell vary. We address how these factors create challenges for employing this analysis in electrophysiological recording. Overall, our results suggest that CCGs from simultaneously recorded pre- and post-synaptic non-DS and DS cells, respectively, are necessary and likely sufficient for solving long-standing problems of circuitry and mechanisms underlying cortical direction selectivity. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session II Citation: Baker PM and Bair W (2010). Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity.. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00332 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Mar 2010; Published Online: 08 Mar 2010. * Correspondence: Pamela M Baker, University of Oxford, Department of Physiology, Anatomy and Genetics, Oxford, United Kingdom, pb2001@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Pamela M Baker Wyeth Bair Google Pamela M Baker Wyeth Bair Google Scholar Pamela M Baker Wyeth Bair PubMed Pamela M Baker Wyeth Bair Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Highly Selective Receptive Fields in Mouse Visual Cortex

Genetic methods available in mice are likely to be powerful tools in dissecting cortical circuits. However, the visual cortex, in which sensory coding has been most thoroughly studied in other species, has essentially been neglected in mice perhaps because of their poor spatial acuity and the lack of columnar organization such as orientation maps. We have now applied quantitative methods to characterize visual receptive fields in mouse primary visual cortex V1 by making extracellular recordings with silicon electrode arrays in anesthetized mice. We used current source density analysis to determine laminar location and spike waveforms to discriminate putative excitatory and inhibitory units. We find that, although the spatial scale of mouse receptive fields is up to one or two orders of magnitude larger, neurons show selectivity for stimulus parameters such as orientation and spatial frequency that is near to that found in other species. Furthermore, typical response properties such as linear versus nonlinear spatial summation (i.e., simple and complex cells) and contrast-invariant tuning are also present in mouse V1 and correlate with laminar position and cell type. Interestingly, we find that putative inhibitory neurons generally have less selective, and nonlinear, responses. This quantitative description of receptive field properties should facilitate the use of mouse visual cortex as a system to address longstanding questions of visual neuroscience and cortical processing.

An Analog Silicon Retina With Multichip Configuration

The neuromorphic silicon retina is a novel analog very large scale integrated circuit that emulates the structure and the function of the retinal neuronal circuit. We fabricated a neuromorphic silicon retina, in which sample/hold circuits were embedded to generate fluctuation-suppressed outputs in the previous study [1]. The applications of this silicon retina, however, are limited because of a low spatial resolution and computational variability. In this paper, we have fabricated a multichip silicon retina in which the functional network circuits are divided into two chips: the photoreceptor network chip (P chip) and the horizontal cell network chip (H chip). The output images of the P chip are transferred to the H chip with analog voltages through the line-parallel transfer bus. The sample/hold circuits embedded in the P and H chips compensate for the pattern noise generated on the circuits, including the analog communication pathway. Using the multichip silicon retina together with an off-chip differential amplifier, spatial filtering of the image with an odd- and an even-symmetric orientation selective receptive fields was carried out in real time. The analog data transfer method in the present multichip silicon retina is useful to design analog neuromorphic multichip systems that mimic the hierarchical structure of neuronal networks in the visual system.

Synaptic homeostasis and input selectivity follow from a calcium-dependent plasticity model.

Modifications in the strengths of synapses are thought to underlie memory, learning, and development of cortical circuits. Many cellular mechanisms of synaptic plasticity have been investigated in which differential elevations of postsynaptic calcium concentrations play a key role in determining the direction and magnitude of synaptic changes. We have previously described a model of plasticity that uses calcium currents mediated by N-methyl-D-aspartate receptors as the associative signal for Hebbian learning. However, this model is not completely stable. Here, we propose a mechanism of stabilization through homeostatic regulation of intracellular calcium levels. With this model, synapses are stable and exhibit properties such as those observed in metaplasticity and synaptic scaling. In addition, the model displays synaptic competition, allowing structures to emerge in the synaptic space that reflect the statistical properties of the inputs. Therefore, the combination of a fast calcium-dependent learning and a slow stabilization mechanism can account for both the formation of selective receptive fields and the maintenance of neural circuits in a state of equilibrium.