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Main stages of the computational model a Retinal development. Model simulations of ON and OFF retinal ganglion cells (red and blue circles) from the contralateral and ipsilateral eyes (high and low contrast). b Receptive fields (RF) of 64 thalamic afferents receiving input from the retinas illustrated in a and thalamic afferent sampling density (16 receptive fields sampling the same visual point). c Model simulations of the afferent sorting in the cortical subplate. The 64 afferents (Aff.) illustrated in b are sorted first by retinotopy (left), then by eye input (middle) and then by ONOFF polarity (right). d Model simulations of the eye-polarity grid after the afferent axon arbors spread and combine in each cortical pixel (red: ON dominated, blue: OFF dominated, black line: border between regions dominated by contralateral and ipsilateral eyes). e Model simulations of the eye-polarity grid in a larger cortical patch. f Primordial orientation maps for the contralateral (left) and ipsilateral eyes (right) resulting from thalamocortical convergence. g Adult orientation map after the primordial map is optimized by visual experience.
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The cerebral cortex receives multiple afferents from the thalamus that segregate by stimulus modality forming cortical maps for each sense. In vision, the primary visual cortex also maps the multiple dimensions of the stimulus in patterns that vary across species for reasons unknown. Here we introduce a general theory of cortical map formation, whi...
Citations
... Our finding that orientation selectivity changes across the surface of the retina, and how it aligns along concentric ellipses centered in ventral retina, has implication for how orientation selectivity is organized downstream of the retina especially given the increase in published models of cortical and collicular organizations that factor in retinal organization and activity 25,37 . This is likely especially true in areas like the superior colliculus, which receives extensive retinal innervation and inherits some computations from the retina 38,39 . ...
Orientation or axial selectivity, the property of neurons in the visual system to respond preferentially to certain angles of visual stimuli, plays a pivotal role in our understanding of visual perception and information processing. This computation is performed as early as the retina, and although much work has established the cellular mechanisms of retinal orientation selectivity, how this computation is organized across the retina is unknown. Using a large dataset collected across the mouse retina, we demonstrate functional organization rules of retinal orientation selectivity. First, we identify three major functional classes of retinal cells that are orientation selective and match previous descriptions. Second, we show that one orientation is predominantly represented in the retina and that this predominant orientation changes as a function of retinal location. Third, we demonstrate that neural activity plays little role on the organization of retinal orientation selectivity. Lastly, we use in silico modeling followed by validation experiments to demonstrate that the overrepresented orientation aligns along concentric axes. These results demonstrate that, similar to direction selectivity, orientation selectivity is organized in a functional map as early as the retina.
... Properties of cortical maps in the primary visual cortex have, in turn, been studied in detail by Bonhoeffer and Grinvald (1991), Blasdel (1992), Koch et al. (2016), Kremkow et al. (2016), andNajafian et al. (2022). ...
This paper presents results of combining (i) theoretical analysis regarding connections between the orientation selectivity and the elongation of receptive fields for the affine Gaussian derivative model with (ii) biological measurements of orientation selectivity in the primary visual cortex, to investigate if (iii) the receptive fields can be regarded as spanning a variability in the degree of elongation. From an in-depth theoretical analysis of idealized models for the receptive fields of simple cells and complex cells in the primary visual cortex, we have established that the directional selectivity becomes more narrow with increasing elongation of the receptive fields. By comparison with previously established biological results, concerning broad vs. sharp orientation tuning of visual neurons in the primary visual cortex, we demonstrate that those underlying theoretical predictions, in combination with these biological results, are consistent with a previously formulated biological hypothesis , stating that the biological receptive field shapes should span the degrees of freedom in affine image transformations, to support affine covariance over the population of receptive fields in the primary visual cortex. Based on this possible indirect support for the working hypothesis concerning affine covariance, we formulate a set of testable predictions that could be used to, with neurophys-iological experiments, judge if the receptive fields in the primary visual cortex of higher mammals could be regarded as spanning a variability over the eccentricity or the elongation of the receptive fields, and, if so, then also characterize if such a variability would, in a structured way, be related to the pinwheel structure in the visual cortex.
... We assume the visual fields of the E-neurons to be overlapping on the retina (refer to Figure 1a). The overlap degrees in visual fields vary across species (including macaques, cats, rodents, etc.) to accommodate the size of IODs in the visual cortex representation as observed by experimental studies (Najafian et al., 2022). ...
... For retinotopic data, we refer to (Srinivasan et al., 2015;Tehovnik & Slocum, 2007;Scholl et al., 2013;Veit et al., 2014;Engelmann & Peichl, 1996;Weigand et al., 2017;Law et al., 1988;Huberman et al., 2006;Niell & Stryker, 2008;van Beest et al., 2021;Foik et al., 2020). The anatomical data on OPMs is sourced from (Najafian et al., 2022). ...
... To quantitatively analyze the orientation maps presented in Figure 2a, several measures are employed (Najafian et al., 2022), including the number of PCs, nearest neighbor pinwheel distance (NNPD), PC density, and size of the hypercolumn. These measures are illustrated in Figure 2b. Figure 2c describes the relationship between the NNPD and the overlap among RFs. ...
Orientation preference maps (OPMs) in the primary visual cortex of primates organize orientation-tuned neurons into columnar structures, forming pinwheel-like patterns. However, lower-level animals like rodents typically exhibit a lack of OPMs, with neurons either randomly distributed or aggregated in small clusters. This distinction prompts an inquiry into whether more structured cortical columns correlate with improved visual computational or coding efficiency. To explore this, we propose a novel self-evolving spiking neural network (SESNN). To the best of our knowledge, the SESNN is the first spiking network, incorporating mechanisms of neural plasticity in forming neural connections without explicit objective functions. We reveal that the emergence of pinwheel structures is primarily driven by sparse coding constraints and local synaptic plasticity as fundamental mechanisms. Second, for higher mammals with expansive iso-orientation domains (IODs), the firing responses in pinwheel structures primarily emanate from pinwheel centers (PCs) and progressively extend toward the periphery, encompassing adjacent IODs. Third, the size and organization of these IODs across species are significantly influenced by the receptive fields' ability to process overlapping visual information. Lastly, PCs within large IODs demonstrate enhanced robustness and population sparseness in detecting a variety of orientation features. These results indicate that the spatial pinwheel structure facilitates highly efficient and reliable coding performance.
... However, this architectural design choice limits their ability to model fundamental aspects of biological vision. A central aspect is the origin and function of cortical topography 12,13 and its relation to behaviour -a central area of research in visual neuroscience for which modelling promises important insights that cannot easily be addressed using only experimental approaches. ...
... One limitation of most existing models of topographic organisation is that they are either truly topographic but not task-performing or task-performing but not truly topographic. Examples of the former are hand-crafted self-organising maps 13,67,68 . The latter are most often, if not always, based on augmenting CNNs, for example by adding a spatial remapping to their units, building self-organising maps based on unit activities, or creating multiple CNN streams 30,60,61,[64][65][66] . ...
Computational models are an essential tool for understanding the origin and functions of the topographic organisation of the primate visual system. Yet, vision is most commonly modelled by convolutional neural networks that ignore topography by learning identical features across space. Here, we overcome this limitation by developing All-Topographic Neural Networks (All-TNNs). Trained on visual input, several features of primate topography emerge in All-TNNs: smooth orientation maps and cortical magnification in their first layer, and category-selective areas in their final layer. In addition, we introduce a novel dataset of human spatial biases in object recognition, which enables us to directly link models to behaviour. We demonstrate that All-TNNs significantly better align with human behaviour than previous state-of-the-art convolutional models due to their topographic nature. All-TNNs thereby mark an important step forward in understanding the spatial organisation of the visual brain and how it mediates visual behaviour.
... Studies in a larger variety of rodent species might reveal whether their visual cortex can also hold a "proper" orientation map, like those found in cats and primates 7,9 . Apart from taxonomy, factors like visual cortex size 34 , the absolute number of neurons 35 or the retino-thalamo-cortical mapping ratio [36][37][38] have been put forward in theoretical work to explain the absence of columns in a visual cortex as small as that of the mouse. While some of these studies refer to a columnar architecture in general, or orientation columns specifically, others 35 explicitly predict that mouse visual cortex does not have ocular dominance columns, contrary to what our experiments show. ...
The columnar organization of response properties is a fundamental feature of the mammalian visual cortex. However, columns have not been observed universally across all mammalian species. Here, we report the discovery of ocular dominance columns in mouse visual cortex. Our observation in this minute cortical area sets a new boundary condition for models explaining the emergence of columnar organizations in the neocortex.
... Whether a canonical neural circuit is responsible for these feature preferences is consistently of great interest for system neuroscientists. Blackwhite asymmetry is reportedly related to orientation selectivity, spatial frequency selectivity, and spatial resolution in the input layer of cat V1 (Kremkow et al., 2016;Kremkow and Alonso, 2018;Najafian et al., 2022;St-Amand and Baker, 2023). Recent research proposes that cortical direction selectivity is attributable to variations in timing and strength between the ON and OFF pathways (Shariati and Freeman, 2012;Luo-Li et al., 2018;Chariker et al., 2021Chariker et al., , 2022. ...
Black and white information is asymmetrically distributed in natural scenes, evokes asymmetric neuronal responses and causes asymmetric perceptions. Recognizing the universality and essentiality of black–white asymmetry in visual information processing, the neural substrates for black–white asymmetry remain unclear. To disentangle the role of the feedforward and recurrent mechanisms in the generation of cortical black–white asymmetry, we recorded the V1 laminar responses and LGN responses of anesthetized cats of both sexes. In a cortical column, we found that black–white asymmetry starts at the input layer and becomes more pronounced in the output layer. We also found distinct dynamics of black–white asymmetry between the output layer and the input layer. Specifically, black responses dominate in all layers after stimulus onset. After stimulus offset, black and white responses are balanced in the input layer, but black responses still dominate in the output layer. Compared to that in the input layer, the rebound response in the output layer is significantly suppressed. The relative suppression strength evoked by white stimuli is notably stronger and depends on the location within the ON-OFF cortical map. A model with delayed and polarity-selective cortical suppression explains black–white asymmetry in the output layer, within which prominent recurrent connections are identified by Granger causality analysis. In addition to black–white asymmetry in response strength, the interlaminar differences in spatial RF varied dynamically. Our findings suggest that the feedforward and recurrent mechanisms are dynamically recruited for the generation of black–white asymmetry in V1.
SIGNIFICANCE STATEMENT:
Black–white asymmetry is universal and essential in visual information processing, yet the neural substrates for cortical black–white asymmetry remain unknown. Leveraging V1 laminar recordings, we provided the first laminar pattern of black–white asymmetry in cat V1 and found distinct dynamics of black–white asymmetry between the output layer and the input layer. Comparing black–white asymmetry across three visual hierarchies, the LGN, V1 input layer and V1 output layer, we demonstrated that the feedforward and recurrent mechanisms are dynamically recruited for the generation of cortical black–white asymmetry. Our findings not only enhance our understanding of laminar processing within a cortical column but also elucidate how feedforward connections and recurrent connections interact to shape neuronal response properties.
... Because both dark-dominance and stronger inhibition at the 13.3-26.7 latency are because of slower inhibition to dark stimuli (Fig. 4C), the reason why neurons are less orientation-selective at the 13.3-26.7 than at the 26.7-40 ms latency could be because of this slower inhibition to dark stimuli. These results are consistent with theoretical models of cortical development in V1 (Najafian et al., 2022), which have suggested that balanced ON/OFF responses lead to higher OS. ...
Neurons in the primary visual cortex (V1) receive excitation and inhibition from distinct parallel pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli, consistent with a preponderance of darker regions in natural images, as well as human psychophysics. However, it has been unclear whether this "dark-dominance" is due to more excitation from the OFF pathway or more inhibition from the ON pathway. To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neurons' responses. Analyzing a sample of 71 neurons (in anesthetized, paralyzed cats of either sex) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations. We show that this asymmetry is predominantly due to slower inhibition to dark stimuli rather than to stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons, we find dark-dominance to solely occur in the early latencies of neurons’ responses. Neurons that are strongly dark-dominated also tend to be less orientation selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.
SIGNIFICANCE STATEMENT:
Neurons in the early visual cortex respond on average more strongly to dark than to light stimuli, but the mechanisms behind this bias have been unclear. Here we address this issue by combining single-unit electrophysiology with a novel machine learning model to analyze neurons’ responses to natural image stimuli in primary visual cortex. Using these techniques, we find slower inhibition to light than to dark stimuli to be the leading mechanism behind stronger dark responses. This slower inhibition to light might help explain other empirical findings, such as why orientation selectivity is weaker at earlier response latencies. These results demonstrate how imbalances in excitation vs. inhibition can give rise to response asymmetries in cortical neuron responses.
... Many theories and mechanisms have been proposed for the establishment of OPM in the V1 of higher mammals (Swindale, 1992;Obermayer and Blasdel, 1993;Miller, 1994;Swindale, 1996;Erwin and Miller, 1998;Koulakov and Chklovskii, 2001;Ringach, 2004;Mariño et al., 2005;Kaschube et al., 2010;Paik and Ringach, 2011;Reichl et al., 2012;Kremkow et al., 2016;Jang et al., 2020;Najafian et al., 2022). However, the OPM in the SC is less well-studied. ...
... ; https://doi.org/10. 1101/2022 measures how balanced are the ON and OFF inputs (perfectly balanced means ON-to-OFF ratio at location x is 1 and thus S ratio ( x, t) = 1), i.e. the inverse of the overall net ON or OFF input to the postsynaptic SC neuron at location x (the inverse of absolute ON-OFF dominance) and S amount ( x, t) = α |W D ( x, α, t)| α W S ( x, α, t) ...
... ; https://doi.org/10. 1101/2022 ...
Neurons in the mouse superior colliculus (SC) are arranged in an orientation preference map that has a concentric organization, which is aligned to the center of vision and the optic flow experienced by the mouse. The developmental mechanisms that underlie this functional map remain unclear. Here, we propose that the spatiotemporal properties of spontaneous retinal waves during development provide a scaffold to establish the concentric orientation map in the mouse SC and its alignment to the optic flow. We test this hypothesis by modelling the orientation-tuned SC neurons that receive ON/OFF retinal inputs. Our results suggest that the stage III retinal wave properties, namely OFF delayed response and the wave propagation direction bias, are key factors that regulate the spatial organization of the SC orientation map. Specifically, the OFF delay mediates the establishment of orientation-tuned SC neurons by segregating their ON/OFF receptive subfields, the wave-like activities facilitate the formation of a concentric pattern, and the wave direction biases align the orientation map to the center of vision. Taken together, our model suggests that retinal waves may play an instructive role in establishing functional properties of SC neurons and provides a promising mechanism for explaining the correlations between the optic flow and the SC orientation map.
Significance
The thalamus provides the principal input to the cortex and therefore to understand the mechanisms underlying cortical processing, perception and behavior requires unraveling the thalamocortical connectivity in vivo. We here describe an approach for mapping the thalamocortical connectivity in mice in vivo using high-density Neuropixels probes. Tangential insertions of high-density electrodes into the middle layer of the cortex allow detecting action potentials (APs) from thalamic axons simultaneously with somatic APs from postsynaptic cortical neurons. The close physical distance of recorded thalamocortical axons and cortical neurons on the high-density electrode results in a high yield of measured thalamocortical connections in vivo, within single recordings. Thus, this method permits to efficiently map the thalamocortical connectivity in vivo.
