Mammalian Visual Cortex Research Articles

An edge-simplicity bias in the visual input to young infants.

The development of sparse edge coding in the mammalian visual cortex depends on early visual experience. In humans, there are multiple indicators that the statistics of early visual experiences has unique properties that may support these developments. However, there are no direct measures of the edge statistics of infant daily-life experience. Using head-mounted cameras to capture egocentric images of young infants and adults in the home, we found infant images to have distinct edge statistics relative to adults. For infants, scenes with sparse edge patterns-few edges and few orientations-dominate. The findings implicate biased early input at the scale of daily life that is likely specific to the early months after birth and provide insights into the quality, amount, and timing of the visual experiences during the foundational developmental period for human vision.

A neurocomputational model of decision and confidence in object recognition task

How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit.

Neural waves and computation in a neural net model I: Convolutional hierarchies.

The computational resources of a neuromorphic network model introduced earlier are investigated in the context of such hierarchical systems as the mammalian visual cortex. It is argued that a form of ubiquitous spontaneous local convolution, driven by spontaneously arising wave-like activity-which itself promotes local Hebbian modulation-enables logical gate-like neural motifs to form into hierarchical feed-forward structures of the Hubel-Wiesel type. Extra-synaptic effects are shown to play a significant rôle in these processes. The type of logic that emerges is not Boolean, confirming and extending earlier findings on the logic of schizophrenia.

Metaplasticity: Dark exposure boosts local excitability and visual plasticity in adult human cortex.

An interlude of dark exposure for about 1 week is known to shift excitatory/inhibitory (E/I) balance of the mammalian visual cortex, promoting plasticity and accelerating visual recovery in animals that have experienced cortical lesions during development. However, the translational impact of our understanding of dark exposure from animal studies to humans remains elusive. Here, we used magnetic resonance spectroscopy as a probe for E/I balance in the primary visual cortex (V1) to determine the effect of 60min of dark exposure, and measured binocular combination as a behavioural assay to assess visual plasticity in 14 normally sighted human adults. To induce neuroplastic changes in the observers, we introduced 60min of monocular deprivation, which is known to temporarily shift sensory eye balance in favour of the previously deprived eye. We report that prior dark exposure for 60 min strengthens local excitability in V1 and boosts visual plasticity in normal adults. However, we show that it does not promote plasticity in amblyopic adults. Nevertheless, our findings are surprising, given the fact that the interlude is very brief. Interestingly, we find that the increased concentration of the excitatory neurotransmitter is not strongly correlated with the enhanced functional plasticity. Instead, the absolute degree of change in its concentration is related to the boost, suggesting that the dichotomy of cortical excitation and inhibition might not explain the physiological basis of plasticity in humans. We present the first evidence that an environmental manipulation that shifts cortical E/I balance can also act as a metaplastic facilitator for visual plasticity in humans. KEY POINTS: A brief interlude (60min) of dark exposure increased the local concentration of glutamine/glutamate but not that of GABA in the visual cortex of adult humans. After dark exposure, the degree of the shift in sensory eye dominance in favour of the previously deprived eye from short-term monocular deprivation was larger than that from only monocular deprivation. The neurochemical and behavioural measures were associated: the magnitude of the shift in the concentration of glutamine/glutamate was correlated with the boost in perceptual plasticity after dark exposure. Surprisingly, the increase in the concentration of glutamine/glutamate was not correlated with the perceptual boost after dark exposure, suggesting that the physiological mechanism of how E/I balance regulates plasticity is not deterministic. In other words, an increased excitation did not unilaterally promote plasticity.

Critical Periods in Vision Revisited.

For four decades, investigations of the biological basis of critical periods in the developing mammalian visual cortex were dominated by study of the consequences of altered early visual experience in cats and nonhuman primates. The neural deficits thus revealed also provided insight into the origin and neural basis of human amblyopia that in turn motivated additional studies of humans with abnormal early visual input. Recent human studies point to deficits arising from alterations in all visual cortical areas and even in nonvisual cortical regions. As the new human data accumulated in parallel with a near-complete shift toward the use of rodent animal models for the study of neural mechanisms, it is now essential to review the human data and the earlier animal data obtained from cats and monkeys to infer general conclusions and to optimize future choice of the most appropriate animal model.

Modeling the role of gap junctions between excitatory neurons in the developing visual cortex.

Recent experiments in the developing mammalian visual cortex have revealed that gap junctions couple excitatory cells and potentially influence the formation of chemical synapses. In particular, cells that were coupled by a gap junction during development tend to share an orientation preference and are preferentially coupled by a chemical synapse in the adult cortex, a property that is diminished when gap junctions are blocked. In this work, we construct a simplified model of the developing mouse visual cortex including spike-timing-dependent plasticity of both the feedforward synaptic inputs and recurrent cortical synapses. We use this model to show that synchrony among gap-junction-coupled cells underlies their preference to form strong recurrent synapses and develop similar orientation preference; this effect decreases with an increase in coupling density. Additionally, we demonstrate that gap-junction coupling works, together with the relative timing of synaptic development of the feedforward and recurrent synapses, to determine the resulting cortical map of orientation preference.

Visual Cortex: Binocular Matchmaking

Most binocular neurons in the mammalian visual cortex show matched selectivity for light stimuli presented through either eye. A recent study tracked the responses of individual neurons in early visual cortex over time, revealing that matched binocular selectivity develops through major rearrangements of binocular visual circuits.

Functional Differentiation of Mouse Visual Cortical Areas Depends upon Early Binocular Experience.

The mammalian visual cortex contains multiple retinotopically defined areas that process distinct features of the visual scene. Little is known about what guides the functional differentiation of visual cortical areas during development. Recent studies in mice have revealed that visual input from the two eyes provides spatiotemporally distinct signals to primary visual cortex (V1), such that contralateral eye-dominated V1 neurons respond to higher spatial frequencies than ipsilateral eye-dominated neurons. To test whether binocular visual input drives the differentiation of visual cortical areas, we used two-photon calcium imaging to characterize the effects of juvenile monocular deprivation (MD) on the responses of neurons in V1 and two higher visual areas, LM (lateromedial) and PM (posteromedial). In adult mice of either sex, we find that MD prevents the emergence of distinct spatiotemporal tuning in V1, LM, and PM. We also find that, within each of these areas, MD reorganizes the distinct spatiotemporal tuning properties driven by the two eyes. Moreover, we find a relationship between speed tuning and ocular dominance in all three areas that MD preferentially disrupts in V1, but not in LM or PM. Together, these results reveal that balanced binocular vision during development is essential for driving the functional differentiation of visual cortical areas. The higher visual areas of mouse visual cortex may provide a useful platform for investigating the experience-dependent mechanisms that set up the specialized processing within neocortical areas during postnatal development.SIGNIFICANCE STATEMENT Little is known about the factors guiding the emergence of functionally distinct areas in the brain. Using in vivo Ca2+ imaging, we recorded visually evoked activity from cells in V1 and higher visual areas LM (lateromedial) and PM (posteromedial) of mice. Neurons in these areas normally display distinct spatiotemporal tuning properties. We found that depriving one eye of normal input during development prevents the functional differentiation of visual areas. Deprivation did not disrupt the degree of speed tuning, a property thought to emerge in higher visual areas. Thus, some properties of visual cortical neurons are shaped by binocular experience, while others are resistant. Our study uncovers the fundamental role of binocular experience in the formation of distinct areas in visual cortex.

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.

Modification and hardware implementation of cortex‐like object recognition model

Object recognition in the visual cortex of mammals and humans has inspired many computational object recognition models. Hierarchical model and X (HMAX) is a well-known biologically motivated object recognition model with scale and position tolerance and high accuracy. Due to the computational intensive nature, hardware implementation with massive parallel processing is suggested for real-time applications. However, it is important to explore algorithmic trade-offs when mapping an algorithm to are configurable hardware. A direct conversion of the software implementation of an algorithm generally results inefficient hardware resource usage. In this study, the authors propose a novel modification into the HMAX model which makes it suitable for hardware implementation. More precisely, to reduce the number of memory blocks and multipliers of the S2 layer of HMAX produces, they replace the first norm by the second norm, which critically affects the silicon area in an application-specific integrated circuit implementation or the required resources in field-programmable gate array (FPGA). To evaluate the proposed model, they implement a pipelined version of the revised model on a mid-range commercial Xilinx FPGA, i.e. XC6VLX240T platform from a Virtex 6 family of Xilinx using ISE. Compared to the recent hardware implementation of HMAX, the proposed model offers 83% resource degradation in DSP48 slices and 3% in memory blocks.

Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.

Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification. Grayscale input images are fed through a feed-forward network consisting of orientation-selective neurons, which then projected to a layer of downstream classifier neurons through the spiking-based supervised tempotron learning rule. Based on the orientation-selective mechanism of the visual cortex and tempotron learning rule, the network can effectively classify images of the extensively studied MNIST database of handwritten digits, which achieves 96% classification accuracy based on only 2000 training samples (traditional training set is 60000). Compared with other classification methods, our model not only guarantees the biological plausibility and the accuracy of image classification but also significantly reduces the needed training samples. Considering the fact that the most commonly used deep learning neural networks need big data samples and high power consumption in image recognition, this brain-inspired computational neural network model based on the layer-by-layer hierarchical image processing mechanism of the visual cortex may provide a basis for the wide application of spiking neural networks in the field of intelligent computing.

Ocular dominance plasticity: Molecular mechanisms revisited

Ocular dominance plasticity (ODP) is a type of cortical plasticity operating in visual cortex of mammals that are endowed with binocular vision based on the competition-driven disparity. Earlier, a molecular mechanism was proposed that catecholamines play an important role in the maintenance of ODP in kittens. Having survived the initial test, the hypothesis was further advanced to identify noradrenaline (NA) as a key factor that regulates ODP in the immature cortex. Later, the ODP-promoting effect of NA is extended to the adult with age-related limitations. Following the enhanced NA availability, the chain events downstream lead to the β-adrenoreceptor-induced cAMP accumulation, which in turn activates the protein kinase A. Eventually, the protein kinase translocates to the cell nucleus to activate cAMP responsive element binding protein (CREB). CREB is a cellular transcription factor that controls the transcription of various genes, underpinning neuronal plasticity and long-term memory. In the advent of molecular genetics in that various types of new tools have become available with relative ease, ODP research has lightly adopted in the rodent model the original concepts and methodologies. Here, after briefly tracing the strategic maturation of our quest, the review moves to the later development of the field, with the emphasis placed around the following issues: (a) Are we testing ODP per se? (b) What does monocular deprivation deprive of the immature cortex? (c) The critical importance of binocular competition, (d) What is the adult plasticity? (e) Excitation-Inhibition balance in local circuits, and (f) Species differences in the animal models.

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Abstract. Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany.

Immunocytochemical localization of tyrosine hydroxylase in the visual cortex of the microbat, Rhinolophus ferrumequinum.

In order to enhance our understanding of bat vision, we investigated tyrosine hydroxylase (TH)-immunoreactive (IR) fibers in the visual cortex of the microbat. The study was conducted on 12 freshly-caught adult bats (Rhinolophus ferrumequinum, both sexes, weighing 15-20 g). We used standard immunocytochemistry and confocal microscopy. TH-IR fibers were distributed throughout all layers of the visual cortex, with the highest density in layer I. Two types of TH-IR fibers were observed: small and large varicose fibers. TH-IR cells were not found in the microbat visual cortex. The microbat substantia nigra and ventral tegmental areas, previously identified sources of TH-IR fibers in the mammalian visual cortex, all contained strongly labeled TH-IR cells. The average diameters of TH-IR cells in the substantia nigra and the ventral tegmental areas were 14.39 ± 0.13 μm (mean ± SEM) and 11.85 ± 0.13 μm, respectively. Our results suggest that the microbat has a well-constructed neurochemical organization of THIR fibers. This observation should provide fundamental insights into a better understanding of the nocturnal, echolocating bat visual system.

Persistent Firing and Adaptation in Optic-Flow-Sensitive Descending Neurons

A general principle of sensory systems is that they adapt to prolonged stimulation by reducing their response over time. Indeed, in many visual systems, including higher-order motion sensitive neurons in the fly optic lobes and the mammalian visual cortex, a reduction in neural activity following prolonged stimulation occurs. In contrast to this phenomenon, the response of the motor system controlling flight maneuvers persists following the offset of visual motion. It has been suggested that this gap is caused by a lingering calcium signal in the output synapses of fly optic lobe neurons. However, whether this directly affects the responses of the post-synaptic descending neurons, leading to the observed behavioral output, is not known. We use extracellular electrophysiology to record from optic-flow-sensitive descending neurons in response to prolonged wide-field stimulation. We find that, as opposed to most sensory and visual neurons, and in particular to the motion vision sensitive neurons in the brains of both flies and mammals, the descending neurons show little adaption during stimulus motion. In addition, we find that the optic-flow-sensitive descending neurons display persistent firing, or an after-effect, following the cessation of visual stimulation, consistent with the lingering calcium signal hypothesis. However, if the difference in after-effect is compensated for, subsequent presentation of stimuli in a test-adapt-test paradigm reveals adaptation to visual motion. Our results thus show a combination of adaptation and persistent firing in the neurons that project to the thoracic ganglia and thereby control behavioral output.

The sifting of visual information in the superior colliculus.

Much of the early visual system is devoted to sifting the visual scene for the few bits of behaviorally relevant information. In the visual cortex of mammals, a hierarchical system of brain areas leads eventually to the selective encoding of important features, like faces and objects. Here, we report that a similar process occurs in the other major visual pathway, the superior colliculus. We investigate the visual response properties of collicular neurons in the awake mouse with large-scale electrophysiology. Compared to the superficial collicular layers, neuronal responses in the deeper layers become more selective for behaviorally relevant stimuli; more invariant to location of stimuli in the visual field; and more suppressed by repeated occurrence of a stimulus in the same location. The memory of familiar stimuli persists in complete absence of the visual cortex. Models of these neural computations lead to specific predictions for neural circuitry in the superior colliculus.

Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex

In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry.

Persistent Firing and Adaptation in Optic Flow Sensitive Descending Neurons

A general principle of sensory systems is that they adapt to prolonged stimulation by reducing their response over time. Indeed, many visual systems, including higher-order motion sensitive neurons in the fly optic lobes and the mammalian visual cortex, a reduction in neural activity following prolonged stimulation occurs. In contrast to this phenomenon, the response of the motor system controlling flight maneuvers persists following the offset of visual motion. It has been suggested that this gap is caused by a lingering calcium signal in the output synapses of optic lobe neurons. However, whether this directly affects the responses of the post-synaptic descending neurons, leading to the observed behavioral output, is not known. We use extracellular electrophysiology to record from optic flow sensitive descending neurons in response to prolonged wide-field stimulation. We find that, as opposed to most sensory and visual neurons, and in particular to the motion vision sensitive neurons in the brains of flies and mammals, the descending neurons show little adaption during stimulus motion. In addition, we find that the optic flow sensitive descending neurons display persistent firing, or an after-effect, following the cessation of visual stimulation, consistent with the lingering calcium signal hypothesis. However, if the difference in after-effect is compensated for, subsequent presentation of stimuli in a test-adapt-test paradigm reveals adaptation to visual motion. Our results thus show an interesting combination of adaptation and persistent firing, in the neurons that project to the thoracic ganglia, and thereby control behavioral output.

Testosterone stimulates perineuronal nets development around parvalbumin cells in the adult canary brain in parallel with song crystallization

Perineuronal nets (PNN) of the extracellular matrix are dense aggregations of chondroitin-sulfate proteoglycans that usually surround fast-spiking parvalbumin-expressing inhibitory interneurons (PV). The development of PNN around PV appears specifically at the end of sensitive periods of visual learning and limits the synaptic plasticity in the visual cortex of mammals. Seasonal songbirds display a high level of adult neuroplasticity associated with vocal learning, which is regulated by fluctuations of circulating testosterone concentrations. Seasonal changes in testosterone concentrations and in neuroplasticity are associated with vocal changes between the non-breeding and breeding seasons. Increases in blood testosterone concentrations in the spring lead to the annual crystallization of song so that song becomes more stereotyped. Here we explore whether testosterone also regulates PNN expression in the song control system of male and female canaries. We show that, in both males and females, testosterone increases the number of PNN and of PV neurons in the three main telencephalic song control nuclei HVC, RA (nucleus robustus arcopallialis) and Area X and increases the PNN localization around PV interneurons. Singing activity was recorded in males and quantitative analyses demonstrated that testosterone also increased male singing rate, song duration and song energy while decreasing song entropy. Together, these data suggest that the development of PNN could provide the synaptic stability required to maintain the stability of the testosterone-induced crystallized song. This provides the new evidence for a role of PNN in the regulation of adult seasonal plasticity in seasonal songbirds.

Inhibitory cell populations depend on age, sex, and prior experience across a neural network for Critical Period learning

In many ways, the complement of cell subtypes determines the information processing that a local brain circuit can perform. For example, the balance of excitatory and inhibitory (E/I) signaling within a brain region contributes to response magnitude and specificity in ways that influence the effectiveness of information processing. An extreme example of response changes to sensory information occur across Critical Periods (CPs). In primary mammalian visual cortex, GAD65 and parvalbumin inhibitory cell types in particular control experience-dependent responses during a CP. Here, we test how the density of GAD65- and parvalbumin-expressing cells may inform on a CP for complex behavioral learning. Juvenile male zebra finch songbirds (females cannot sing) learn to sing through coordinated sensory, sensorimotor, and motor learning processes distributed throughout a well-defined neural network. There is a CP for sensory learning, the process by which a young male forms a memory of his “tutor’s” song, which is then used to guide the young bird’s emerging song structure. We quantified the effect of sex and experience with a tutor on the cell densities of GAD65- and parvalbumin-expressing cells across major nodes of the song network, using ages that span the CP for tutor song memorization. As a resource, we also include whole-brain mapping data for both genes. Results indicate that inhibitory cell populations differ across sex, age, and experiential conditions, but not always in the ways we predicted.