Visual Receptive Field Properties of Neurons in the Mouse Lateral Geniculate Nucleus

Characterization of receptive fields in the dorsal lateral geniculate nucleus of the tammar wallaby

Orientation selectivity is a prominent feature of neurons in the mammalian primary visual cortex (V1), yet its emergence along the visual pathway varies across species. In carnivores and primates, neurons with elongated and orientation-selective receptive fields (RFs) emerge in V1, whereas in mice such RFs appear earlier, in the retina or dorsal lateral geniculate nucleus (dLGN). Here, we investigate the RF properties of neurons in the dLGN of a marsupial, the wallaby (Macropus eugenii) (n = 2; males), using multichannel electrodes and nonlinear input model (NIM) analysis. Do dLGN RFs resemble those of carnivores and primates or exhibit unique characteristics, particularly regarding orientation selectivity? We found that 82% of neurons have a predominant ON-center response. We identified two main cell types: X-cells (n = 15/22), which exhibit linear properties, and Y-cells (n = 7/22), which display nonlinear characteristics. Most dLGN RFs were blob-like and lacked the oriented structures seen in cortical neurons but some had slightly elongated central areas. These results indicate that robust orientation selectivity develops fully in V1 (76% of neurons). However, mild orientation biases were observed in 41% of dLGN neurons. This study enhances our understanding of visual processing in marsupials and underscores the evolutionary significance of orientation selectivity in mammalian visual pathways.NEW & NOTEWORTHY This study examines receptive field (RF) properties of neurons in the dorsal lateral geniculate nucleus (dLGN) of wallabies using multichannel electrodes and nonlinear input model (NIM) analysis. We identified two main cell types: X-cells (linear) and Y-cells (nonlinear). Most dLGN RFs were blob-like, with mild orientation biases in 41% of neurons, indicating robust orientation selectivity develops fully in primary visual cortex (V1) (76% of neurons).

Determination of the Time-frequency Features for Impulse Components in EEG Signals

Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. Inspired by the visual system's ability to identify objects and their locations, we propose a new method that integrates a visual system model with wavelet analysis to calculate both time and frequency features of local impulses in EEG signals. We develop a mathematical model based on invariant pattern recognition by the visual system, combined with wavelet analysis using Krawtchouk functions as the mother wavelet. Our method precisely identifies the localization and frequency characteristics of the impulse components in EEG signals. Tested on task-related EEG data, it accurately detected blink components (0.5 to 1 Hz) and separated muscle artifacts (16 Hz). It also identified muscle response durations (298 ms) within the 1 to 31 Hz range in emotional reaction studies, offering insights into both individual and typical emotional responses. We further illustrated how the new method circumvents the uncertainty principle in low-frequency wavelet analysis. Unlike classical wavelet analysis, our method provides spectral characteristics of EEG impulses invariant to time shifts, improving the identification and classification of EEG components.

Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat

The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.

Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma (DBA/2J) mice

Glaucoma is a leading cause of irreversible blindness worldwide, and previous studies have shown that, in addition to affecting the eyes, it also causes abnormalities in the brain. However, it is not yet clear how the primary visual cortex (V1) is altered in glaucoma. This study used DBA/2J mice as a model for spontaneous secondary glaucoma. The aim of the study was to compare the electrophysiological and histomorphological characteristics of neurons in the V1 between 9-month-old DBA/2J mice and age-matched C57BL/6J mice. We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses, including single-unit spiking and gamma band oscillations. The morphology of layer II/III neurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections. Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined. Compared with the C57BL/6J group, V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation. Moreover, fewer neurons were observed in the V1 of DBA/2J mice compared with C57BL/6J mice. These findings suggest that DBA/2J mice have fewer neurons in the V1 compared with C57BL/6J mice, and that these neurons have impaired visual tuning. Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model. This study might offer some innovative perspectives regarding the treatment of glaucoma.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably 'read out' visually driven thalamic input.

Information Theoretic Approaches to Deciphering the Neural Code with Functional Fluorescence Imaging

Information theoretic metrics have proven useful in quantifying the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, these metrics are typically applied to action potential (AP) recordings and were not designed for the slow timescales and variable amplitudes typical of functional fluorescence recordings (e.g., calcium imaging). The lack of research guidelines on how to apply and interpret these metrics with fluorescence traces means the neuroscience community has yet to realize the power of information theoretic metrics. Here, we used computational methods to create mock AP traces with known amounts of information. From these, we generated fluorescence traces and examined the ability of different information metrics to recover the known information values. We provide guidelines for how to use information metrics when applying them to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from mouse hippocampal neurons imaged during virtual navigation.

Emergence of local and global synaptic organization on cortical dendrites

Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales.

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.

Organization, Function, and Development of the Mouse Retinogeniculate Synapse

Visual information is encoded in distinct retinal ganglion cell (RGC) types in the eye tuned to specific features of the visual space. These streams of information project to the visual thalamus, the first station of the image-forming pathway. In the mouse, this connection between RGCs and thalamocortical neurons, the retinogeniculate synapse, has become a powerful experimental model for understanding how circuits in the thalamus are constructed to process these incoming lines of information. Using modern molecular and genetic tools, recent studies have suggested a more complex circuit organization than was previously understood. In this review, we summarize the current understanding of the structural and functional organization of the retinogeniculate synapse in the mouse. We discuss a framework by which a seemingly complex circuit can effectively integrate and parse information to downstream stations of the visual pathway. Finally, we review how activity and visual experience can sculpt this exquisite connectivity.

Contrast-Dependence of Temporal Frequency Tuning in Mouse V1

The perception of speed is influenced by visual contrast. In primary visual cortex (V1), an early stage in the visual perception pathway, the neural tuning to speed is directly related to the neural tuning to temporal frequency of stimulus changes. The influence of contrast on speed perception can be caused by the joint dependency of neural responses in V1 on temporal frequency and contrast. Here, we investigated how tuning to contrast and temporal frequency in V1 of anesthetized mice are related. We found that temporal frequency tuning is contrast-dependent. V1 was more responsive at lower temporal frequencies than the dLGN, consistent with previous work at high contrast. The temporal frequency tuning moves toward higher temporal frequencies with increasing contrast. The low half-maximum temporal frequency does not change with contrast. The Heeger divisive normalization equation provides a good fit to many response characteristics in V1, but does not fit the dependency of temporal frequency and contrast with set of parameters for all temporal frequencies. Different mechanisms for normalization in the visual cortex may predict different relationships between temporal frequency and contrast non-linearity. Our data could help to make a model selection.

An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex

The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits ("macrocolumns"), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.

The Visual Cortex in Context

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

A central transformation that occurs within mammalian visual cortex is the change from linear, polarity-sensitive responses to nonlinear, polarity-insensitive responses. These neurons are classically labelled as either simple or complex, respectively, on the basis of their response linearity (Skottun et al., 1991). While the difference between cell classes is clear when the stimulus strength is high, reducing stimulus strength diminishes the differences between the cell types and causes some complex cells to respond as simple cells (Crowder et al., 2007; van Kleef et al., 2010; Hietanen et al., 2013). To understand the synaptic basis for this shift in behavior, we used in vivo whole-cell recordings while systematically shifting stimulus contrast. We find systematic shifts in the degree of complex cell responses in mouse primary visual cortex (V1) at the subthreshold level, demonstrating that synaptic inputs change in concert with the shifts in response linearity and that the change in response linearity is not simply due to the threshold nonlinearity. These shifts are consistent with a visual cortex model in which the recurrent amplification acts as a critical component in the generation of complex cell responses (Chance et al., 1999).

Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.

Visual response properties of neurons in the superficial layers of the superior colliculus of awake mouse

KEY POINTS

In rodents, including mice, the superior colliculus is the major target of the retina, but its visual response is not well characterized. In the present study, extracellular recordings from single nerve cells in the superficial layers of the superior colliculus were made in awake, head-restrained mice, and their responses to visual stimuli were measured. It was found that these neurons show brisk, highly sensitive and short latency visual responses, a preference for black over white stimuli, and diverse responses to moving patterns. At least five broad classes can be defined by variation in functional properties among units. The results of the present study demonstrate that eye movements have a measurable impact on visual responses in awake animals and show how they may be mitigated in analyses.

ABSTRACT

The mouse is an increasingly important animal model of visual function in health and disease. In mice, most retinal signals are routed through the superficial layers of the midbrain superior colliculus, and it is well established that much of the visual behaviour of mice relies on activity in the superior colliculus. The functional organization of visual signals in the mouse superior colliculus is, however, not well established in awake animals. We therefore made extracellular recordings from the superficial layers of the superior colliculus in awake mice, while the animals were viewing visual stimuli including flashed spots and drifting gratings. We find that neurons in the superficial layers of the superior colliculus of awake mouse generally show short latency, brisk responses. Receptive fields are usually 'ON-OFF' with a preference for black stimuli, and are weakly non-linear in response to gratings and other forms of luminance modulation. Population responses to drifting gratings are highly contrast sensitive, with a robust response to spatial frequencies above 0.3 cycles degree-1 and temporal frequencies above 15 Hz. The receptive fields are also often speed-tuned or direction-selective. Analysis of the response across multiple stimulus dimensions reveals at least five functionally distinct groups of units. We also find that eye movements affect measurements of receptive field properties in awake animals, and show how these may be mitigated in analyses. Qualitatively similar responses were obtained in urethane-anaesthetized animals, although receptive fields in awake animals had higher contrast sensitivity, shorter visual latency and a stronger response to high temporal frequencies.

Stimulus vignetting and orientation selectivity in human visual cortex

Neural selectivity to orientation is one of the simplest and most thoroughly-studied cortical sensory features. Here, we show that a large body of research that purported to measure orientation tuning may have in fact been inadvertently measuring sensitivity to second-order changes in luminance, a phenomenon we term 'vignetting'. Using a computational model of neural responses in primary visual cortex (V1), we demonstrate the impact of vignetting on simulated V1 responses. We then used the model to generate a set of predictions, which we confirmed with functional MRI experiments in human observers. Our results demonstrate that stimulus vignetting can wholly determine the orientation selectivity of responses in visual cortex measured at a macroscopic scale, and suggest a reinterpretation of a well-established literature on orientation processing in visual cortex.

Development, form, and function of the mouse visual thalamus

The dorsal lateral geniculate nucleus (dLGN) of the thalamus is the exclusive relay of retinal information en route to the visual cortex. Although much of our understanding about dLGN comes from studies done in higher mammals, such as the cat and primate, the mouse as a model organism has moved to the forefront as a tractable experimental platform to examine cell type-specific relations. This review highlights our current knowledge about the development, structure, and function of the mouse dLGN.

A Fine-Scale Functional Logic to Convergence from Retina to Thalamus

Numerous well-defined classes of retinal ganglion cells innervate the thalamus to guide image-forming vision, yet the rules governing their convergence and divergence remain unknown. Using two-photon calcium imaging in awake mouse thalamus, we observed a functional arrangement of retinal ganglion cell axonal boutons in which coarse-scale retinotopic ordering gives way to fine-scale organization based on shared preferences for other visual features. Specifically, at the ∼6 μm scale, clusters of boutons from different axons often showed similar preferences for either one or multiple features, including axis and direction of motion, spatial frequency, and changes in luminance. Conversely, individual axons could "de-multiplex" information channels by participating in multiple, functionally distinct bouton clusters. Finally, ultrastructural analyses demonstrated that retinal axonal boutons in a local cluster often target the same dendritic domain. These data suggest that functionally specific convergence and divergence of retinal axons may impart diverse, robust, and often novel feature selectivity to visual thalamus.

Thalamocortical processing in vision

Visual information reaches the cerebral cortex through a major thalamocortical pathway that connects the lateral geniculate nucleus (LGN) of the thalamus with the primary visual area of the cortex (area V1). In humans, ∼3.4 million afferents from the LGN are distributed within a V1 surface of ∼2400 mm2, an afferent number that is reduced by half in the macaque and by more than two orders of magnitude in the mouse. Thalamocortical afferents are sorted in visual cortex based on the spatial position of their receptive fields to form a map of visual space. The visual resolution within this map is strongly correlated with total number of thalamic afferents that V1 receives and the area available to sort them. The ∼20,000 afferents of the mouse are only sorted by spatial position because they have to cover a large visual field (∼300 deg) within just 4 mm2 of V1 area. By contrast, the ∼500,000 afferents of the cat are also sorted by eye input and light/dark polarity because they cover a smaller visual field (∼200 deg) within a much larger V1 area (∼400 mm2), a sorting principle that is likely to apply also to macaques and humans. The increased precision of thalamic sorting allows building multiple copies of the V1 visual map for left/right eyes and light/dark polarities, which become interlaced to keep neurons representing the same visual point close together. In turn, this interlaced arrangement makes cortical neurons with different preferences for stimulus orientation to rotate around single cortical points forming a pinwheel pattern that allows more efficient processing of objects and visual textures.

Application of autofluorescence robotic histology for quantitative evaluation of the 3-dimensional morphology of murine articular cartilage

Murine models of osteoarthritis (OA) are increasingly important for understating pathogenesis and for testing new therapeutic approaches. Their translational potential is, however, limited by the reduced size of mouse limbs which requires a much higher resolution to evaluate their articular cartilage compared to clinical imaging tools. In experimental models, this tissue has been predominantly assessed by time-consuming histopathology using standardized semi-quantitative scoring systems. This study aimed to develop a novel imaging method for 3-dimensional (3D) histology of mouse articular cartilage, using a robotic system-termed here "3D histocutter"-which automatically sections tissue samples and serially acquires fluorescence microscopy images of each section. Tibiae dissected from C57Bl/6 mice, either naïve or OA-induced by surgical destabilization of the medial meniscus (DMM), were imaged using the 3D histocutter by exploiting tissue autofluorescence. Accuracy of 3D imaging was validated by ex vivo contrast-enhanced micro-CT and sensitivity to lesion detection compared with conventional histology. Reconstructions of tibiae obtained from 3D histocutter serial sections showed an excellent agreement with contrast-enhanced micro-CT reconstructions. Furthermore, osteoarthritic features, including articular cartilage loss and osteophytes, were also visualized. An in-house developed software allowed to automatically evaluate articular cartilage morphology, eliminating the subjectivity associated to semi-quantitative scoring and considerably increasing analysis throughput. The novelty of this methodology is, not only the increased throughput in imaging and evaluating mouse articular cartilage morphology starting from conventionally embedded samples, but also the ability to add the third dimension to conventional histomorphometry which might be useful to improve disease assessment in the model.

Cortically coordinated NREM thalamocortical oscillations play an essential, instructive role in visual system plasticity

Two long-standing questions in neuroscience are how sleep promotes brain plasticity and why some forms of plasticity occur preferentially during sleep vs. wake. Establishing causal relationships between specific features of sleep (e.g., network oscillations) and sleep-dependent plasticity has been difficult. Here we demonstrate that presentation of a novel visual stimulus (a single oriented grating) causes immediate, instructive changes in the firing of mouse lateral geniculate nucleus (LGN) neurons, leading to increased firing-rate responses to the presented stimulus orientation (relative to other orientations). However, stimulus presentation alone does not affect primary visual cortex (V1) neurons, which show response changes only after a period of subsequent sleep. During poststimulus nonrapid eye movement (NREM) sleep, LGN neuron overall spike-field coherence (SFC) with V1 delta (0.5-4 Hz) and spindle (7-15 Hz) oscillations increased, with neurons most responsive to the presented stimulus showing greater SFC. To test whether coherent communication between LGN and V1 was essential for cortical plasticity, we first tested the role of layer 6 corticothalamic (CT) V1 neurons in coherent firing within the LGN-V1 network. We found that rhythmic optogenetic activation of CT V1 neurons dramatically induced coherent firing in LGN neurons and, to a lesser extent, in V1 neurons in the other cortical layers. Optogenetic interference with CT feedback to LGN during poststimulus NREM sleep (but not REM or wake) disrupts coherence between LGN and V1 and also blocks sleep-dependent response changes in V1. We conclude that NREM oscillations relay information regarding prior sensory experience between the thalamus and cortex to promote cortical plasticity.

Different Modes of Visual Integration in the Lateral Geniculate Nucleus Revealed by Single-Cell-Initiated Transsynaptic Tracing

The thalamus receives sensory input from different circuits in the periphery. How these sensory channels are integrated at the level of single thalamic cells is not well understood. We performed targeted single-cell-initiated transsynaptic tracing to label the retinal ganglion cells that provide input to individual principal cells in the mouse lateral geniculate nucleus (LGN). We identified three modes of sensory integration by single LGN cells. In the first, 1–5 ganglion cells of mostly the same type converged from one eye, indicating a relay mode. In the second, 6–36 ganglion cells of different types converged from one eye, revealing a combination mode. In the third, up to 91 ganglion cells converged from both eyes, revealing a binocular combination mode in which functionally specialized ipsilateral inputs joined broadly distributed contralateral inputs. Thus, the LGN employs at least three modes of visual input integration, each exhibiting different degrees of specialization.

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Individual LGN cells integrate retinal inputs in one of three distinct modes

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Relay-mode cells integrate inputs from few retinal ganglion cells of mostly one type

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Combination- and binocular-mode cells combine inputs from many ganglion cell types

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The three integration modes exhibit different degrees of cell-type specialization

A Comparison of Visual Response Properties in the Lateral Geniculate Nucleus and Primary Visual Cortex of Awake and Anesthetized Mice

The cerebral cortex of the mouse has become one of the most important systems for studying information processing and the neural correlates of behavior. Multiple studies have examined the first stages of visual cortical processing: primary visual cortex (V1) and its thalamic inputs from the dorsal lateral geniculate nucleus (dLGN), but more rarely in the lateral posterior nucleus (LP) in mice. Multiple single-unit surveys of dLGN and V1, both with electrophysiology and two-photon calcium imaging, have described receptive fields in anesthetized animals. Increasingly, awake animals are being used in physiological studies, so it is important to compare neuronal responses between awake and anesthetized state. We have performed a comprehensive survey of spatial and temporal response properties in V1, dLGN, and lateral posterior nucleus of both anesthetized and awake animals, using a common set of stimuli: drifting sine-wave gratings spanning a broad range of spatial and temporal parameters, and sparse noise stimuli consisting of flashed light and dark squares. Most qualitative receptive field parameters were found to be unchanged between the two states, such as most aspects of spatial processing, but there were significant differences in several parameters, most notably in temporal processing. Compared with anesthetized animals, the temporal frequency that evoked the peak response was shifted toward higher values in the dLGN of awake mice and responses were more sustained. Further, the peak response to a flashed stimulus was earlier in all three areas. Overall, however, receptive field properties in the anesthetized animal remain a good model for those in the awake animal.

SIGNIFICANCE STATEMENT

The primary visual cortex (V1) of the mouse and its inputs from visual thalamus (dLGN), have become a dominant model for studying information processing in the brain. Early surveys of visual response properties (receptive fields) were performed in anesthetized animals. Although most recent studies of V1 have been performed in awake animals to examine links between vision and behavior, there have been few comprehensive studies of receptive field properties in the awake mouse, especially in dLGN and lateral posterior nucleus. We have performed a comparative survey of receptive fields in dLGN, lateral posterior nucleus, and V1 in anesthetized and awake mice. We found multiple differences in processing of time-varying stimuli, whereas the spatial aspects of receptive fields remain comparatively unchanged.

Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus

Comparative physiological and anatomical studies have greatly advanced our understanding of sensory systems. Many lines of evidence show that the murine lateral geniculate nucleus (LGN) has unique attributes, compared with other species such as cat and monkey. For example, in rodent, thalamic receptive field structure is markedly diverse, and many cells are sensitive to stimulus orientation and direction. To explore shared and different strategies of synaptic integration across species, we made whole-cell recordings in vivo from the murine LGN during the presentation of visual stimuli, analyzed the results with different computational approaches, and compared our findings with those from cat. As for carnivores, murine cells with classical center-surround receptive fields had a "push-pull" structure of excitation and inhibition within a given On or Off subregion. These cells compose the largest single population in the murine LGN (∼40%), indicating that push-pull is key in the form vision pathway across species. For two cell types with overlapping On and Off responses, which recalled either W3 or suppressed-by-contrast ganglion cells in murine retina, inhibition took a different form and was most pronounced for spatially extensive stimuli. Other On-Off cells were selective for stimulus orientation and direction. In these cases, retinal inputs were tuned and, for oriented cells, the second-order subunit of the receptive field predicted the preferred angle. By contrast, suppression was not tuned and appeared to sharpen stimulus selectivity. Together, our results provide new perspectives on the role of excitation and inhibition in retinothalamic processing.

SIGNIFICANCE STATEMENT

We explored the murine lateral geniculate nucleus from a comparative physiological perspective. In cat, most retinal cells have center-surround receptive fields and push-pull excitation and inhibition, including neurons with the smallest (highest acuity) receptive fields. The same is true for thalamic relay cells. In mouse retina, the most numerous cell type has the smallest receptive fields but lacks push-pull. The most common receptive field in rodent thalamus, however, is center-surround with push-pull. Thus, receptive field structure supersedes size per se for form vision. Further, for many orientation-selective cells, the second-order component of the receptive field aligned with stimulus preference, whereas suppression was untuned. Thus, inhibition may improve spatial resolution and sharpen other forms of selectivity in rodent lateral geniculate nucleus.

An emergentist perspective on the origin of number sense

The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a 'nativist' stance on the origin of number sense. Here, we tackle this issue within the 'emergentist' perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense.This article is part of a discussion meeting issue 'The origins of numerical abilities'.

Organization of the dorsal lateral geniculate nucleus in the mouse

The dorsal lateral geniculate nucleus (dLGN) of the thalamus is the principal conduit for visual information from retina to visual cortex. Viewed initially as a simple relay, recent studies in the mouse reveal far greater complexity in the way input from the retina is combined, transmitted, and processed in dLGN. Here we consider the structural and functional organization of the mouse retinogeniculate pathway by examining the patterns of retinal projections to dLGN and how they converge onto thalamocortical neurons to shape the flow of visual information to visual cortex.

Neural Elements for Predictive Coding

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).