Concept: Receptive field
- Proceedings of the National Academy of Sciences of the United States of America
- Published about 5 years ago
Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON-center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes.
Visual abilities of the honey bee have been studied for more than 100 years, recently revealing unexpectedly sophisticated cognitive skills rivalling those of vertebrates. However, the physiological limits of the honey bee eye have been largely unaddressed and only studied in an unnatural, dark state. Using a bright display and intracellular recordings, we here systematically investigated the angular sensitivity across the light adapted eye of honey bee foragers. Angular sensitivity is a measure of photoreceptor receptive field size and thus small values indicate higher visual acuity. Our recordings reveal a fronto-ventral acute zone in which angular sensitivity falls below 1.9°, some 30% smaller than previously reported. By measuring receptor noise and responses to moving dark objects, we also obtained direct measures of the smallest features detectable by the retina. In the frontal eye, single photoreceptors respond to objects as small as 0.6° × 0.6°, with >99% reliability. This indicates that honey bee foragers possess significantly better resolution than previously reported or estimated behaviourally, and commonly assumed in modelling of bee acuity.
We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.
Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients. Here we show that these dynamical behaviours may in fact be understood as hallmarks of the specific representation and algorithm that the cortex employs to perform probabilistic inference. We demonstrate that a particular family of probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally maps onto the dynamics of excitatory-inhibitory neural networks. Specifically, we constructed a model of an excitatory-inhibitory circuit in primary visual cortex that performed HMC inference, and thus inherently gave rise to oscillations and transients. These oscillations were not mere epiphenomena but served an important functional role: speeding up inference by rapidly spanning a large volume of state space. Inference thus became an order of magnitude more efficient than in a non-oscillatory variant of the model. In addition, the network matched two specific properties of observed neural dynamics that would otherwise be difficult to account for using probabilistic inference. First, the frequency of oscillations as well as the magnitude of transients increased with the contrast of the image stimulus. Second, excitation and inhibition were balanced, and inhibition lagged excitation. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.
To accurately localize our limbs and guide movements toward external objects, the brain must represent the body and its surrounding (peripersonal) visual space. Specific multisensory neurons encode peripersonal space in the monkey brain, and neurobehavioral studies have suggested the existence of a similar representation in humans. However, because peripersonal space lacks a distinct perceptual correlate, its involvement in spatial and bodily perception remains unclear. Here, we show that applying brushstrokes in mid-air at some distance above a rubber hand-without touching it-in synchrony with brushstrokes applied to a participant’s hidden real hand results in the illusory sensation of a “magnetic force” between the brush and the rubber hand, which strongly correlates with the perception of the rubber hand as one’s own. In eight experiments, we characterized this “magnetic touch illusion” by using quantitative subjective reports, motion tracking, and behavioral data consisting of pointing errors toward the rubber hand in an intermanual pointing task. We found that the illusion depends on visuo-tactile synchrony and exhibits similarities with the visuo-tactile receptive field properties of peripersonal space neurons, featuring a non-linear decay at 40cm that is independent of gaze direction and follows changes in the rubber hand position. Moreover, the “magnetic force” does not penetrate physical barriers, thus further linking this phenomenon to body-specific visuo-tactile integration processes. These findings provide strong support for the notion that multisensory integration within peripersonal space underlies bodily self-attribution. Furthermore, we propose that the magnetic touch illusion constitutes a perceptual correlate of visuo-tactile integration in peripersonal space.
Skin on the foot sole plays an important role in postural control. Cooling the skin of the foot is often used to induce anaesthesia to determine the role of skin in motor and balance control. The effect of cooling on the four classes of mechanoreceptor in the skin is largely unknown and thus the aim of the current study was to characterize the effects of cooling on individual skin receptors in the foot sole. Such insight will better isolate individual receptor contributions to balance control. Using microneurography, we recorded 39 single nerve afferents innervating mechanoreceptors in the skin of the foot sole in humans. Afferents were identified as fast- or slowly-adapting type I or II (FA I n=16, FA II n=7, SA I n=6, SA II n=11). Receptor response to vibration was compared before and after cooling the receptive field (2-20 minutes). Overall, firing response was abolished in 30% of all receptors and this was equally distributed across receptor type (p=0.69). Longer cooling times were more likely to reduce firing response below 50% of baseline however some afferents responses were abolished with shorter cooling times (2-5 min.). Skin temperature was not a reliable indicator of the level of receptor activation, and often became uncoupled from receptor response levels, cautioning the use of this parameter as an indicator of anesthesia. When cooled, receptors preferentially coded lower frequencies in response to vibration. In response to a sustained indentation, SA receptors responded more like FA receptors, primarily coding ‘on-off’ events.
Every moment of every day, our skin and its embedded sensory neurons are bombarded with mechanical cues that we experience as pleasant or painful. Knowing the difference between innocuous and noxious mechanical stimuli is critical for survival and relies on the function of mechanoreceptor neurons that vary in their size, shape, and sensitivity. Their function is poorly understood at the molecular level. This review emphasizes the importance of integrating analysis at the molecular and cellular levels and focuses on the discovery of ion channel proteins coexpressed in the mechanoreceptors of worms, flies, and mice.
- The Journal of neuroscience : the official journal of the Society for Neuroscience
- Published about 6 years ago
Most nonprimate mammals possess dichromatic (“red-green color blind”) color vision based on short-wavelength-sensitive (S) and medium/long-wavelength-sensitive (ML) cone photoreceptor classes. However, the neural pathways carrying signals underlying the primitive “blue-yellow” axis of color vision in nonprimate mammals are largely unexplored. Here, we have characterized a population of color opponent (blue-ON) cells in recordings from the dorsal lateral geniculate nucleus of anesthetized cats. We found five points of similarity to previous descriptions of primate blue-ON cells. First, cat blue-ON cells receive ON-type excitation from S-cones, and OFF-type excitation from ML-cones. We found no blue-OFF cells. Second, the S- and ML-cone-driven receptive field regions of cat blue-ON cells are closely matched in size, consistent with specialization for detecting color contrast. Third, the receptive field center diameter of cat blue-ON cells is approximately three times larger than the center diameter of non-color opponent receptive fields at any eccentricity. Fourth, S- and ML-cones contribute weak surround inhibition to cat blue-ON cells. These data show that blue-ON receptive fields in cats are functionally very similar to blue-ON type receptive fields previously described in macaque and marmoset monkeys. Finally, cat blue-ON cells are found in the same layers as W-cells, which are thought to be homologous to the primate koniocellular system. Based on these data, we suggest that cat blue-ON cells are part of a “blue-yellow” color opponent system that is the evolutionary homolog of the blue-ON division of the koniocellular pathway in primates.
A key property of neurons in primary visual cortex (V1) is the distinction between simple and complex cells. Recent reports in cat visual cortex indicate the categorization of simple and complex can change depending on stimulus conditions. We investigated the stability of the simple/complex classification with changes in drive produced by either contrast or modulation by the extra-classical receptive field (eCRF). These two conditions were reported to increase the proportion of simple cells in cat cortex. The ratio of the modulation depth of the response (F1) to the elevation of response (F0) to a drifting grating (F1/F0 ratio) was used as the measure of simple/complex. The majority V1 complex cells remained classified as complex with decreasing contrast. Near contrast threshold an equal proportion of simple and complex cells changed their classification. The F1/F0 ratio was stable between optimal and large stimulus areas even for those neurons that showed strong eCRF suppression. There was no discernable overall affect of surrounding spatial context on the F1/F0 ratio. Simple - complex cell classification is relatively stable across a range of stimulus drives, either produced by contrast or eCRF suppression.
In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.