Cone photoreceptor contributions to noise and correlations in the retinal output

S cone increments and decrements: Nearly-linear perceptual scales and variable noise

Two psychophysical experiments investigated perceptual differences between increases and decreases in stimulation of the short-wavelength (S) cone photoreceptors. In Experiment 1, observers' suprathreshold perceptual scale responses to S cone stimulation were estimated using the Maximum Likelihood Difference Scaling (MLDS) procedure. In Experiment 2, observers' pedestal discrimination thresholds were measured with a two alternative forced choice (2AFC) method. Both experiments were performed using incremental (S+) and decremental (S-) contrasts separately. Substantial asymmetry between S+ and S- was found in pedestal discrimination thresholds, but not in S+ and S- perceptual scales: perceived S cone contrast was nearly linear with S cone contrast for both polarities. To reconcile perceptual scales and thresholds, a model is proposed in which the noise in the S cone pathway is assumed to be proportional to the square root of stimulus contrast. The model works well for both the perceptual scales and forced-choice discrimination, indicating that S+ and S- signals are processed in an asymmetrical way, likely due to the physiological differences between S ON and S OFF pathways.

Luminance invariant encoding in mouse primary visual cortex

The visual system adapts to maintain sensitivity and selectivity over a large range of luminance intensities. One way that the retina maintains sensitivity across night and day is by switching between rod and cone photoreceptors, which alters the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to transmit visual information to the brain across environmental conditions, the code used for that transmission varies. To determine how downstream targets encode visual scenes across light levels, we measured the effects of luminance adaptation on thalamic and cortical population activity. While changes in the retinal output are evident in the lateral geniculate nucleus (LGN), selectivity in the primary visual cortex (V1) is largely invariant to the changes in luminance. We show that the visual system could maintain sensitivity across environmental conditions without altering cortical selectivity through the convergence of parallel functional pathways from the thalamus to the cortex.

Nonlinear receptive fields evoke redundant retinal coding of natural scenes

The role of the vertebrate retina in early vision is generally described by the efficient coding hypothesis1,2, which predicts that the retina reduces the redundancy inherent in natural scenes3 by discarding spatiotemporal correlations while preserving stimulus information4. It is unclear, however, whether the predicted decorrelation and redundancy reduction in the activity of ganglion cells, the retina's output neurons, hold under gaze shifts, which dominate the dynamics of the natural visual input5. We show here that species-specific gaze patterns in natural stimuli can drive correlated spiking responses both in and across distinct types of ganglion cells in marmoset as well as mouse retina. These concerted responses disrupt redundancy reduction to signal fixation periods with locally high spatial contrast. Model-based analyses of ganglion cell responses to natural stimuli show that the observed response correlations follow from nonlinear pooling of ganglion cell inputs. Our results indicate cell-type-specific deviations from efficient coding in retinal processing of natural gaze shifts.

GABAergic amacrine cells balance biased chromatic information in the mouse retina

The retina extracts chromatic information present in an animal's environment. How this information is processed in the retina is not well understood. In the mouse, chromatic information is not collected equally throughout the retina. Green and UV signals are primarily detected in the dorsal and ventral retina, respectively. However, at the output of the retina, chromatic tuning is more mixed throughout the retina. This suggests that lateral processing by inhibitory amacrine cells shapes chromatic information at the retinal output. We systematically surveyed the chromatic responses of dendritic processes of the 40+ GABAergic amacrine cell types. We identified 25 functional types with distinct chromatic and achromatic properties. We used pharmacology and a biologically inspired deep learning model to explore how inhibition and excitation shape the properties of functional types. Our data suggest that amacrine cells balance the biased spectral tuning of excitation, thereby supporting diversity of chromatic information at the retinal output.

Diffusion Optics Technology (DOT): A Myopia Control Spectacle Lens Based on Contrast Theory

Diffusion optics Technology (DOT) myopia control spectacle lenses are based on contrast theory. This innovative theory represents a radical departure from the classical concept of visual deprivation myopia. However, traditional theories have evolved, arriving at remarkably similar solutions for myopia control as the DOT lenses. Nonetheless, contrast theory still represents a departure from mainstream theories. Here, in an effort to resolve discrepancies, we review the science behind contrast theory and compare it to more conventional blur and defocus theories. Finally, we consider the implications of the different theories for the rational design of myopia control solutions.

Clarification on the understanding of contrast theory in relation to the article "ON and OFF receptive field processing in the presence of optical scattering": comment

We are writing to address errors of misrepresentation in the article "ON and OFF receptive field processing in the presence of optical scattering" [Biomed. Opt. Express14, 2618 (2023)10.1364/BOE.489117]. In their investigation of predictions of "contrast theory" to explain the efficacy of diffusion optics technology (DOT), a myopia control lens design [Br. J. Ophthalmol.107, 1709 (2023)10.1136/bjo-2021-321005], Breher et al. incorrectly indicated that our contrast theory proposed that the association between cone opsin gene splicing defects and myopia was due to differential involvement in ON- and OFF-visual pathways. In addition, the Authors write that we have "hypothesized enhanced ON contrast sensitivity in myopes," but we predict the opposite.

Origin of Discrete and Continuous Dark Noise in Rod Photoreceptors

The detection of a single photon by a rod photoreceptor is limited by two sources of physiological noise, called discrete and continuous noise. Discrete noise occurs as intermittent current deflections with a waveform very similar to that of the single-photon response to real light and is thought to be produced by spontaneous activation of rhodopsin. Continuous noise occurs as random and continuous fluctuations in outer-segment current and is usually attributed to some intermediate in the phototransduction cascade. To confirm the origin of these noise sources, we have recorded from retinas of mouse lines with rods having reduced levels of rhodopsin, transducin, or phosphodiesterase. We show that the rate of discrete noise is diminished in proportion to the decrease in rhodopsin concentration, and that continuous noise is independent of transducin concentration but clearly elevated when the level of phosphodiesterase is reduced. Our experiments provide new molecular evidence that discrete noise is indeed produced by rhodopsin itself, and that continuous noise is generated by spontaneous activation of phosphodiesterase resulting in random fluctuations in outer-segment current.

Little information loss with red-green color deficient vision in natural environments

Inherited color vision deficiency affects red-green discrimination in about one in twelve men from European populations. Its effects have been studied mainly in primitive foraging but also in detecting blushing and breaking camouflage. Yet there is no obvious relationship between these specific tasks and vision in the real world. The aim here was to quantify the impact of color vision deficiency by estimating computationally the information available to observers about colored surfaces in natural scenes. With representative independent sets of 50 and 100 hyperspectral images, estimated information was found to be only a little less in red-green color vision deficiency than in normal trichromacy. Colorimetric analyses revealed the importance of large lightness variations within scenes, small redness-greenness variations, and uneven frequencies of different colored surfaces. While red-green color vision deficiency poses challenges in some tasks, it has much less effect on gaining information from natural environments.

Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.

Information gains from commercial spectral filters in anomalous trichromacy

Red-green color discrimination is compromised in anomalous trichromacy, the most common inherited color vision deficiency. This computational analysis tested whether three commercial optical filters with medium-to-long-wavelength stop bands increased information about colored surfaces. The surfaces were sampled from 50 hyperspectral images of outdoor scenes. At best, potential gains in the effective number of surfaces discriminable solely by color reached 9% in protanomaly and 15% in deuteranomaly, much less than with normal trichromacy. Gains were still less with lower scene illumination and more severe color vision deficiency. Stop-band filters may offer little improvement in objective real-world color discrimination.

Synchronous inhibitory pathways create both efficiency and diversity in the retina

Complex connections in neural circuits make it difficult to quantitatively assign even the most basic neural computations to the actions of specific neurons. Retinal ganglion cells are most sensitive to changes in intensity across space and over time. This property, caused by a region known as the receptive field surround, improves information transmission about natural scenes. We dynamically manipulated individual interneurons to directly measure their effect on retinal receptive fields, finding that two inhibitory neuron types, horizontal cells and amacrine cells, synchronously create the same contribution to the receptive field surround at different spatial scales. By analyzing large populations of ganglion cells, we show that this arrangement increases diversity in retinal signaling while preserving maximal information transmission about natural scenes.

Sensory receptive fields combine features that originate in different neural pathways. Retinal ganglion cell receptive fields compute intensity changes across space and time using a peripheral region known as the surround, a property that improves information transmission about natural scenes. The visual features that construct this fundamental property have not been quantitatively assigned to specific interneurons. Here, we describe a generalizable approach using simultaneous intracellular and multielectrode recording to directly measure and manipulate the sensory feature conveyed by a neural pathway to a downstream neuron. By directly controlling the gain of individual interneurons in the circuit, we show that rather than transmitting different temporal features, inhibitory horizontal cells and linear amacrine cells synchronously create the linear surround at different spatial scales and that these two components fully account for the surround. By analyzing a large population of ganglion cells, we observe substantial diversity in the relative contribution of amacrine and horizontal cell visual features while still allowing individual cells to increase information transmission under the statistics of natural scenes. Established theories of efficient coding have shown that optimal information transmission under natural scenes allows a diverse set of receptive fields. Our results give a mechanism for this theory, showing how distinct neural pathways synthesize a sensory computation and how this architecture both generates computational diversity and achieves the objective of high information transmission.

An image reconstruction framework for characterizing initial visual encoding

We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond psychophysical discrimination, takes into account the statistical regularities of the visual environment, and provides a unifying framework for answering a wide range of questions regarding the visual front end. Using the error in the reconstructions as a metric, we analyzed variations of the number of different photoreceptor types on human retina as an optimal design problem. In addition, the reconstructions allow both visualization and quantification of information loss due to physiological optics and cone mosaic sampling, and how these vary with eccentricity. Furthermore, in simulations of color deficiencies and interferometric experiments, we found that the reconstructed images provide a reasonable proxy for modeling subjects' percepts. Lastly, we used the reconstruction-based observer for the analysis of psychophysical threshold, and found notable interactions between spatial frequency and chromatic direction in the resulting spatial contrast sensitivity function. Our method is widely applicable to experiments and applications in which the initial visual encoding plays an important role.

Asymmetries around the visual field: From retina to cortex to behavior

Visual performance varies around the visual field. It is best near the fovea compared to the periphery, and at iso-eccentric locations it is best on the horizontal, intermediate on the lower, and poorest on the upper meridian. The fovea-to-periphery performance decline is linked to the decreases in cone density, retinal ganglion cell (RGC) density, and V1 cortical magnification factor (CMF) as eccentricity increases. The origins of polar angle asymmetries are not well understood. Optical quality and cone density vary across the retina, but recent computational modeling has shown that these factors can only account for a small percentage of behavior. Here, we investigate how visual processing beyond the cone photon absorptions contributes to polar angle asymmetries in performance. First, we quantify the extent of asymmetries in cone density, midget RGC density, and V1 CMF. We find that both polar angle asymmetries and eccentricity gradients increase from cones to mRGCs, and from mRGCs to cortex. Second, we extend our previously published computational observer model to quantify the contribution of phototransduction by the cones and spatial filtering by mRGCs to behavioral asymmetries. Starting with photons emitted by a visual display, the model simulates the effect of human optics, cone isomerizations, phototransduction, and mRGC spatial filtering. The model performs a forced choice orientation discrimination task on mRGC responses using a linear support vector machine classifier. The model shows that asymmetries in a decision maker's performance across polar angle are greater when assessing the photocurrents than when assessing isomerizations and are greater still when assessing mRGC signals. Nonetheless, the polar angle asymmetries of the mRGC outputs are still considerably smaller than those observed from human performance. We conclude that cone isomerizations, phototransduction, and the spatial filtering properties of mRGCs contribute to polar angle performance differences, but that a full account of these differences will entail additional contribution from cortical representations.

Temporal vision: measures, mechanisms and meaning

Time is largely a hidden variable in vision. It is the condition for seeing interesting things such as spatial forms and patterns, colours and movements in the external world, and yet is not meant to be noticed in itself. Temporal aspects of visual processing have received comparatively little attention in research. Temporal properties have been made explicit mainly in measurements of resolution and integration in simple tasks such as detection of spatially homogeneous flicker or light pulses of varying duration. Only through a mechanistic understanding of their basis in retinal photoreceptors and circuits can such measures guide modelling of natural vision in different species and illuminate functional and evolutionary trade-offs. Temporal vision research would benefit from bridging traditions that speak different languages. Towards that goal, I here review studies from the fields of human psychophysics, retinal physiology and neuroethology, with a focus on fundamental constraints set by early vision.

Summary: Simple measures of temporal vision such as the critical flicker frequency can be useful for modelling natural vision only if their relationship to photoreceptor responses and retinal processing is understood.

Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions

Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.

Temporal filtering of luminance and chromaticity in macaque visual cortex

Contrast sensitivity peaks near 10 Hz for luminance modulations and at lower frequencies for modulations between equiluminant lights. This difference is rooted in retinal filtering, but additional filtering occurs in the cerebral cortex. To measure the cortical contributions to luminance and chromatic temporal contrast sensitivity, signals in the lateral geniculate nucleus (LGN) were compared to the behavioral contrast sensitivity of macaque monkeys. Long wavelength-sensitive (L) and medium wavelength-sensitive (M) cones were modulated in phase to produce a luminance modulation (L + M) or in counterphase to produce a chromatic modulation (L - M). The sensitivity of LGN neurons was well matched to behavioral sensitivity at low temporal frequencies but was approximately 7 times greater at high temporal frequencies. Similar results were obtained for L + M and L - M modulations. These results show that differences in the shapes of the luminance and chromatic temporal contrast sensitivity functions are due almost entirely to pre-cortical mechanisms.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

Synaptic inputs to broad thorny ganglion cells in macaque retina

In primates, broad thorny retinal ganglion cells are highly sensitive to small, moving stimuli. They have tortuous, fine dendrites with many short, spine-like branches that occupy three contiguous strata in the middle of the inner plexiform layer. The neural circuits that generate their responses to moving stimuli are not well-understood, and that was the goal of this study. A connectome from central macaque retina was generated by serial block-face scanning electron microscopy, a broad thorny cell was reconstructed, and its synaptic inputs were analyzed. It received fewer than 2% of its inputs from both ON and OFF types of bipolar cells; the vast majority of its inputs were from amacrine cells. The presynaptic amacrine cells were reconstructed, and seven types were identified based on their characteristic morphology. Two types of narrow-field cells, knotty bistratified Type 1 and wavy multistratified Type 2, were identified. Two types of medium-field amacrine cells, ON starburst and spiny, were also presynaptic to the broad thorny cell. Three types of wide-field amacrine cells, wiry Type 2, stellate wavy, and semilunar Type 2, also made synapses onto the broad thorny cell. Physiological experiments using a macaque retinal preparation in vitro confirmed that broad thorny cells received robust excitatory input from both the ON and the OFF pathways. Given the paucity of bipolar cell inputs, it is likely that amacrine cells provided much of the excitatory input, in addition to inhibitory input.

Efficient population coding depends on stimulus convergence and source of noise

Sensory organs transmit information to downstream brain circuits using a neural code comprised of spikes from multiple neurons. According to the prominent efficient coding framework, the properties of sensory populations have evolved to encode maximum information about stimuli given biophysical constraints. How information coding depends on the way sensory signals from multiple channels converge downstream is still unknown, especially in the presence of noise which corrupts the signal at different points along the pathway. Here, we calculated the optimal information transfer of a population of nonlinear neurons under two scenarios. First, a lumped-coding channel where the information from different inputs converges to a single channel, thus reducing the number of neurons. Second, an independent-coding channel when different inputs contribute independent information without convergence. In each case, we investigated information loss when the sensory signal was corrupted by two sources of noise. We determined critical noise levels at which the optimal number of distinct thresholds of individual neurons in the population changes. Comparing our system to classical physical systems, these changes correspond to first- or second-order phase transitions for the lumped- or the independent-coding channel, respectively. We relate our theoretical predictions to coding in a population of auditory nerve fibers recorded experimentally, and find signatures of efficient coding. Our results yield important insights into the diverse coding strategies used by neural populations to optimally integrate sensory stimuli in the presence of distinct sources of noise.

Predicting synchronous firing of large neural populations from sequential recordings

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells' responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible.

Seeing Beyond Violet: UV Cones Guide High-Resolution Prey-Capture Behavior in Fish

How can fish see tiny underwater prey invisible to human eyes? In this issue of Neuron, Yoshimatsu et al. (2020) show that ultraviolet light and a rich set of fine-tuned anatomical and neural specializations originating in ultraviolet-sensitive cones underlie high-resolution prey-capture behavior in larval zebrafish.

Fovea-like Photoreceptor Specializations Underlie Single UV Cone Driven Prey-Capture Behavior in Zebrafish

In the eye, the function of same-type photoreceptors must be regionally adjusted to process a highly asymmetrical natural visual world. Here, we show that UV cones in the larval zebrafish area temporalis are specifically tuned for UV-bright prey capture in their upper frontal visual field, which may use the signal from a single cone at a time. For this, UV-photon detection probability is regionally boosted more than 10-fold. Next, in vivo two-photon imaging, transcriptomics, and computational modeling reveal that these cones use an elevated baseline of synaptic calcium to facilitate the encoding of bright objects, which in turn results from expressional tuning of phototransduction genes. Moreover, the light-driven synaptic calcium signal is regionally slowed by interactions with horizontal cells and later accentuated at the level of glutamate release driving retinal networks. These regional differences tally with variations between peripheral and foveal cones in primates and hint at a common mechanistic origin.

Efficient Coding by Midget and Parasol Ganglion Cells in the Human Retina

In humans, midget and parasol ganglion cells account for most of the input from the eyes to the brain. Yet, how they encode visual information is unknown. Here, we perform large-scale multi-electrode array recordings from retinas of treatment-naive patients who underwent enucleation surgery for choroidal malignant melanomas. We identify robust differences in the function of midget and parasol ganglion cells, consistent asymmetries between their ON and OFF types (that signal light increments and decrements, respectively) and divergence in the function of human versus non-human primate retinas. Our computational analyses reveal that the receptive fields of human midget and parasol ganglion cells divide naturalistic movies into adjacent spatiotemporal frequency domains with equal stimulus power, while the asymmetric response functions of their ON and OFF types simultaneously maximize stimulus coverage and information transmission and minimize metabolic cost. Thus, midget and parasol ganglion cells in the human retina efficiently encode our visual environment.

Connectomic analysis reveals an interneuron with an integral role in the retinal circuit for night vision

Night vision in mammals depends fundamentally on rod photoreceptors and the well-studied rod bipolar (RB) cell pathway. The central neuron in this pathway, the AII amacrine cell (AC), exhibits a spatially tuned receptive field, composed of an excitatory center and an inhibitory surround, that propagates to ganglion cells, the retina's projection neurons. The circuitry underlying the surround of the AII, however, remains unresolved. Here, we combined structural, functional and optogenetic analyses of the mouse retina to discover that surround inhibition of the AII depends primarily on a single interneuron type, the NOS-1 AC: a multistratified, axon-bearing GABAergic cell, with dendrites in both ON and OFF synaptic layers, but with a pure ON (depolarizing) response to light. Our study demonstrates generally that novel neural circuits can be identified from targeted connectomic analyses and specifically that the NOS-1 AC mediates long-range inhibition during night vision and is a major element of the RB pathway.

Selectivity to approaching motion in retinal inputs to the dorsal visual pathway

To efficiently navigate through the environment and avoid potential threats, an animal must quickly detect the motion of approaching objects. Current models of primate vision place the origins of this complex computation in the visual cortex. Here, we report that detection of approaching motion begins in the retina. Several ganglion cell types, the retinal output neurons, show selectivity to approaching motion. Synaptic current recordings from these cells further reveal that this preference for approaching motion arises in the interplay between presynaptic excitatory and inhibitory circuit elements. These findings demonstrate how excitatory and inhibitory circuits interact to mediate an ethologically relevant neural function. Moreover, the elementary computations that detect approaching motion begin early in the visual stream of primates.

Temporal information loss in the macaque early visual system

Stimuli that modulate neuronal activity are not always detectable, indicating a loss of information between the modulated neurons and perception. To identify where in the macaque visual system information about periodic light modulations is lost, signal-to-noise ratios were compared across simulated cone photoreceptors, lateral geniculate nucleus (LGN) neurons, and perceptual judgements. Stimuli were drifting, threshold-contrast Gabor patterns on a photopic background. The sensitivity of LGN neurons, extrapolated to populations, was similar to the monkeys' at low temporal frequencies. At high temporal frequencies, LGN sensitivity exceeded the monkeys' and approached the upper bound set by cone photocurrents. These results confirm a loss of high-frequency information downstream of the LGN. However, this loss accounted for only about 5% of the total. Phototransduction accounted for essentially all of the rest. Together, these results show that low temporal frequency information is lost primarily between the cones and the LGN, whereas high-frequency information is lost primarily within the cones, with a small additional loss downstream of the LGN.

The association between L:M cone ratio, cone opsin genes and myopia susceptibility

In syndromic forms of myopia caused by long (L) to middle (M) wavelength (L/M) interchange mutations, erroneous contrast signals from ON-bipolar cells activated by cones with different levels of opsin expression are suggested to make the eye susceptible to increased growth. This susceptibility is modulated by the L:M cone ratio. Here, we examined L and M opsin genes, L:M cone ratios and their association with common refractive errors in a population with low myopia prevalence. Cycloplegic autorefraction and ocular biometry were obtained for Norwegian genetically-confirmed normal trichromats. L:M cone ratios were estimated from spectral sensitivity functions measured with full-field ERG, after adjusting for individual differences in the wavelength of peak absorption deduced from cone opsin genetics. Mean L:M cone ratios and the frequency of alanine at L opsin position 180 were higher in males than what has been reported in males in populations with high myopia prevalence. High L:M cone ratios in females were associated with lower degree of myopia, and myopia was more frequent in females who were heterozygous for L opsin exon 3 haplotypes than in those who were homozygous. The results suggest that the L:M cone ratio, combined with milder versions of L opsin gene polymorphisms, may play a role in common myopia. This may in part explain the low myopia prevalence in Norwegian adolescents and why myopia prevalence was higher in females who were heterozygous for the L opsin exon 3 haplotype, since females are twice as likely to have genetic polymorphisms carried on the X-chromosome.

Optimal Noise-Canceling Networks

Natural and artificial networks, from the cerebral cortex to large-scale power grids, face the challenge of converting noisy inputs into robust signals. The input fluctuations often exhibit complex yet statistically reproducible correlations that reflect underlying internal or environmental processes such as synaptic noise or atmospheric turbulence. This raises the practically and biophysically relevant question of whether and how noise filtering can be hard wired directly into a network's architecture. By considering generic phase oscillator arrays under cost constraints, we explore here analytically and numerically the design, efficiency, and topology of noise-canceling networks. Specifically, we find that when the input fluctuations become more correlated in space or time, optimal network architectures become sparser and more hierarchically organized, resembling the vasculature in plants or animals. More broadly, our results provide concrete guiding principles for designing more robust and efficient power grids and sensor networks.

Range, routing and kinetics of rod signaling in primate retina

Stimulus- or context-dependent routing of neural signals through parallel pathways can permit flexible processing of diverse inputs. For example, work in mouse shows that rod photoreceptor signals are routed through several retinal pathways, each specialized for different light levels. This light-level-dependent routing of rod signals has been invoked to explain several human perceptual results, but it has not been tested in primate retina. Here, we show, surprisingly, that rod signals traverse the primate retina almost exclusively through a single pathway – the dedicated rod bipolar pathway. Identical experiments in mouse and primate reveal substantial differences in how rod signals traverse the retina. These results require reevaluating human perceptual results in terms of flexible computation within this single pathway. This includes a prominent speeding of rod signals with light level – which we show is inherited directly from the rod photoreceptors themselves rather than from different pathways with distinct kinetics.

Convergence and Divergence of CRH Amacrine Cells in Mouse Retinal Circuitry

Inhibitory interneurons sculpt the outputs of excitatory circuits to expand the dynamic range of information processing. In mammalian retina, >30 types of amacrine cells provide lateral inhibition to vertical, excitatory bipolar cell circuits, but functional roles for only a few amacrine cells are well established. Here, we elucidate the function of corticotropin-releasing hormone (CRH)-expressing amacrine cells labeled in Cre-transgenic mice of either sex. CRH cells costratify with the ON alpha ganglion cell, a neuron highly sensitive to positive contrast. Electrophysiological and optogenetic analyses demonstrate that two CRH types (CRH-1 and CRH-3) make GABAergic synapses with ON alpha cells. CRH-1 cells signal via graded membrane potential changes, whereas CRH-3 cells fire action potentials. Both types show sustained ON-type responses to positive contrast over a range of stimulus conditions. Optogenetic control of transmission at CRH-1 synapses demonstrates that these synapses are tuned to low temporal frequencies, maintaining GABA release during fast hyperpolarizations during brief periods of negative contrast. CRH amacrine cell output is suppressed by prolonged negative contrast, when ON alpha ganglion cells continue to receive inhibitory input from converging OFF-pathway amacrine cells; the converging ON- and OFF-pathway inhibition balances tonic excitatory drive to ON alpha cells. Previously, it was demonstrated that CRH-1 cells inhibit firing by suppressed-by-contrast (SbC) ganglion cells during positive contrast. Therefore, divergent outputs of CRH-1 cells inhibit two ganglion cell types with opposite responses to positive contrast. The opposing responses of ON alpha and SbC ganglion cells are explained by differing excitation/inhibition balance in the two circuits.SIGNIFICANCE STATEMENT A goal of neuroscience research is to explain the function of neural circuits at the level of specific cell types. Here, we studied the function of specific types of inhibitory interneurons, corticotropin-releasing hormone (CRH) amacrine cells, in the mouse retina. Genetic tools were used to identify and manipulate CRH cells, which make GABAergic synapses with a well studied ganglion cell type, the ON alpha cell. CRH cells converge with other types of amacrine cells to tonically inhibit ON alpha cells and balance their high level of excitation. CRH cells diverge to different types of ganglion cell, the unique properties of which depend on their balance of excitation and inhibition.

Contrast-dependent red-green hue shift

On bright surrounds, red-green-balanced yellow targets become greenish brown with decreased target luminance, and red-green-balanced brown targets become reddish yellow with increased target luminance. These effects imply luminance- and/or contrast-dependent weighting of M- and L-cone signals in post-receptoral pathways. We show psychophysically that luminance contrast between the surround and the target is the primary determinant of the magnitude of red-green hue shift, requiring surround luminance at least twice the target luminance and increasing with further increases of surround/target contrast. There is a much smaller effect of absolute stimulus luminance, with dimmer stimuli showing slightly larger hue shifts. To evaluate a possible retinal origin of the changes in cone-signal weightings underlying the hue shift, we recorded spike responses from both ON- and OFF-center midget ganglion cells in peripheral primate retina. We found no evidence that the relative strength of L- and M-cone post-receptoral responses changed systematically with change of surround irradiance. Nor was there any systematic difference between ON- and OFF-subtypes. This suggests that the change in cone signal weighting occurs later in the visual system.

Neural Mechanisms Mediating Motion Sensitivity in Parasol Ganglion Cells of the Primate Retina

Considerable theoretical and experimental effort has been dedicated to understanding how neural circuits detect visual motion. In primates, much is known about the cortical circuits that contribute to motion processing, but the role of the retina in this fundamental neural computation is poorly understood. Here, we used a combination of extracellular and whole-cell recording to test for motion sensitivity in the two main classes of output neurons in the primate retina-midget (parvocellular-projecting) and parasol (magnocellular-projecting) ganglion cells. We report that parasol, but not midget, ganglion cells are motion sensitive. This motion sensitivity is present in synaptic excitation and disinhibition from presynaptic bipolar cells and amacrine cells, respectively. Moreover, electrical coupling between neighboring bipolar cells and the nonlinear nature of synaptic release contribute to the observed motion sensitivity. Our findings indicate that motion computations arise far earlier in the primate visual stream than previously thought.

Optimal Information Transmission by Overlapping Retinal Cell Mosaics

The retina provides an excellent system for understanding the trade-offs that influence distributed information processing across multiple neuron types. We focus here on the problem faced by the visual system of allocating a limited number neurons to encode different visual features at different spatial locations. The retina needs to solve three competing goals: 1) encode different visual features, 2) maximize spatial resolution for each feature, and 3) maximize accuracy with which each feature is encoded at each location. There is no current understanding of how these goals are optimized together. While information theory provides a platform for theoretically solving these problems, evaluating information provided by the responses of large neuronal arrays is in general challenging. Here we present a solution to this problem in the case where multi-dimensional stimuli can be decomposed into approximately independent components that are subsequently coupled by neural responses. Using this approach we quantify information transmission by multiple overlapping retinal ganglion cell mosaics. In the retina, translation invariance of input signals makes it possible to use Fourier basis as a set of independent components. The results reveal a transition where one high-density mosaic becomes less informative than two or more overlapping lower-density mosaics. The results explain differences in the fractions of multiple cell types, predict the existence of new retinal ganglion cell subtypes, relative distribution of neurons among cell types and differences in their nonlinear and dynamical response properties.

Flexible Neural Hardware Supports Dynamic Computations in Retina

The ability of the retina to adapt to changes in mean light intensity and contrast is well known. Classically, however, adaptation is thought to affect gain but not to change the visual modality encoded by a given type of retinal neuron. Recent findings reveal unexpected dynamic properties in mouse retinal neurons that challenge this view. Specifically, certain cell types change the visual modality they encode with variations in ambient illumination or following repetitive visual stimulation. These discoveries demonstrate that computations performed by retinal circuits with defined architecture can change with visual input. Moreover, they pose a major challenge for central circuits that must decode properties of the dynamic visual signal from retinal outputs.

Electrical synapses convey orientation selectivity in the mouse retina

Sensory neurons downstream of primary receptors are selective for specific stimulus features, and they derive their selectivity both from excitatory and inhibitory synaptic inputs from other neurons and from their own intrinsic properties. Electrical synapses, formed by gap junctions, modulate sensory circuits. Retinal ganglion cells (RGCs) are diverse feature detectors carrying visual information to the brain, and receive excitatory input from bipolar cells and inhibitory input from amacrine cells (ACs). Here we describe a RGC that relies on gap junctions, rather than chemical synapses, to convey its selectivity for the orientation of a visual stimulus. This represents both a new functional role of electrical synapses as the primary drivers of feature selectivity and a new circuit mechanism for orientation selectivity in the retina.

Visual input received by photoreceptors is relayed to retinal ganglion cells (RGCs), which have selectivity for inputs of certain orientations. Here, the authors show that gap junction-mediated input onto one type of RGC contributes to its orientation selectivity.

Asymmetry between ON and OFF α ganglion cells of mouse retina: integration of signal and noise from synaptic inputs

KEY POINTS

Bipolar and amacrine cells presynaptic to the ON sustained α cell of mouse retina provide currents with a higher signal-to-noise power ratio (SNR) than those presynaptic to the OFF sustained α cell. Yet the ON cell loses proportionately more SNR from synaptic inputs to spike output than the OFF cell does. The higher SNR of ON bipolar cells at the beginning of the ON pathway compensates for losses incurred by the ON ganglion cell, and improves the processing of positive contrasts.

ABSTRACT

ON and OFF pathways in the retina include functional pairs of neurons that, at first glance, appear to have symmetrically similar responses to brightening and darkening, respectively. Upon careful examination, however, functional pairs exhibit asymmetries in receptive field size and response kinetics. Until now, descriptions of how light-adapted retinal circuitry maintains a preponderance of signal over the noise have not distinguished between ON and OFF pathways. Here I present evidence of marked asymmetries between members of a functional pair of sustained α ganglion cells in the mouse retina. The ON cell exhibited a proportionately greater loss of signal-to-noise power ratio (SNR) from its presynaptic arrays to its postsynaptic currents. Thus the ON cell combines signal and noise from its presynaptic arrays of bipolar and amacrine cells less efficiently than the OFF cell does. Yet the inefficiency of the ON cell is compensated by its presynaptic arrays providing a higher SNR than the arrays presynaptic to the OFF cell, apparently to improve visual processing of positive contrasts. Dynamic clamp experiments were performed that introduced synaptic conductances into ON and OFF cells. When the amacrine-modulated conductance was removed, the ON cell's spike train exhibited an increase in SNR. The OFF cell, however, showed the opposite effect of removing amacrine input, which was a decrease in SNR. Thus ON and OFF cells have different modes of synaptic integration with direct effects on the SNR of the spike output.

A Mammalian Retinal Ganglion Cell Implements a Neuronal Computation That Maximizes the SNR of Its Postsynaptic Currents

Neurons perform computations by integrating excitatory and inhibitory synaptic inputs. Yet, it is rarely understood what computation is being performed, or how much excitation or inhibition this computation requires. Here we present evidence for a neuronal computation that maximizes the signal-to-noise power ratio (SNR). We recorded from OFF delta retinal ganglion cells in the guinea pig retina and monitored synaptic currents that were evoked by visual stimulation (flashing dark spots). These synaptic currents were mediated by a decrease in an outward current from inhibitory synapses (disinhibition) combined with an increase in an inward current from excitatory synapses. We found that the SNR of combined excitatory and disinhibitory currents was voltage sensitive, peaking at membrane potentials near resting potential. At the membrane potential for maximal SNR, the amplitude of each current, either excitatory or disinhibitory, was proportional to its SNR. Such proportionate scaling is the theoretically best strategy for combining excitatory and disinhibitory currents to maximize the SNR of their combined current. Moreover, as spot size or contrast changed, the amplitudes of excitatory and disinhibitory currents also changed but remained in proportion to their SNRs, indicating a dynamic rebalancing of excitatory and inhibitory currents to maximize SNR.SIGNIFICANCE STATEMENT We present evidence that the balance of excitatory and disinhibitory inputs to a type of retinal ganglion cell maximizes the signal-to-noise ratio power ratio (SNR) of its postsynaptic currents. This is significant because chemical synapses on a retinal ganglion cell require the probabilistic release of transmitter. Consequently, when the same visual stimulus is presented repeatedly, postsynaptic currents vary in amplitude. Thus, maximizing SNR may be a strategy for producing the most reliable signal possible given the inherent unreliability of synaptic transmission.

How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

Calcium dynamics change in degenerating cone photoreceptors

Cone photoreceptors (cones) are essential for high-resolution daylight vision and colour perception. Loss of cones in hereditary retinal diseases has a dramatic impact on human vision. The mechanisms underlying cone death are poorly understood, and consequently, there are no treatments available. Previous studies suggest a central role for calcium (Ca²⁺) homeostasis deficits in photoreceptor degeneration; however, direct evidence for this is scarce and physiological measurements of Ca²⁺in degenerating mammalian cones are lacking.Here, we took advantage of the transgenic HR2.1:TN-XL mouse line that expresses a genetically encoded Ca²⁺biosensor exclusively in cones. We cross-bred this line with mouse models for primary ("cone photoreceptor function loss-1", cpfl1) and secondary ("retinal degeneration-1", rd1) cone degeneration, respectively, and assessed resting Ca²⁺levels and light-evoked Ca²⁺responses in cones using two-photon imaging. We found that Ca²⁺dynamics were altered in cpfl1 cones, showing higher noise and variable Ca²⁺levels, with significantly wider distribution than for wild-type and rd1 cones. Unexpectedly, up to 21% of cpfl1 cones still displayed light-evoked Ca²⁺responses, which were larger and slower than wild-type responses. In contrast, genetically intact rd1 cones were characterized by lower noise and complete lack of visual function.Our study demonstrates alterations in cone Ca²⁺dynamics in both primary and secondary cone degeneration. Our results are consistent with the view that higher (fluctuating) cone Ca²⁺levels are involved in photoreceptor cell death in primary (cpfl1) but not in secondary (rd1) cone degeneration. These findings may guide the future development of therapies targeting photoreceptor Ca²⁺homeostasis.

Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code

Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent, and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction 2-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons.

Brains studying brains: look before you think in vision

Using our own brains to study our brains is extraordinary. For example, in vision this makes us naturally blind to our own blindness, since our impression of seeing our world clearly is consistent with our ignorance of what we do not see. Our brain employs its 'conscious' part to reason and make logical deductions using familiar rules and past experience. However, human vision employs many 'subconscious' brain parts that follow rules alien to our intuition. Our blindness to our unknown unknowns and our presumptive intuitions easily lead us astray in asking and formulating theoretical questions, as witnessed in many unexpected and counter-intuitive difficulties and failures encountered by generations of scientists. We should therefore pay a more than usual amount of attention and respect to experimental data when studying our brain. I show that this can be productive by reviewing two vision theories that have provided testable predictions and surprising insights.

The impact of inhibitory mechanisms in the inner retina on spatial tuning of RGCs

Spatial tuning properties of retinal ganglion cells (RGCs) are sharpened by lateral inhibition originating at both the outer and inner plexiform layers. Lateral inhibition in the retina contributes to local contrast enhancement and sharpens edges. In this study, we used dynamic clamp recordings to examine the contribution of inner plexiform inhibition, originating from spiking amacrine cells, to the spatial tuning of RGCs. This was achieved by injecting currents generated from physiologically recorded excitatory and inhibitory stimulus-evoked conductances, into different types of primate and mouse RGCs. We determined the effects of injections of size-dependent conductances in which presynaptic inhibition and/or direct inhibition onto RGCs were partly removed by blocking the activity of spiking amacrine cells. We found that inhibition originating from spiking amacrine cells onto bipolar cell terminals and onto RGCs, work together to sharpen the spatial tuning of RGCs. Furthermore, direct inhibition is crucial for preventing spike generation at stimulus offset. These results reveal how inhibitory mechanisms in the inner plexiform layer contribute to determining size tuning and provide specificity to stimulus polarity.

A thesaurus for a neural population code

Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns.

DOI:http://dx.doi.org/10.7554/eLife.06134.001

Our ability to perceive the world is dependent on information from our senses being passed between different parts of the brain. The information is encoded as patterns of electrical pulses or ‘spikes’, which other brain regions must be able to decipher. Cracking this code would thus enable us to predict the patterns of nerve impulses that would occur in response to specific stimuli, and ‘decode’ which stimuli had produced particular patterns of impulses.

This task is challenging in part because of its scale—vast numbers of stimuli are encoded by huge numbers of neurons that can send their spikes in many different combinations. Furthermore, neurons are inherently noisy and their response to identical stimuli may vary considerably in the number of spikes and their timing. This means that the brain cannot simply link a single unchanging pattern of firing with each stimulus, because these firing patterns are often distorted by biophysical noise.

Ganmor et al. have now modeled the effects of noise in a network of neurons in the retina (found at the back of the eye), and, in doing so, have provided insights into how the brain solves this problem. This has brought us a step closer to cracking the neural code. First, 10 second video clips of natural scenes and artificial stimuli were played on a loop to a sample of retina taken from a salamander, and the responses of nearly 100 neurons in the sample were recorded for two hours. Dividing the 10 second clip into short segments provided a series of 500 stimuli, which the network had been exposed to more than 600 times.

Ganmor et al. analyzed the responses of groups of 20 cells to each stimulus and found that physically similar firing patterns were not particularly likely to encode the same stimulus. This can be likened to the way that words such as ‘light’ and ‘night’ have similar structures but different meanings. Instead, the model reveals that each stimulus was represented by a cluster of firing patterns that bore little physical resemblance to one another, but which nevertheless conveyed the same meaning. To continue on with the previous example, this is similar to way that ‘light’ and ‘illumination’ have the same meaning but different structures.

Ganmor et al. use these new data to map the organization of the ‘vocabulary’ of populations of cells the retina, and put together a kind of ‘thesaurus’ that enables new activity patterns of the retina to be decoded and could be used to crack the neural code. Furthermore, the organization of ‘synonyms’ is strikingly similar to codes that are favored in many forms of telecommunication. In these man-made codes, codewords that represent different items are chosen to be so distinct from each other that even if they were corrupted by noise, they could be correctly deciphered. Correspondingly, in the retina, patterns that carry the same meaning occupy a distinct area, and new patterns can be interpreted based on their proximity to these clusters.

DOI:http://dx.doi.org/10.7554/eLife.06134.002

High Accuracy Decoding of Dynamical Motion from a Large Retinal Population

Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar's position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina's population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.

Photoreceptor ablation initiates the immediate loss of glutamate receptors in postsynaptic bipolar cells in retina

Structural changes underlying neurodegenerative diseases include dismantling of synapses, degradation of circuitry, and even massive rewiring. Our limited understanding of synapse dismantling stems from the inability to control the timing and extent of cell death. In this study, selective ablation of cone photoreceptors in live mouse retina and tracking of postsynaptic partners at the cone-to-ON cone bipolar cell synapse reveals that early reaction to cone loss involves rapid and local changes in postsynaptic glutamate receptor distribution. Glutamate receptors disappear with a time constant of 2 h. Furthermore, binding of glutamate receptors by agonists and antagonists is insufficient to rescue glutamate receptor loss, suggesting that receptor allocation depends on the physical presence of cones. These findings demonstrate that the initial step in synapse disassembly involves postsynaptic receptor loss rather than dendritic retraction, providing insight into the early stages of neurodegenerative disease.

How and why neural and motor variation are related

Movements are variable. Recent findings in smooth pursuit eye movements provide an explanation for motor variation in terms of the organization of the brain's sensory-motor pathways. Variation in sensory estimation is propagated through sensory-motor circuits and ultimately causes motor variation. The sensory origin of motor variation creates trial-by-trial correlations among the responses of neurons at each level of the sensory motor circuit, and between neural and behavioral responses. We suggest that motor variation is a compromise between multiple competing constraints. The brain strives for motor behavior that is 'good enough' in the face of constraints that tend to promote variation.

Critical and maximally informative encoding between neural populations in the retina

Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid-gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid-gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment.

Toward functional classification of neuronal types

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species.

Benefits of pathway splitting in sensory coding

In many sensory systems, the neural signal splits into multiple parallel pathways. For example, in the mammalian retina, ~20 types of retinal ganglion cells transmit information about the visual scene to the brain. The purpose of this profuse and early pathway splitting remains unknown. We examine a common instance of splitting into ON and OFF neurons excited by increments and decrements of light intensity in the visual scene, respectively. We test the hypothesis that pathway splitting enables more efficient encoding of sensory stimuli. Specifically, we compare a model system with an ON and an OFF neuron to one with two ON neurons. Surprisingly, the optimal ON-OFF system transmits the same information as the optimal ON-ON system, if one constrains the maximal firing rate of the neurons. However, the ON-OFF system uses fewer spikes on average to transmit this information. This superiority of the ON-OFF system is also observed when the two systems are optimized while constraining their mean firing rate. The efficiency gain for the ON-OFF split is comparable with that derived from decorrelation, a well known processing strategy of early sensory systems. The gain can be orders of magnitude larger when the ecologically important stimuli are rare but large events of either polarity. The ON-OFF system also provides a better code for extracting information by a linear downstream decoder. The results suggest that the evolution of ON-OFF diversification in sensory systems may be driven by the benefits of lowering average metabolic cost, especially in a world in which the relevant stimuli are sparse.