Primate retina trades single-photon detection for high-fidelity contrast encoding.

How the spike output of the retina enables human visual perception is not fully understood. Here, we address this at the sensitivity limit of vision by correlating human visual perception with the spike outputs of primate ON and OFF parasol (magnocellular) retinal ganglion cells in tightly matching stimulus conditions. We show that human vision at its ultimate sensitivity limit depends on the spike output of the ON but not the OFF retinal pathway. Consequently, nonlinear signal processing in the retinal ON pathway precludes perceptual detection of single photons in darkness but enables quantal-resolution discrimination of differences in light intensity.

Retinal OFF ganglion cells allow detection of quantal shadows at starlight.

Perception of light in darkness requires no more than a handful of photons, and this remarkable behavioral performance can be directly linked to a particular retinal circuit-the retinal ON pathway. However, the neural limits of shadow detection in very dim light have remained unresolved. Here, we unravel the neural mechanisms that determine the sensitivity of mice (CBA/CaJ) to light decrements at the lowest light levels by measuring signals from the most sensitive ON and OFF retinal ganglion cell types and by correlating their signals with visually guided behavior. We show that mice can detect shadows when only a few photon absorptions are missing among thousands of rods. Behavioral detection of such "quantal" shadows relies on the retinal OFF pathway and is limited by noise and loss of single-photon signals in retinal processing. Thus, in the dim-light regime, light increments and decrements are encoded separately via the ON and OFF retinal pathways, respectively.

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.

Paradoxical Rules of Spike Train Decoding Revealed at the Sensitivity Limit of Vision.

All sensory information is encoded in neural spike trains. It is unknown how the brain utilizes this neural code to drive behavior. Here, we unravel the decoding rules of the brain at the most elementary level by linking behavioral decisions to retinal output signals in a single-photon detection task. A transgenic mouse line allowed us to separate the two primary retinal outputs, ON and OFF pathways, carrying information about photon absorptions as increases and decreases in spiking, respectively. We measured the sensitivity limit of rods and the most sensitive ON and OFF ganglion cells and correlated these results with visually guided behavior using markerless head and eye tracking. We show that behavior relies only on the ON pathway even when the OFF pathway would allow higher sensitivity. Paradoxically, behavior does not rely on the spike code with maximal information but instead relies on a decoding strategy based on increases in spiking.

Describing complex cells in primary visual cortex: a comparison of context and multifilter LN models.

Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multifilter linear-nonlinear (LN) models and context models. Models are, however, never correct, and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: 1) we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions, and 2) we evaluate context models and multifilter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multifilter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multifilter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantizations of neural behavior. NEW & NOTEWORTHY We used data from complex cells in primary visual cortex to estimate a wide variety of receptive field models from two frameworks that have previously not been compared with each other. The models included traditionally used multifilter linear-nonlinear models and novel variants of context models. Using mutual information and correlation coefficients as performance measures, we showed that context models are superior for describing complex cells and that the novel context models performed the best.

Capturing contextual effects in spectro-temporal receptive fields.

Spectro-temporal receptive fields (STRFs) are thought to provide descriptive images of the computations performed by neurons along the auditory pathway. However, their validity can be questioned because they rely on a set of assumptions that are probably not fulfilled by real neurons exhibiting contextual effects, that is, nonlinear interactions in the time or frequency dimension that cannot be described with a linear filter. We used a novel approach to investigate how a variety of contextual effects, due to facilitating nonlinear interactions and synaptic depression, affect different STRF models, and if these effects can be captured with a context field (CF). Contextual effects were incorporated in simulated networks of spiking neurons, allowing one to define the true STRFs of the neurons. This, in turn, made it possible to evaluate the performance of each STRF model by comparing the estimations with the true STRFs. We found that currently used STRF models are particularly poor at estimating inhibitory regions. Specifically, contextual effects make estimated STRFs dependent on stimulus density in a contrasting fashion: inhibitory regions are underestimated at lower densities while artificial inhibitory regions emerge at higher densities. The CF was found to provide a solution to this dilemma, but only when it is used together with a generalized linear model. Our results therefore highlight the limitations of the traditional STRF approach and provide useful recipes for how different STRF models and stimuli can be used to arrive at reliable quantifications of neural computations in the presence of contextual effects. The results therefore push the purpose of STRF analysis from simply finding an optimal stimulus toward describing context-dependent computations of neurons along the auditory pathway.

Memory Stacking in Hierarchical Networks.

Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

Computational modelling suggests that temporal integration results from synaptic adaptation in auditory cortex.

Incoming sounds are represented in the context of preceding events, and this requires a memory mechanism that integrates information over time. Here, it was demonstrated that response adaptation, the suppression of neural responses due to stimulus repetition, might reflect a computational solution that auditory cortex uses for temporal integration. Adaptation is observed in single-unit measurements as two-tone forward masking effects and as stimulus-specific adaptation (SSA). In non-invasive observations, the amplitude of the auditory N1m response adapts strongly with stimulus repetition, and it is followed by response recovery (the so-called mismatch response) to rare deviant events. The current computational simulations described the serial core-belt-parabelt structure of auditory cortex, and included synaptic adaptation, the short-term, activity-dependent depression of excitatory corticocortical connections. It was found that synaptic adaptation is sufficient for columns to respond selectively to tone pairs and complex tone sequences. These responses were defined as combination sensitive, thus reflecting temporal integration, when a strong response to a stimulus sequence was coupled with weaker responses both to the time-reversed sequence and to the isolated sequence elements. The temporal complexity of the stimulus seemed to be reflected in the proportion of combination-sensitive columns across the different regions of the model. Our results suggest that while synaptic adaptation produces facilitation and suppression effects, including SSA and the modulation of the N1m response, its functional significance may actually be in its contribution to temporal integration. This integration seems to benefit from the serial structure of auditory cortex.