Multiple objects evoke fluctuating responses in several regions of the visual pathway.

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here, we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action.

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (for example, visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time. However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action. Impact Statement: We find neural fluctuations in multiple areas along the visual cortical hierarchy that could allow the brain to represent distinct co-occurring visual stimuli.

Coordinated multiplexing of information about separate objects in visual cortex.

Sensory receptive fields are large enough that they can contain more than one perceptible stimulus. How, then, can the brain encode information about each of the stimuli that may be present at a given moment? We recently showed that when more than one stimulus is present, single neurons can fluctuate between coding one vs. the other(s) across some time period, suggesting a form of neural multiplexing of different stimuli (Caruso et al., 2018). Here, we investigate (a) whether such coding fluctuations occur in early visual cortical areas; (b) how coding fluctuations are coordinated across the neural population; and (c) how coordinated coding fluctuations depend on the parsing of stimuli into separate vs. fused objects. We found coding fluctuations do occur in macaque V1 but only when the two stimuli form separate objects. Such separate objects evoked a novel pattern of V1 spike count ('noise') correlations involving distinct distributions of positive and negative values. This bimodal correlation pattern was most pronounced among pairs of neurons showing the strongest evidence for coding fluctuations or multiplexing. Whether a given pair of neurons exhibited positive or negative correlations depended on whether the two neurons both responded better to the same object or had different object preferences. Distinct distributions of spike count correlations based on stimulus preferences were also seen in V4 for separate objects but not when two stimuli fused to form one object. These findings suggest multiple objects evoke different response dynamics than those evoked by single stimuli, lending support to the multiplexing hypothesis and suggesting a means by which information about multiple objects can be preserved despite the apparent coarseness of sensory coding.

Efficient coding, channel capacity, and the emergence of retinal mosaics.

Among the most striking features of retinal organization is the grouping of its output neurons, the retinal ganglion cells (RGCs), into a diversity of functional types. Each of these types exhibits a mosaic-like organization of receptive fields (RFs) that tiles the retina and visual space. Previous work has shown that many features of RGC organization, including the existence of ON and OFF cell types, the structure of spatial RFs, and their relative arrangement, can be predicted on the basis of efficient coding theory. This theory posits that the nervous system is organized to maximize information in its encoding of stimuli while minimizing metabolic costs. Here, we use efficient coding theory to present a comprehensive account of mosaic organization in the case of natural videos as the retinal channel capacity-the number of simulated RGCs available for encoding-is varied. We show that mosaic density increases with channel capacity up to a series of critical points at which, surprisingly, new cell types emerge. Each successive cell type focuses on increasingly high temporal frequencies and integrates signals over larger spatial areas. In addition, we show theoretically and in simulation that a transition from mosaic alignment to anti-alignment across pairs of cell types is observed with increasing output noise and decreasing input noise. Together, these results offer a unified perspective on the relationship between retinal mosaics, efficient coding, and channel capacity that can help to explain the stunning functional diversity of retinal cell types.

Scene statistics and noise determine the relative arrangement of receptive field mosaics.

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

Inter-mosaic coordination of retinal receptive fields.

The output of the retina is organized into many detector grids, called 'mosaics', that signal different features of visual scenes to the brain1-4. Each mosaic comprises a single type of retinal ganglion cell (RGC), whose receptive fields tile visual space. Many mosaics arise as pairs, signalling increments (ON) and decrements (OFF), respectively, of a particular visual feature5. Here we use a model of efficient coding6 to determine how such mosaic pairs should be arranged to optimize the encoding of natural scenes. We find that information is maximized when these mosaic pairs are anti-aligned, meaning that the distances between the receptive field centres across mosaics are greater than expected by chance. We tested this prediction across multiple receptive field mosaics acquired using large-scale measurements of the light responses of rat and primate RGCs. ON and OFF RGC pairs with similar feature selectivity had anti-aligned receptive field mosaics, consistent with this prediction. ON and OFF RGC types that encode distinct features have independent mosaics. These results extend efficient coding theory beyond individual cells to predict how populations of diverse types of RGC are spatially arranged.

Bubblewrap: Online tiling and real-time flow prediction on neural manifolds.

While most classic studies of function in experimental neuroscience have focused on the coding properties of individual neurons, recent developments in recording technologies have resulted in an increasing emphasis on the dynamics of neural populations. This has given rise to a wide variety of models for analyzing population activity in relation to experimental variables, but direct testing of many neural population hypotheses requires intervening in the system based on current neural state, necessitating models capable of inferring neural state online. Existing approaches, primarily based on dynamical systems, require strong parametric assumptions that are easily violated in the noise-dominated regime and do not scale well to the thousands of data channels in modern experiments. To address this problem, we propose a method that combines fast, stable dimensionality reduction with a soft tiling of the resulting neural manifold, allowing dynamics to be approximated as a probability flow between tiles. This method can be fit efficiently using online expectation maximization, scales to tens of thousands of tiles, and outperforms existing methods when dynamics are noise-dominated or feature multi-modal transition probabilities. The resulting model can be trained at kiloHertz data rates, produces accurate approximations of neural dynamics within minutes, and generates predictions on submillisecond time scales. It retains predictive performance throughout many time steps into the future and is fast enough to serve as a component of closed-loop causal experiments.

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.

Activation of Distinct Channelrhodopsin Variants Engages Different Patterns of Network Activity.

Several recently developed Channelrhodopsin (ChR) variants are characterized by rapid kinetics and reduced desensitization in comparison to the widely used ChR2. However, little is known about how varying opsin properties may regulate their interaction with local network dynamics. We compared evoked cortical activity in mice expressing three ChR variants with distinct temporal profiles under the CamKII promoter: Chronos, Chrimson, and ChR2. We assessed overall neural activation by measuring the amplitude and temporal progression of evoked spiking. Using γ-range (30-80 Hz) local field potential (LFP) power as an assay for local network engagement, we examined the recruitment of cortical network activity by each tool. All variants caused light-evoked increases in firing in vivo, but each demonstrated different temporal patterning of evoked activity. In addition, the three ChRs had distinct effects on cortical γ-band activity. Our findings suggest the properties of optogenetic tools can substantially affect their efficacy in vivo, as well their engagement of circuit resonance.

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.

Analysis of regional and temporal characteristics of PM10 during an Asian dust episode in Korea.

This study analyzes the regional and temporal distributions of PM10 concentrations observed in major metropolitan cities in Korea before, during and after a recent Asian dust episode in 2002. There were spatial and temporal variations in PM10 concentrations among the mid-western, the southwestern, the southeastern, and the southern parts of Korea during this Asian dust period due to the different air mass movement time and the different wind directions and speeds of prevailing winds in each city or region. The origins of the three-day Asian dust episode were identified by an analysis of two-day backward isentropic air trajectories. The different origins for each day also significantly contributed to the spatial and temporal variations in PM10 concentrations. A significant relationship was found between PM10 concentrations on the day preceding the first peak day and the first peak day of the Asian dust period but only in the mid-western areas. The concentrations of PM10 just after the Asian dust episode were much higher than those just before. There was a significant increase in a coarse fraction, having soil origins, of particles during the Asian dust episode. Concentrations of Mn, Fe, Ni and Cr extracted from the total suspended particulate (TSP) samples collected in 7 cities during the Asian dust episode were much higher when compared with other days in 2001. However, the Asian dust did not consistently increase the concentrations of lead, cadmium and copper as they are influenced by local sources such as local traffic or industrial emissions.