Recording spikes from a large fraction of the ganglion cells in a retinal patch

Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model

Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron's own spike history and the cross-coupling parameters of other neurons' spike history. We use simulation data to test the estimation algorithm's effectiveness and analyze the main factors that evoke neuronal responding activity. Finally, we use the coupling generalized linear model to encode neuronal spiking activity evoked by two acupuncture manipulations, and estimate the coupling parameters of stimulus, the self-coupling parameters and the cross-coupling parameters. The results show that in acupuncture experiments, acupuncture stimulus is the inducing factor of neuronal spiking activity, and the cross-coupling of other neurons' spike history is the main factor of neuronal spiking activity. Additionally, the higher the amplitude of the neuronal spiking waveform, the greater the cross-coupling parameter. This lays a theoretical foundation for the scientific application of acupuncture therapy.

Localization of neurons from extracellular footprints

BACKGROUND

High density microelectrode arrays (HD-MEAs) are now widely used for both in-vitro and in-vivo recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices.

NEW METHOD

The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions.

RESULTS

The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than 10μm, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost.

COMPARISON WITH EXISTING METHOD(S)

The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before.

CONCLUSION

The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.

A new role for excitation in the retinal direction-selective circuit

A key feature of the receptive field of neurons in the visual system is their centre-surround antagonism, whereby the centre and the surround exhibit responses of opposite polarity. This organization is thought to enhance visual acuity, but whether and how such antagonism plays a role in more complex processing remains poorly understood. Here, we investigate the role of centre and surround receptive fields in retinal direction selectivity by exposing posterior-preferring On-Off direction-selective ganglion cells (pDSGCs) to adaptive light and recording their response to globally moving objects. We reveal that light adaptation leads to surround expansion in pDSGCs. The pDSGCs maintain their original directional tuning in the centre receptive field, but present the oppositely tuned response in their surround. Notably, although inhibition is the main substrate for retinal direction selectivity, we found that following light adaptation, both the centre- and surround-mediated responses originate from directionally tuned excitatory inputs. Multi-electrode array recordings show similar oppositely tuned responses in other DSGC subtypes. Together, these data attribute a new role for excitation in the direction-selective circuit. This excitation carries an antagonistic centre-surround property, possibly designed to sharpen the detection of motion direction in the retina. KEY POINTS: Receptive fields of direction-selective retinal ganglion cells expand asymmetrically following light adaptation. The increase in the surround receptive field generates a delayed spiking phase that is tuned to the null direction and is mediated by excitation. Following light adaptation, excitation rules the computation in the centre receptive field and is tuned to the preferred direction. GABAergic and glycinergic inputs modulate the null-tuned delayed response differentially. Null-tuned delayed spiking phases can be detected in all types of direction-selective retinal ganglion cells. Light adaptation exposes a hidden directional excitation in the circuit, which is tuned to opposite directions in the centre and surround receptive fields.

Top-down modulation of the retinal code via histaminergic neurons of the hypothalamus

The mammalian retina is considered an autonomous circuit, yet work dating back to Ramon y Cajal indicates that it receives inputs from the brain. How such inputs affect retinal processing has remained unknown. We confirmed brain-to-retina projections of histaminergic neurons from the mouse hypothalamus. Histamine application ex vivo altered the activity of various retinal ganglion cells (RGCs), including direction-selective RGCs that gained responses to high motion velocities. These results were reproduced in vivo with optic tract recordings where histaminergic retinopetal axons were activated chemogenetically. Such changes could improve vision of fast-moving objects (e.g., while running), which fits with the known increased activity of histaminergic neurons during arousal. An antihistamine drug reduced optomotor responses to high-speed moving stimuli in freely moving mice. In humans, the same antihistamine nonuniformly modulated visual sensitivity across the visual field, indicating an evolutionary conserved function of the histaminergic system. Our findings expose a previously unappreciated role for brain-to-retina projections in modulating retinal function.

Improvements for recording retinal function with Microelectrode Arrays

A microelectrode array (MEA) is a configuration of multiple electrodes that enables the concurrent targeting of multiple sites for extracellular recording and stimulation. By utilizing light pulses or electrical stimulations, MEA recordings unveil the complex patterns of electrical activities that arise from the signaling processes within the retinal network. Here, we present a stepwise approach for using microelectrode arrays (MEAs) for recording action potentials from the mouse retina in response to electrical and light stimuli. We provide detailed techniques accompanied by description of a custom optical system, example recordings, troubleshooting guidelines, and data processing methods including spike sorting and code resources for analyzing light and electrical responses. The comprehensive nature of this paper aims to guide researchers in utilizing MEAs effectively for investigating retinal functionality. In particular, it can be easy to have a MEA experiment fail, but hard to identify the source of the failure. This paper is meant to demystify that process. It includes:•A description of MEA setup, recording, and spike data validation.•A troubleshooting guide for common failure modes in MEA recordings from mouse retina.•Spike detection and sorting to precisely extract distinctive action potential.

Spike sorting with Kilosort4

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

Highly Customizable 3D Microelectrode Arrays for In Vitro and In Vivo Neuronal Tissue Recordings

Planar microelectrode arrays (MEAs) for - in vitro or in vivo - neuronal signal recordings lack the spatial resolution and sufficient signal-to-noise ratio (SNR) required for a detailed understanding of neural network function and synaptic plasticity. To overcome these limitations, a highly customizable three-dimensional (3D) printing process is used in combination with thin film technology and a self-aligned template-assisted electrochemical deposition process to fabricate 3D-printed-based MEAs on stiff or flexible substrates. Devices with design flexibility and physical robustness are shown for recording neural activity in different in vitro and in vivo applications, achieving high-aspect ratio 3D microelectrodes of up to 33:1. Here, MEAs successfully record neural activity in 3D neuronal cultures, retinal explants, and the cortex of living mice, thereby demonstrating the versatility of the 3D MEA while maintaining high-quality neural recordings. Customizable 3D MEAs provide unique opportunities to study neural activity under regular or various pathological conditions, both in vitro and in vivo, and contribute to the development of drug screening and neuromodulation systems that can accurately monitor the activity of large neural networks over time.

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R2=0.951for human 1 andR2=0.944for human 2).Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.

Discovering optimal features for neuron-type identification from extracellular recordings

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

Differences in nonlinearities determine retinal cell types

Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record from a large number of cells to classify ganglion cells into different types based on functional information. Although the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to stimuli. These two approaches have not been compared directly. Here, we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into functional groups, one based on the receptive field properties, and the other one using directly their responses to stimuli with various temporal frequencies. We show that the response-based approach allows separation of more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the response-based method takes into account not only the linear part of ganglion cell function but also some of the nonlinearities. A careful characterization of nonlinear processing is thus key to allowing functional classification of sensory neurons.NEW & NOTEWORTHY In the retina, ganglion cells can be classified based on their response to visual stimuli. Although some methods are based on the modeling of receptive fields, others rely on responses to characteristic stimuli. We compared these two classes of methods and show that the latter provides a higher discrimination performance. We also show that this gain arises from the ability to account for the nonlinear behavior of neurons.

Functional subtypes of rodent melanopsin ganglion cells switch roles between night and day illumination

Intrinsically photosensitive retinal ganglion cells (ipRGCs), contain the photopigment melanopsin, and influence both image and non-image forming behaviors. Despite being categorized into multiple types (M1-M6), physiological variability within these types suggests our current understanding of ipRGCs is incomplete. We used multi-electrode array (MEA) recordings and unbiased cluster analysis under synaptic blockade to identify 8 functional clusters of ipRGCs, each with distinct photosensitivity and response timing. We used Cre mice to drive the expression of channelrhodopsin in SON-ipRGCs, enabling the localization of distinct ipRGCs in the dorsal retina. Additionally, we conducted a retrospective unbiased cluster analysis of ipRGC photoresponses to light stimuli across scotopic, mesopic, and photopic intensities, aimed at activating both rod and cone inputs to ipRGCs. Our results revealed shared and distinct synaptic inputs to the identified functional clusters, demonstrating that ipRGCs encode visual information with high fidelity at low light intensities, but poorly at photopic light intensities, when melanopsin activation is highest. Collectively, our findings support a framework with at least 8 functional subtypes of ipRGCs, each encoding luminance with distinct spike outputs, highlighting the inherent functional diversity and complexity of ipRGCs and suggesting a reevaluation of their contributions to retinal function and visual perception under varying light conditions.

Toward the Next Generation of Neural Iontronic Interfaces

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.

Boundary vector cells in the goldfish central telencephalon encode spatial information

Navigation is one of the most fundamental cognitive skills for the survival of fish, the largest vertebrate class, and almost all other animal classes. Space encoding in single neurons is a critical component of the neural basis of navigation. To study this fundamental cognitive component in fish, we recorded the activity of neurons in the central area of the goldfish telencephalon while the fish were freely navigating in a quasi-2D water tank embedded in a 3D environment. We found spatially modulated neurons with firing patterns that gradually decreased with the distance of the fish from a boundary in each cell's preferred direction, resembling the boundary vector cells found in the mammalian subiculum. Many of these cells exhibited beta rhythm oscillations. This type of spatial representation in fish brains is unique among space-encoding cells in vertebrates and provides insights into spatial cognition in this lineage.

A Platform for Spatiotemporal "Matrix" Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity

Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated "network assay" for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.

A Scalable Readout IC Based on Wideband Noise Cancelling for Full-Rate Scanning of High-Density Microelectrode Arrays

This paper presents a highly scalable readout IC for high-density microelectrode arrays (MEAs). Although the recent development of large-scale high-density MEAs provides opportunities to achieve sub-cellular neural recording over a wide network area, it is challenging to implement the readout IC that can operate with such MEAs. The requirement of high-speed recording in large-scale arrays induces wideband-noise folding, which makes it challenging to achieve a good noise performance for high-fidelity neural recording. Moreover, for the wideband readout, the major noise contributor changes from the readout circuit to the cell-electrode interface. In this paper, we first show why the interface noise becomes the dominant noise source and elucidate its component that contributes the most: sealing resistance. Then, we propose a new readout circuit structure, which can effectively cancel the wideband interface noise. As a result, the signal-to-noise ratio of input neural spike signals is improved dramatically in all cell-attachment or sealing conditions. Particularly, it is shown that under weakly sealed conditions, the spikes can be detected only when the proposed wideband noise cancellation technique is applied.

Optogenetic fUSI for brain-wide mapping of neural activity mediating collicular-dependent behaviors

Neuronal cell types are arranged in brain-wide circuits that guide behavior. In mice, the superior colliculus innervates a set of targets that direct orienting and defensive actions. We combined functional ultrasound imaging (fUSI) with optogenetics to reveal the network of brain regions functionally activated by four collicular cell types. Stimulating each neuronal group triggered different behaviors and activated distinct sets of brain nuclei. This included regions not previously thought to mediate defensive behaviors, for example, the posterior paralaminar nuclei of the thalamus (PPnT), which we show to play a role in suppressing habituation. Neuronal recordings with Neuropixels probes show that (1) patterns of spiking activity and fUSI signals correlate well in space and (2) neurons in downstream nuclei preferentially respond to innately threatening visual stimuli. This work provides insight into the functional organization of the networks governing innate behaviors and demonstrates an experimental approach to explore the whole-brain neuronal activity downstream of targeted cell types.

Latent Dynamical Variables Produce Signatures of Spatiotemporal Criticality in Large Biological Systems

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse graining had been previously applied to experimental neural recordings, which showed over two decades of apparent scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model such data by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally observed scalings, suggesting that they do not require fine-tuning of internal parameters, but will arise in any system, biological or not, where activity variables are coupled to latent dynamic stimuli. Parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to nonplace stimuli.

Visual properties of human retinal ganglion cells

The retinal output is the sole source of visual information for the brain. Studies in non-primate mammals estimate that this information is carried by several dozens of retinal ganglion cell types, each informing the brain about different aspects of a visual scene. Even though morphological studies of primate retina suggest a similar diversity of ganglion cell types, research has focused on the function of only a few cell types. In human retina, recordings from individual cells are anecdotal or focus on a small subset of identified types. Here, we present the first systematic ex-vivo recording of light responses from 342 ganglion cells in human retinas obtained from donors. We find a great variety in the human retinal output in terms of preferences for positive or negative contrast, spatio-temporal frequency encoding, contrast sensitivity, and speed tuning. Some human ganglion cells showed similar response behavior as known cell types in other primate retinas, while we also recorded light responses that have not been described previously. This first extensive description of the human retinal output should facilitate interpretation of primate data and comparison to other mammalian species, and it lays the basis for the use of ex-vivo human retina for in-vitro analysis of novel treatment approaches.

Spike-sorting analysis of neural electrical signals evoked by acupuncture based on model

Acupuncturing the Zusanli (ST 36) point with different types of manual acupuncture manipulations (MAs) and different frequencies can evoke a lot of neural response activities in spinal dorsal root neurons. The action potential is the basic unit of communication in the neural response process. With the rapid development of the electrode acquisition technology, we can simultaneously obtain neural electrical signals of multiple neurons in the target area. So it is crucial to extract spike trains of each neuron from raw recorded data. To solve the problem of variability of the spike waveform, this paper adopts a optimization algorithm based on model to improve the wave-cluster algorithm, which can provide higher accuracy and reliability. Further, through this optimization algorithm, we make a statistical analysis on spike events evoked by different MAs. Results suggest that numbers of response spikes under reinforcing manipulations are far more than reducing manipulations, which mainly embody in synchronous spike activities.

Spiking Correlation Analysis of Synchronous Spikes Evoked by Acupuncture Mechanical Stimulus

Acupuncturing the ST36 acupoint can evoke the response of the sensory nervous system, which is translated into output electrical signals in the spinal dorsal root. Neural response activities, especially synchronous spike events, evoked by different acupuncture manipulations have remarkable differences. In order to identify these network collaborative activities, we analyze the underlying spike correlation in the synchronous spike event. In this paper, we adopt a log-linear model to describe network response activities evoked by different acupuncture manipulations. Then the state-space model and Bayesian theory are used to estimate network spike correlations. Two sets of simulation data are used to test the effectiveness of the estimation algorithm and the model goodness-of-fit. In addition, simulation data are also used to analyze the relationship between spike correlations and synchronous spike events. Finally, we use this method to identify network spike correlations evoked by four different acupuncture manipulations. Results show that reinforcing manipulations (twirling reinforcing and lifting-thrusting reinforcing) can evoke the third-order spike correlation but reducing manipulations (twirling reducing and lifting-thrusting reducing) does not. This is the main reason why synchronous spikes evoked by reinforcing manipulations are more abundant than reducing manipulations.

Representation of edges, head direction, and swimming kinematics in the brain of freely-navigating fish

Like most animals, the survival of fish depends on navigation in space. This capacity has been documented in behavioral studies that have revealed navigation strategies. However, little is known about how freely swimming fish represent space and locomotion in the brain to enable successful navigation. Using a wireless neural recording system, we measured the activity of single neurons in the goldfish lateral pallium, a brain region known to be involved in spatial memory and navigation, while the fish swam freely in a two-dimensional water tank. We found that cells in the lateral pallium of the goldfish encode the edges of the environment, the fish head direction, the fish swimming speed, and the fish swimming velocity-vector. This study sheds light on how information related to navigation is represented in the brain of fish and addresses the fundamental question of the neural basis of navigation in this group of vertebrates.

Seeing Natural Images through the Eye of a Fly with Remote Focusing Two-Photon Microscopy

Visual systems of many animals, including the fruit fly Drosophila, represent the surrounding space as 2D maps, formed by populations of neurons. Advanced genetic tools make the fly visual system especially well accessible. However, in typical in vivo preparations for two-photon calcium imaging, relatively few neurons can be recorded at the same time. Here, we present an extension to a conventional two-photon microscope, based on remote focusing, which enables real-time rotation of the imaging plane, and thus flexible alignment to cellular structures, without resolution or speed trade-off. We simultaneously record from over 100 neighboring cells spanning the 2D retinotopic map. We characterize its representation of moving natural images, which we find is comparable to noise predictions. Our method increases throughput 10-fold and allows us to visualize a significant fraction of the fly's visual field. Furthermore, our system can be applied in general for a more flexible investigation of neural circuits.

Searching for collective behavior in a small brain

In large neuronal networks, it is believed that functions emerge through the collective behavior of many interconnected neurons. Recently, the development of experimental techniques that allow simultaneous recording of calcium concentration from a large fraction of all neurons in Caenorhabditis elegans-a nematode with 302 neurons-creates the opportunity to ask whether such emergence is universal, reaching down to even the smallest brains. Here, we measure the activity of 50+ neurons in C. elegans, and analyze the data by building the maximum entropy model that matches the mean activity and pairwise correlations among these neurons. To capture the graded nature of the cells' responses, we assign each cell multiple states. These models, which are equivalent to a family of Potts glasses, successfully predict higher statistical structure in the network. In addition, these models exhibit signatures of collective behavior: the state of single cells can be predicted from the state of the rest of the network; the network, despite being sparse in a way similar to the structural connectome, distributes its response globally when locally perturbed; the distribution over network states has multiple local maxima, as in models of memory; and the parameters that describe the real network are close to a critical surface in this family of models.

A projection specific logic to sampling visual inputs in mouse superior colliculus

Using sensory information to trigger different behaviors relies on circuits that pass through brain regions. The rules by which parallel inputs are routed to downstream targets are poorly understood. The superior colliculus mediates a set of innate behaviors, receiving input from >30 retinal ganglion cell types and projecting to behaviorally important targets including the pulvinar and parabigeminal nucleus. Combining transsynaptic circuit tracing with in vivo and ex vivo electrophysiological recordings, we observed a projection-specific logic where each collicular output pathway sampled a distinct set of retinal inputs. Neurons projecting to the pulvinar or the parabigeminal nucleus showed strongly biased sampling from four cell types each, while six others innervated both pathways. The visual response properties of retinal ganglion cells correlated well with those of their disynaptic targets. These findings open the possibility that projection-specific sampling of retinal inputs forms a basis for the selective triggering of behaviors by the superior colliculus.

Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons

We develop a phenomenological coarse-graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse-grained variables seem to approach a fixed non-Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a nontrivial fixed point.

The Efemp1R345W Macular Dystrophy Mutation Causes Amplified Circadian and Photophobic Responses to Light in Mice

Purpose

The R345W mutation in EFEMP1 causes malattia leventinese, an autosomal dominant eye disease with pathogenesis similar to an early-onset age-related macular degeneration. In mice, Efemp1^R345W does not cause detectable degeneration but small subretinal deposits do accumulate. The purpose of this study was to determine whether there were abnormal responses to light at this presymptomatic stage in Efemp1^R345W mice.

Methods

Responses to light were assessed by visual water task, circadian phase shifting, and negative masking behavior. The mechanism of abnormal responses was investigated by anterior eye exam, electroretinogram, melanopsin cell quantification, and multielectrode recording of retinal ganglion cell activity.

Results

Visual acuity was not different in Efemp1^R345W mice. However, amplitudes of circadian phase shifting (P = 0.016) and negative masking (P < 0.0001) were increased in Efemp1^R345W mice. This phenotype was not explained by anterior eye defects or amplified outer retina responses. Instead, we identified increased melanopsin-generated responses to light in the ganglion cell layer of the retina (P < 0.01).

Conclusions

Efemp1^R345W increases the sensitivity to light of behavioral responses driven by detection of irradiance. An amplified response to light in melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) is consistent with this phenotype. The major concern with this effect of the malattia leventinese mutation is the potential for abnormal regulation of physiology by light to negatively affect health.

Large-Scale, High-Resolution Microelectrode Arrays for Interrogation of Neurons and Networks

High-density microelectrode arrays (HD-MEAs) are increasingly being used for the observation and manipulation of neurons and networks in vitro. Large-scale electrode arrays allow for long-term extracellular recording of the electrical activity from thousands of neurons simultaneously. Beyond population activity, it has also become possible to extract information of single neurons at subcellular level (e.g., the propagation of action potentials along axons). In effect, HD-MEAs have become an electrical imaging platform for label-free extraction of the structure and activation of cells in cultures and tissues. The quality of HD-MEA data depends on the resolution of the electrode array and the signal-to-noise ratio. In this chapter, we begin with an introduction to HD-MEA signals. We provide an overview of the developments on complementary metal-oxide-semiconductor or CMOS-based HD-MEA technology. We also discuss the factors affecting the performance of HD-MEAs and the trending application requirements that drive the efforts for future devices. We conclude with an outlook on the potential of HD-MEAs for advancing basic neuroscience and drug discovery.

Long-range neural inhibition and stimulus competition in the archerfish optic tectum

The archerfish, which is unique in its ability to hunt insects above the water level by shooting a jet of water at its prey, operates in a complex visual environment. The fish needs to quickly select one object from among many others. In animals other than the archerfish, long-range inhibition is considered to drive selection. As a result of long-range inhibition, a potential target outside a neuron's receptive field suppresses the activity elicited by another potential target within the receptive field. We tested whether a similar mechanism operates in the archerfish by recording the activity of neurons in the optic tectum while presenting a target stimulus inside the receptive field and a competing stimulus outside the receptive field. We held the features of the target constant while varying the size, speed, and distance of the competing stimulus. We found cells that exhibit long-range inhibition; i.e., inhibition that extends to a significant part of the entire visual field of the animal. The competing stimulus depressed the firing rate. In some neurons, this effect was dependent on the features of the competing stimulus. These findings suggest that long-range inhibition may play a crucial role in the target selection process in the archerfish.

N3-MEA Probes: Scooping Neuronal Networks

In the current work, we introduce a brand new line of versatile, flexible, and multifunctional MEA probes, the so-called Nano Neuro Net, or N³-MEAs. Material choice, dimensions, and room for further upgrade, were carefully considered when designing such probes in order to cover the widest application range possible. Proof of the operation principle of these novel probes is shown in the manuscript via the recording of extracellular signals, such as action potentials and local field potentials from cardiac cells and retinal ganglion cells of the heart tissue and eye respectively. Reasonably large signal to noise ratio (SNR) combined with effortless operation of the devices, mechanical and chemical stability, multifunctionality provide, in our opinion, an unprecedented blend. We show successful recordings of (1) action potentials from heart tissue with a SNR up to 13.2; (2) spontaneous activity of retinal ganglion cells with a SNR up to 12.8; and (3) local field potentials with an ERG-like waveform, as well as spiking responses of the retina to light stimulation. The results reveal not only the multi-functionality of these N³-MEAs, but high quality recordings of electrogenic tissues.

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it remains unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex, naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

The dark matter of the brain

The bulk of brain energy expenditure is allocated for maintenance of perpetual intrinsic activity of neurons and neural circuits. Long-term electrophysiological and neuroimaging studies in anesthetized and behaving animals show, however, that the great majority of nerve cells in the intact brain do not fire action potentials, i.e., are permanently silent. Herein, I review emerging data suggesting massive redundancy of nerve cells in mammalian nervous system, maintained in inhibited state at high energetic costs. Acquired in the course of evolution, these collections of dormant neurons and circuits evade routine functional undertakings, and hence, keep out of the reach of natural selection. Under penetrating stress and disease, however, they occasionally switch in active state and drive a variety of neuro-psychiatric symptoms and behavioral abnormalities. The increasing evidence for widespread occurrence of silent neurons warrants careful revision of functional models of the brain and entails unforeseen reserves for rehabilitation and plasticity.

Scaling Spike Detection and Sorting for Next-Generation Electrophysiology

Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, are essential steps in the analysis of extracellular electrical recordings from neurons. The volume and complexity of the data from recently developed large-scale, high-density microelectrode arrays and probes, which allow recording from thousands of channels simultaneously, substantially complicate this task conceptually and computationally. This chapter provides a summary and discussion of recently developed methods to tackle these challenges and discusses the important aspect of algorithm validation, and assessment of detection and sorting quality.

Pathway-Specific Asymmetries between ON and OFF Visual Signals

Visual processing is largely organized into ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways exhibit natural pairings based on morphological and physiological similarities, such as ON and OFF α-ganglion cells in the mammalian retina. Several studies have noted asymmetries in the properties of ON and OFF pathways. For example, the spatial receptive fields (RFs) of OFF α-cells are systematically smaller than ON α-cells. Analysis of natural scenes suggests that these asymmetries are optimal for visual encoding. To test the generality of ON/OFF asymmetries, we measured the spatiotemporal RF properties of multiple RGC types in rat retina. Through a quantitative and serial classification, we identified three functional pairs of ON and OFF RGCs. We analyzed the structure of their RFs and compared spatial integration, temporal integration, and gain across ON and OFF pairs. Similar to previous results from the cat and primate, RGC types with larger spatial RFs exhibited briefer temporal integration and higher gain. However, each pair of ON and OFF RGC types exhibited distinct asymmetric relationships between RF properties, some of which were opposite to the findings of previous reports. These results reveal the functional organization of six RGC types in the rodent retina and indicate that ON/OFF asymmetries are pathway specific.SIGNIFICANCE STATEMENT Circuits that process sensory input frequently process increments separately from decrements, so-called ON and OFF responses. Theoretical studies indicate that this separation, and associated asymmetries in ON and OFF pathways, may be beneficial for encoding natural stimuli. However, the generality of ON and OFF pathway asymmetries has not been tested. Here we compare the functional properties of three distinct pairs of ON and OFF pathways in the rodent retina and show that their asymmetries are pathway specific. These results provide a new view on the partitioning of vision across diverse ON and OFF signaling pathways.

Lycium Barbarum Polysaccharides Protect Retina in rd1 Mice During Photoreceptor Degeneration

Purpose

As an active component in wolfberry, lycium barbarum polysaccharides (LBP) are capable of protecting retinal neurons in several animal disease models. Here, we asked whether LBP rescues the retinal morphology and function in rd1 mouse, a photoreceptor fast-degenerating animal model of retinitis pigmentosa, and in particular focused on LBP's effects on the function of retinal ganglion cells (RGCs) during photoreceptor degeneration.

Methods

An equal volume of LBP or control vehicle was daily intraperitoneal (i.p.) injected in rd1 mice from postnatal day 4 (P4) to P14, P20, or P24 when photoreceptors completely degenerate. Immunostaining, electroretinogram (ERG), visual behavior tests and multielectrode array (MEA) recordings were assessed to determine the structure and function of the treated retina.

Results

LBP treatment greatly promoted photoreceptor survival, enhanced ERG responses, and improved visual behaviors in rd1 mice. MEA data showed that LBP treatment in general decreased the abnormally high spontaneous spiking that occurs in rd1 mice, and increased the percentage of light-responsive RGCs as well as their light-evoked response, light sensitivity, signal-to-noise ratio, and response speed. Interestingly, LBP treatment affected ON and OFF responses differently.

Conclusions

LBP improves retinal morphology and function in rd1 mice, and delays the functional decay of RGCs during photoreceptor degeneration. This is the first study that has examined in detail the effects of LBP on RGC responses. Our data suggest that LBP may help extend the effective time window before more invasive RP therapeutic approaches such as retinoprosthesis are applied.

Functional Assessment of Melanopsin-Driven Light Responses in the Mouse: Multielectrode Array Recordings

Intrinsically photosensitive retinal ganglion cells (ipRGCs) are a special subset of retinal output neurons capable of detecting and responding to light via a unique photopigment called melanopsin. Melanopsin activation is essential to a wide array of physiological functions, especially to those related to non-image-forming vision. Since ipRGCs only constitute a very small proportion of retinal ganglion cells, targeted recording of melanopsin-driven responses used to be a big challenge to vision researchers. Multielectrode array (MEA) recording provides a noninvasive, high throughput method to monitor melanopsin-driven responses. When synaptic inputs from rod/cone photoreceptors are silenced with glutamatergic blockers, extracellular electric signals derived from melanopsin activation can be recorded from multiple ipRGCs simultaneously by tens of microelectrodes aligned in an array. In this chapter we describe how our labs have approached MEA recording of melanopsin-driven light responses in adult mouse retinas. Instruments, tools and chemical reagents routinely used for setting up a successful MEA recording are listed, and a standard experimental procedure is provided. The implementation of this technique offers a useful paradigm that can be used to conduct functional assessments of ipRGCs and NIF vision.

A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain 'ground truth' data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.

Perspectives on theory at the interface of physics and biology

Theoretical physics is the search for simple and universal mathematical descriptions of the natural world. In contrast, much of modern biology is an exploration of the complexity and diversity of life. For many, this contrast is prima facie evidence that theory, in the sense that physicists use the word, is impossible in a biological context. For others, this contrast serves to highlight a grand challenge. I am an optimist, and believe (along with many colleagues) that the time is ripe for the emergence of a more unified theoretical physics of biological systems, building on successes in thinking about particular phenomena. In this essay I try to explain the reasons for my optimism, through a combination of historical and modern examples.

The structured 'low temperature' phase of the retinal population code

Recent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a 'structured', collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.

Model-based spike sorting with a mixture of drifting t-distributions

BACKGROUND

Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains.

NEW METHOD

We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings-cluster drift over time and heavy tails in the distribution of spikes-and offers improved robustness to outliers.

RESULTS

We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets in a few seconds, enabling interactive clustering of chronic recordings.

COMPARISON WITH EXISTING METHODS

We identify three common failure modes of spike sorting methods that assume stationarity and evaluate their impact given the empirically-observed cluster drift in chronic recordings. Using hybrid ground truth datasets, we also demonstrate that our model-based estimate of misclassification error is more accurate than previous unit isolation metrics.

CONCLUSIONS

The mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

Activation of ganglion cells and axon bundles using epiretinal electrical stimulation

Epiretinal prostheses for treating blindness activate axon bundles, causing large, arc-shaped visual percepts that limit the quality of artificial vision. Improving the function of epiretinal prostheses therefore requires understanding and avoiding axon bundle activation. This study introduces a method to detect axon bundle activation on the basis of its electrical signature and uses the method to test whether epiretinal stimulation can directly elicit spikes in individual retinal ganglion cells without activating nearby axon bundles. Combined electrical stimulation and recording from isolated primate retina were performed using a custom multielectrode system (512 electrodes, 10-μm diameter, 60-μm pitch). Axon bundle signals were identified by their bidirectional propagation, speed, and increasing amplitude as a function of stimulation current. The threshold for bundle activation varied across electrodes and retinas, and was in the same range as the threshold for activating retinal ganglion cells near their somas. In the peripheral retina, 45% of electrodes that activated individual ganglion cells (17% of all electrodes) did so without activating bundles. This permitted selective activation of 21% of recorded ganglion cells (7% of expected ganglion cells) over the array. In one recording in the central retina, 75% of electrodes that activated individual ganglion cells (16% of all electrodes) did so without activating bundles. The ability to selectively activate a subset of retinal ganglion cells without axon bundles suggests a possible novel architecture for future epiretinal prostheses.NEW & NOTEWORTHY Large-scale multielectrode recording and stimulation were used to test how selectively retinal ganglion cells can be electrically activated without activating axon bundles. A novel method was developed to identify axon activation on the basis of its unique electrical signature and was used to find that a subset of ganglion cells can be activated at single-cell, single-spike resolution without producing bundle activity in peripheral and central retina. These findings have implications for the development of advanced retinal prostheses.

Recent progress in multi-electrode spike sorting methods

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

The effects of temperature changes on retinal ganglion cell responses to electrical stimulation

Little is known about how the retina's response to electrical stimulation is modified by temperatures. In vitro experiments are often used to inform in vivo studies, hence it is important to understand what changes occur at physiological temperature. To investigate this, we recorded from eight RGCs in vitro at three temperatures; room temperature (24°C), 30°C and 34°C. Results show that response latencies and thresholds are reduced, bursting spike rates in response to stimulation increases, and the spiking becomes more consistently locked to the stimulus at higher temperatures.

Consensus-Based Sorting of Neuronal Spike Waveforms

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

Analysis of Neuronal Spike Trains, Deconstructed

As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

Unified selective sorting approach to analyse multi-electrode extracellular data

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Optical detection of neuron connectivity by random access two-photon microscopy

BACKGROUND

Knowledge about the distribution, strength, and direction of synaptic connections within neuronal networks are crucial for understanding brain function. Electrophysiology using multiple electrodes provides a very high temporal resolution, but does not yield sufficient spatial information for resolving neuronal connection topology. Optical recording techniques using single-cell resolution have provided promise for providing spatial information. Although calcium imaging from hundreds of neurons has provided a novel view of the neural connections within the network, the kinetics of calcium responses are not fast enough to resolve each action potential event with high fidelity. Therefore, it is not possible to detect the direction of neuronal connections.

NEW METHOD

We took advantage of the fast kinetics and large dynamic range of the DiO/DPA combination of voltage sensitive dye and the fast scan speed of a custom-made random-access two-photon microscope to resolve each action potential event from multiple neurons in culture.

RESULTS

Long-duration recording up to 100min from cultured hippocampal neurons yielded sufficient numbers of spike events for analyzing synaptic connections. Cross-correlation analysis of neuron pairs clearly distinguished synaptically connected neuron pairs with the connection direction.

COMPARISON WITH EXISTING METHOD

The long duration recording of action potentials with voltage-sensitive dye utilized in the present study is much longer than in previous studies. Simultaneous optical voltage and calcium measurements revealed that voltage-sensitive dye is able to detect firing events more reliably than calcium indicators.

CONCLUSIONS

This novel method reveals a new view of the functional structure of neuronal networks.

Connexin 36 and rod bipolar cell independent rod pathways drive retinal ganglion cells and optokinetic reflexes

Rod pathways are a parallel set of synaptic connections which enable night vision by relaying and processing rod photoreceptor light responses. We use dim light stimuli to isolate rod pathway contributions to downstream light responses then characterize these contributions in knockout mice lacking rod transducin-α (Trα), or certain pathway components associated with subsets of rod pathways. These comparisons reveal that rod pathway driven light sensitivity in retinal ganglion cells (RGCs) is entirely dependent on Trα, but partially independent of connexin 36 (Cx36) and rod bipolar cells. Pharmacological experiments show that rod pathway-driven and Cx36-independent RGC ON responses are also metabotropic glutamate receptor 6-dependent. To validate the RGC findings in awake, behaving animals we measured optokinetic reflexes (OKRs), which are sensitive to changes in ON pathways. Scotopic OKR contrast sensitivity was lost in Trα(-/-) mice, but indistinguishable from controls in Cx36(-/-) and rod bipolar cell knockout mice. Mesopic OKRs were also altered in mutant mice: Trα(-/-) mice had decreased spatial acuity, rod BC knockouts had decreased sensitivity, and Cx36(-/-) mice had increased sensitivity. These results provide compelling evidence against the complete Cx36 or rod BC dependence of night vision's ON component. Further, the findings suggest the parallel nature of rod pathways provides considerable redundancy to scotopic light sensitivity but distinct contributions to mesopic responses through complicated interactions with cone pathways.