Early-stage waves in the retinal network emerge close to a critical state transition between local and global functional connectivity

How Does the Inner Retinal Network Shape the Ganglion Cells Receptive Field? A Computational Study

We consider a model of basic inner retinal connectivity where bipolar and amacrine cells interconnect and both cell types project onto ganglion cells, modulating their response output to the brain visual areas. We derive an analytical formula for the spatiotemporal response of retinal ganglion cells to stimuli, taking into account the effects of amacrine cells inhibition. This analysis reveals two important functional parameters of the network: (1) the intensity of the interactions between bipolar and amacrine cells and (2) the characteristic timescale of these responses. Both parameters have a profound combined impact on the spatiotemporal features of retinal ganglion cells' responses to light. The validity of the model is confirmed by faithfully reproducing pharmacogenetic experimental results obtained by stimulating excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed on ganglion cells and amacrine cells' subclasses, thereby modifying the inner retinal network activity to visual stimuli in a complex, entangled manner. Our mathematical model allows us to explore and decipher these complex effects in a manner that would not be feasible experimentally and provides novel insights in retinal dynamics.

A Self-Similarity Logic May Shape the Organization of the Nervous System

From the morphological point of view, the nervous system exhibits a fractal, self-similar geometry at various levels of observations, from single cells up to cell networks. From the functional point of view, it is characterized by a hierarchical organization in which self-similar structures (networks) of different miniaturizations are nested within each other. In particular, neuronal networks, interconnected to form neuronal systems, are formed by neurons, which operate thanks to their molecular networks, mainly having proteins as components that via protein-protein interactions can be assembled in multimeric complexes working as micro-devices. On this basis, the term "self-similarity logic" was introduced to describe a nested organization where, at the various levels, almost the same rules (logic) to perform operations are used. Self-similarity and self-similarity logic both appear to be intimately linked to the biophysical evidence for the nervous system being a pattern-forming system that can flexibly switch from one coherent state to another. Thus, they can represent the key concepts to describe its complexity and its concerted, holistic behavior.

Modeling cholinergic retinal waves: starburst amacrine cells shape wave generation, propagation, and direction bias

Stage II cholinergic retinal waves are one of the first instances of neural activity in the visual system as they are present at a developmental timepoint in which light-evoked activity remains largely undetectable. These waves of spontaneous neural activity sweeping across the developing retina are generated by starburst amacrine cells, depolarize retinal ganglion cells, and drive the refinement of retinofugal projections to numerous visual centers in the brain. Building from several well-established models, we assemble a spatial computational model of starburst amacrine cell-mediated wave generation and wave propagation that includes three significant advancements. First, we model the intrinsic spontaneous bursting of the starburst amacrine cells, including the slow afterhyperpolarization, which shapes the stochastic process of wave generation. Second, we establish a mechanism of wave propagation using reciprocal acetylcholine release, synchronizing the bursting activity of neighboring starburst amacrine cells. Third, we model the additional starburst amacrine cell release of GABA, changing the spatial propagation of retinal waves and in certain instances, the directional bias of the retinal wave front. In total, these advancements comprise a now more comprehensive model of wave generation, propagation, and direction bias.

Spatial organization and transitions of spontaneous neuronal activities in the developing sensory cortex

The sensory cortex underlies our ability to perceive and interact with the external world. Sensory perceptions are controlled by specialized neuronal circuits established through fine-tuning, which relies largely on neuronal activity during the development. Spontaneous neuronal activity is an essential driving force of neuronal circuit refinement. At early developmental stages, sensory cortices display spontaneous activities originating from the periphery and characterized by correlated firing arranged spatially according to the modality. The firing patterns are reorganized over time and become sparse, which is typical for the mature brain. This review focuses mainly on rodent sensory cortices. First, the features of the spontaneous activities during early postnatal stages are described. Then, the developmental changes in the spatial organization of the spontaneous activities and the transition mechanisms involved are discussed. The identification of the principles controlling the spatial organization of spontaneous activities in the developing sensory cortex is essential to understand the self-organization process of neuronal circuits.

Dystrophin Is Required for the Proper Timing in Retinal Histogenesis: A Thorough Investigation on the mdx Mouse Model of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a lethal X-linked muscular disease caused by defective expression of the cytoskeletal protein dystrophin (Dp427). Selected autonomic and central neurons, including retinal neurons, express Dp427 and/or dystrophin shorter isoforms. Because of this, DMD patients may also experience different forms of cognitive impairment, neurological and autonomic disorders, and specific visual defects. DMD-related damages to the nervous system are established during development, suggesting a role for all dystrophin isoforms in neural circuit development and differentiation; however, to date, their function in retinogenesis has never been investigated. In this large-scale study, we analyzed whether the lack of Dp427 affects late retinogenesis in the mdx mouse, the most well studied animal model of DMD. Retinal gene expression and layer maturation, as well as neural cell proliferation, apoptosis, and differentiation, were evaluated in E18 and/or P0, P5, P10, and adult mice. In mdx mice, expression of Capn3, Id3 (E18-P5), and Dtnb (P5) genes, encoding proteins involved in different aspects of retina development and synaptogenesis (e.g., Calpain 3, DNA-binding protein inhibitor-3, and β-dystrobrevin, respectively), was transiently reduced compared to age-matched wild type mice. Concomitantly, a difference in the time required for the retinal ganglion cell layer to reach appropriate thickness was observed (P0–P5). Immunolabeling for specific cell markers also evidenced a significant dysregulation in the number of GABAergic amacrine cells (P5–P10), a transient decrease in the area immunopositive for the Vesicular Glutamate Transporter 1 (VGluT1) during ribbon synapse maturation (P10) and a reduction in the number of calretinin⁺ retinal ganglion cells (RGCs) (adults). Finally, the number of proliferating retinal progenitor cells (P5–P10) and apoptotic cells (P10) was reduced. These results support the hypothesis of a role for Dp427 during late retinogenesis different from those proposed in consolidated neural circuits. In particular, Dp427 may be involved in shaping specific steps of retina differentiation. Notably, although most of the above described quantitative alterations recover over time, the number of calretinin⁺ RGCs is reduced only in the mature retina. This suggests that alterations subtler than the timing of retinal maturation may occur, a hypothesis that demands further in-depth functional studies.

Retinal Drug Delivery: Rethinking Outcomes for the Efficient Replication of Retinal Behavior

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.

Proper spatial heterogeneities expand the regime of scale-free behavior in a lattice of excitable elements

Signatures of criticality, such as power law scaling of observables, have been empirically found in a plethora of real-life settings, including biological systems. The presence of critical states is believed to have many functional advantages and is associated with optimal operational abilities. Typically, critical dynamics arises in the proximity of phase transition points between absorbing disordered states (subcriticality) and ordered active regimes (supercriticality) and requires a high degree of fine tuning to emerge, which is unlikely to occur in real biological systems. In the present study we propose a rather simple, and biologically relevant mechanism that profoundly expands the critical-like region. In particular, by means of numerical simulation we show that incorporating spatial heterogeneities into the square lattice of map-based excitable oscillators broadens the parameter space in which the distribution of excitation wave sizes follows closely a power law. Most importantly, this behavior is only observed if the spatial profile exhibits intermediate-sized patches with similar excitability levels, whereas for large and small spatial clusters only marginal widening of the critical state is detected. Furthermore, it turned out that the presence of spatial disorder in general amplifies the size of excitation waves, whereby the relatively highest contributions are observed in the proximity of the critical point. We argue that the reported mechanism is of particular importance for excitable systems with local interactions between individual elements.

Neural field models for latent state inference: Application to large-scale neuronal recordings

Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatiotemporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.

Developing statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent state-space equations and demonstrate that this enables inference of the intrinsic states of neurons from recorded spike trains in large populations.

A biophysical model explains the spontaneous bursting behavior in the developing retina

During early development, waves of activity propagate across the retina and play a key role in the proper wiring of the early visual system. During a particular phase of the retina development (stage II) these waves are triggered by a transient network of neurons, called Starburst Amacrine Cells (SACs), showing a bursting activity which disappears upon further maturation. The underlying mechanisms of the spontaneous bursting and the transient excitability of immature SACs are not completely clear yet. While several models have attempted to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and reveal how these cells change their intrinsic properties during development. Here, we introduce a mathematical model, grounded on biophysics, which enables us to reproduce the bursting activity of SACs and to propose a plausible, generic and robust, mechanism that generates it. The core parameters controlling repetitive firing are fast depolarizing V-gated calcium channels and hyperpolarizing V-gated potassium channels. The quiescent phase of bursting is controlled by a slow after hyperpolarization (sAHP), mediated by calcium-dependent potassium channels. Based on a bifurcation analysis we show how biophysical parameters, regulating calcium and potassium activity, control the spontaneously occurring fast oscillatory activity followed by long refractory periods in individual SACs. We make a testable experimental prediction on the role of voltage-dependent potassium channels on the excitability properties of SACs and on the evolution of this excitability along development. We also propose an explanation on how SACs can exhibit a large variability in their bursting periods, as observed experimentally within a SACs network as well as across different species, yet based on a simple, unique, mechanism. As we discuss, these observations at the cellular level have a deep impact on the retinal waves description.

Theoretical Models of Neural Development

Constructing a functioning nervous system requires the precise orchestration of a vast array of mechanical, molecular, and neural-activity-dependent cues. Theoretical models can play a vital role in helping to frame quantitative issues, reveal mathematical commonalities between apparently diverse systems, identify what is and what is not possible in principle, and test the abilities of specific mechanisms to explain the data. This review focuses on the progress that has been made over the last decade in our theoretical understanding of neural development.

Traits of criticality in membrane potential fluctuations of pyramidal neurons in the CA1 region of rat hippocampus

Evidence that neural circuits are operating near criticality has been provided at various levels of brain organisation with a presumed role in maximising information processing and multiscale activity association. Criticality has been linked to excitation at both the single-cell and network levels, as action potential generation marks an obvious phase transition from a resting to an excitable state. Using in vitro intracellular recordings, we examine irregular, small amplitude membrane potential fluctuations from CA1 pyramidal neurons of Wistar male rats. We show that these fluctuations, confounded with noise, carry information relevant to the neuronal state. The underlying dynamics exhibit intermittent characteristics shown to describe the temporal fluctuations of the order parameter of a macroscopic system at its critical point even in the absence of firing. An externally applied stimulus serves as the control parameter, driving the system in and out of its critical state. Based on our experimental observations we calculate the equivalent of the isothermal critical exponent δ_h finding a value which depends on the applied stimulus. For each neuron there is a stimulus amplitude for which the critical behaviour becomes most pronounced. The corresponding mean value of δ_h in the considered ensemble of neurons is δ_h  ≈ 1.89, close to theoretical predictions for critical networks. Finally, we show that the firing rate of a neuron decreases exponentially with δ_h .

Growing Critical: Self-Organized Criticality in a Developing Neural System

Experiments in various neural systems found avalanches: bursts of activity with characteristics typical for critical dynamics. A possible explanation for their occurrence is an underlying network that self-organizes into a critical state. We propose a simple spiking model for developing neural networks, showing how these may "grow into" criticality. Avalanches generated by our model correspond to clusters of widely applied Hawkes processes. We analytically derive the cluster size and duration distributions and find that they agree with those of experimentally observed neuronal avalanches.

Optimizing information processing in neuronal networks beyond critical states

Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.

Retinal Wave Patterns Are Governed by Mutual Excitation among Starburst Amacrine Cells and Drive the Refinement and Maintenance of Visual Circuits

Retinal waves are correlated bursts of spontaneous activity whose spatiotemporal patterns are critical for early activity-dependent circuit elaboration and refinement in the mammalian visual system. Three separate developmental wave epochs or stages have been described, but the mechanism(s) of pattern generation of each and their distinct roles in visual circuit development remain incompletely understood. We used neuroanatomical,in vitroandin vivoelectrophysiological, and optical imaging techniques in genetically manipulated mice to examine the mechanisms of wave initiation and propagation and the role of wave patterns in visual circuit development. Through deletion of β2 subunits of nicotinic acetylcholine receptors (β2-nAChRs) selectively from starburst amacrine cells (SACs), we show that mutual excitation among SACs is critical for Stage II (cholinergic) retinal wave propagation, supporting models of wave initiation and pattern generation from within a single retinal cell type. We also demonstrate that β2-nAChRs in SACs, and normal wave patterns, are necessary for eye-specific segregation. Finally, we show that Stage III (glutamatergic) retinal waves are not themselves necessary for normal eye-specific segregation, but elimination of both Stage II and Stage III retinal waves dramatically disrupts eye-specific segregation. This suggests that persistent Stage II retinal waves can adequately compensate for Stage III retinal wave loss during the development and refinement of eye-specific segregation. These experiments confirm key features of the "recurrent network" model for retinal wave propagation and clarify the roles of Stage II and Stage III retinal wave patterns in visual circuit development.

SIGNIFICANCE STATEMENT

Spontaneous activity drives early mammalian circuit development, but the initiation and patterning of activity vary across development and among modalities. Cholinergic "retinal waves" are initiated in starburst amacrine cells and propagate to retinal ganglion cells and higher-order visual areas, but the mechanism responsible for creating their unique and critical activity pattern is incompletely understood. We demonstrate that cholinergic wave patterns are dictated by recurrent connectivity within starburst amacrine cells, and retinal ganglion cells act as "readouts" of patterned activity. We also show that eye-specific segregation occurs normally without glutamatergic waves, but elimination of both cholinergic and glutamatergic waves completely disrupts visual circuit development. These results suggest that each retinal wave pattern during development is optimized for concurrently refining multiple visual circuits.

Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

Developmental time windows for axon growth influence neuronal network topology

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

A reaction-diffusion model of cholinergic retinal waves

Prior to receiving visual stimuli, spontaneous, correlated activity in the retina, called retinal waves, drives activity-dependent developmental programs. Early-stage waves mediated by acetylcholine (ACh) manifest as slow, spreading bursts of action potentials. They are believed to be initiated by the spontaneous firing of Starburst Amacrine Cells (SACs), whose dense, recurrent connectivity then propagates this activity laterally. Their inter-wave interval and shifting wave boundaries are the result of the slow after-hyperpolarization of the SACs creating an evolving mosaic of recruitable and refractory cells, which can and cannot participate in waves, respectively. Recent evidence suggests that cholinergic waves may be modulated by the extracellular concentration of ACh. Here, we construct a simplified, biophysically consistent, reaction-diffusion model of cholinergic retinal waves capable of recapitulating wave dynamics observed in mice retina recordings. The dense, recurrent connectivity of SACs is modeled through local, excitatory coupling occurring via the volume release and diffusion of ACh. In addition to simulation, we are thus able to use non-linear wave theory to connect wave features to underlying physiological parameters, making the model useful in determining appropriate pharmacological manipulations to experimentally produce waves of a prescribed spatiotemporal character. The model is used to determine how ACh mediated connectivity may modulate wave activity, and how parameters such as the spontaneous activation rate and sAHP refractory period contribute to critical wave size variability.

Copper is an endogenous modulator of neural circuit spontaneous activity

For reasons that remain insufficiently understood, the brain requires among the highest levels of metals in the body for normal function. The traditional paradigm for this organ and others is that fluxes of alkali and alkaline earth metals are required for signaling, but transition metals are maintained in static, tightly bound reservoirs for metabolism and protection against oxidative stress. Here we show that copper is an endogenous modulator of spontaneous activity, a property of functional neural circuitry. Using Copper Fluor-3 (CF3), a new fluorescent Cu(+) sensor for one- and two-photon imaging, we show that neurons and neural tissue maintain basal stores of loosely bound copper that can be attenuated by chelation, which define a labile copper pool. Targeted disruption of these labile copper stores by acute chelation or genetic knockdown of the CTR1 (copper transporter 1) copper channel alters the spatiotemporal properties of spontaneous activity in developing hippocampal and retinal circuits. The data identify an essential role for copper neuronal function and suggest broader contributions of this transition metal to cell signaling.

Slow feature analysis on retinal waves leads to V1 complex cells

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.

A role for correlated spontaneous activity in the assembly of neural circuits

Before the onset of sensory transduction, developing neural circuits spontaneously generate correlated activity in distinct spatial and temporal patterns. During this period of patterned activity, sensory maps develop and initial coarse connections are refined, which are critical steps in the establishment of adult neural circuits. Over the last decade, there has been substantial evidence that altering the pattern of spontaneous activity disrupts refinement, but the mechanistic understanding of this process remains incomplete. In this review, we discuss recent experimental and theoretical progress toward the process of activity-dependent refinement, focusing on circuits in the visual, auditory, and motor systems. Although many outstanding questions remain, the combination of several novel approaches has brought us closer to a comprehensive understanding of how complex neural circuits are established by patterned spontaneous activity during development.

MicroRNAs in neuronal communication

MicroRNAs (miRNAs) are short nucleotides sequences that regulate the expression of genes in different eukaryotic cell types. A tremendous amount of knowledge on miRNAs has rapidly accumulated over the last few years, revealing the growing interest in this field of research. On the other hand, clarifying the physiological regulation of gene expression in the central nervous system is important for establishing a reference for comparison to the diseased state. It is well known that the fine tuning of neuronal networks relies on intricate molecular mechanisms, such as the adjustment of the synaptic transmission. As determined by recent studies, regulation of neuronal interactions by miRNAs has critical consequences in the development, adaptation to ambient demands, and degeneration of the nervous system. In contrast, activation of synaptic receptors triggers downstream signaling cascades that generate a vast array of effects, which includes the regulation of novel genes involved in the control of the miRNA life cycle. In this review, we have examined the hot topics on miRNA gene-regulatory activities in the broad field of neuronal communication-related processes. Furthermore, in addition to indicating the newly described effect of miRNAs on the regulation of specific neurotransmitter systems, we have pointed out how these systems affect the expression, transport, and stability of miRNAs. Moreover, we discuss newly described and under-investigation mechanisms involving the intercellular transfer of miRNAs, aided by exosomes and gap junctions. Thus, in the current review, we were able to highlight recent findings related to miRNAs that indisputably contributed towards the understanding of the nervous system in health and disease.

Following the ontogeny of retinal waves: pan-retinal recordings of population dynamics in the neonatal mouse

The immature retina generates spontaneous waves of spiking activity that sweep across the ganglion cell layer during a limited period of development before the onset of visual experience. The spatiotemporal patterns encoded in the waves are believed to be instructive for the wiring of functional connections throughout the visual system. However, the ontogeny of retinal waves is still poorly documented as a result of the relatively low resolution of conventional recording techniques. Here, we characterize the spatiotemporal features of mouse retinal waves from birth until eye opening in unprecedented detail using a large-scale, dense, 4096-channel multielectrode array that allowed us to record from the entire neonatal retina at near cellular resolution. We found that early cholinergic waves propagate with random trajectories over large areas with low ganglion cell recruitment. They become slower, smaller and denser when GABA_A signalling matures, as occurs beyond postnatal day (P) 7. Glutamatergic influences dominate from P10, coinciding with profound changes in activity dynamics. At this time, waves cease to be random and begin to show repetitive trajectories confined to a few localized hotspots. These hotspots gradually tile the retina with time, and disappear after eye opening. Our observations demonstrate that retinal waves undergo major spatiotemporal changes during ontogeny. Our results support the hypotheses that cholinergic waves guide the refinement of retinal targets and that glutamatergic waves may also support the wiring of retinal receptive fields.

Spontaneous Network Activity and Synaptic Development

Throughout development, the nervous system produces patterned spontaneous activity. Research over the past two decades has revealed a core group of mechanisms that mediate spontaneous activity in diverse circuits. Many circuits engage several of these mechanisms sequentially to accommodate developmental changes in connectivity. In addition to shared mechanisms, activity propagates through developing circuits and neuronal pathways (i.e., linked circuits in different brain areas) in stereotypic patterns. Increasing evidence suggests that spontaneous network activity shapes synaptic development in vivo Variations in activity-dependent plasticity may explain how similar mechanisms and patterns of activity can be employed to establish diverse circuits. Here, I will review common mechanisms and patterns of spontaneous activity in emerging neural networks and discuss recent insights into their contribution to synaptic development.

Neutral stability, rate propagation, and critical branching in feedforward networks

Recent experimental and computational evidence suggests that several dynamical properties may characterize the operating point of functioning neural networks: critical branching, neutral stability, and production of a wide range of firing patterns. We seek the simplest setting in which these properties emerge, clarifying their origin and relationship in random, feedforward networks of McCullochs-Pitts neurons. Two key parameters are the thresholds at which neurons fire spikes and the overall level of feedforward connectivity. When neurons have low thresholds, we show that there is always a connectivity for which the properties in question all occur, that is, these networks preserve overall firing rates from layer to layer and produce broad distributions of activity in each layer. This fails to occur, however, when neurons have high thresholds. A key tool in explaining this difference is the eigenstructure of the resulting mean-field Markov chain, as this reveals which activity modes will be preserved from layer to layer. We extend our analysis from purely excitatory networks to more complex models that include inhibition and local noise, and find that both of these features extend the parameter ranges over which networks produce the properties of interest.

A role for TREK1 in generating the slow afterhyperpolarization in developing starburst amacrine cells

Slow afterhyperpolarizations (sAHPs) play an important role in establishing the firing pattern of neurons that in turn influence network activity. sAHPs are mediated by calcium-activated potassium channels. However, the molecular identity of these channels and the mechanism linking calcium entry to their activation are still unknown. Here we present several lines of evidence suggesting that the sAHPs in developing starburst amacrine cells (SACs) are mediated by two-pore potassium channels. First, we use whole cell and perforated patch voltage clamp recordings to characterize the sAHP conductance under different pharmacological conditions. We find that this conductance was calcium dependent, reversed at EK, blocked by barium, insensitive to apamin and TEA, and activated by arachidonic acid. In addition, pharmacological inhibition of calcium-activated phosphodiesterase reduced the sAHP. Second, we performed gene profiling on isolated SACs and found that they showed strong preferential expression of the two-pore channel gene kcnk2 that encodes TREK1. Third, we demonstrated that TREK1 knockout animals exhibited an altered frequency of retinal waves, a frequency that is set by the sAHPs in SACs. With these results, we propose a model in which depolarization-induced decreases in cAMP lead to disinhibition of the two-pore potassium channels and in which the kinetics of this biochemical pathway dictate the slow activation and deactivation of the sAHP conductance. Our model offers a novel pathway for the activation of a conductance that is physiologically important.

Hyperpolarization of resting membrane potential causes retraction of spontaneous Ca(i)²⁺ transients during mouse embryonic circuit development

Abstract  Spontaneous activity supports developmental processes in many brain regions during embryogenesis, and the spatial extent and frequency of the spontaneous activity are tightly regulated by stage. In the developing mouse hindbrain, spontaneous activity propagates widely and the waves can cover the entire hindbrain at E11.5. The activity then retracts to waves that are spatially restricted to the rostral midline at E13.5, before disappearing altogether by E15.5. However, the mechanism of retraction is unknown. We studied passive membrane properties of cells that are spatiotemporally relevant to the pattern of retraction in mouse embryonic hindbrain using whole-cell patch clamp and imaging techniques. We find that membrane excitability progressively decreases due to hyperpolarization of resting membrane potential and increased resting conductance density between E11.5 and E15.5, in a spatiotemporal pattern correlated with the retraction sequence. Retraction can be acutely reversed by membrane depolarization at E15.5, and the induced events propagate similarly to spontaneous activity at earlier stages, though without involving gap junctional coupling. Manipulation of [K(+)](o) or [Cl(-)](o) reveals that membrane potential follows E(K) more closely than E(Cl), suggesting a dominant role for K(+) conductance in the membrane hyperpolarization. Reducing membrane excitability by hyperpolarization of the resting membrane potential and increasing resting conductance are effective mechanisms to desynchronize spontaneous activity in a spatiotemporal manner, while allowing information processing to occur at the synaptic and cellular level.

Blocking of connexin-mediated communication promotes neuroprotection during acute degeneration induced by mechanical trauma

Accruing evidence indicates that connexin (Cx) channels in the gap junctions (GJ) are involved in neurodegeneration after injury. However, studies using KO animal models endowed apparently contradictory results in relation to the role of coupling in neuroprotection. We analyzed the role of Cx-mediated communication in a focal lesion induced by mechanical trauma of the retina, a model that allows spatial and temporal definition of the lesion with high reproducibility, permitting visualization of the focus, penumbra and adjacent areas. Cx36 and Cx43 exhibited distinct gene expression and protein levels throughout the neurodegeneration progress. Cx36 was observed close to TUNEL-positive nuclei, revealing the presence of this protein surrounding apoptotic cells. The functional role of cell coupling was assessed employing GJ blockers and openers combined with lactate dehydrogenase (LDH) assay, a direct method for evaluating cell death/viability. Carbenoxolone (CBX), a broad-spectrum GJ blocker, reduced LDH release after 4 hours, whereas quinine, a Cx36-channel specific blocker, decreased LDH release as early as 1 hour after lesion. Furthermore, analysis of dying cell distribution confirmed that the use of GJ blockers reduced apoptosis spread. Accordingly, blockade of GJ communication during neurodegeneration with quinine, but not CBX, caused downregulation of initial and effector caspases. To summarize, we observed specific changes in Cx gene expression and protein distribution during the progress of retinal degeneration, indicating the participation of these elements in acute neurodegeneration processes. More importantly, our results revealed that direct control of GJ channels permeability may take part in reliable neuroprotection strategies aimed to rapid, fast treatment of mechanical trauma in the retina.

Self-Regulated Dynamical Criticality in Human ECoG

Mounting experimental and theoretical results indicate that neural systems are poised near a critical state. In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution. Electrocorticography (ECoG) provides a unique window into human brain activity: each electrode records, with high temporal resolution, the activity resulting from the sum of the local field potentials of ∼10⁵ neurons. We show that the human brain ECoG recordings display features of self-regulated dynamical criticality: dynamical modes of activation drift around the critical stability threshold, moving in and out of the unstable region and equilibrating the global dynamical state at a very fast time scale. Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

Cellular mechanisms underlying spatiotemporal features of cholinergic retinal waves

Before vision, a transient network of recurrently connected cholinergic interneurons, called starburst amacrine cells (SACs), generates spontaneous retinal waves. Despite an absence of robust inhibition, cholinergic retinal waves initiate infrequently and propagate within finite boundaries. Here, we combine a variety of electrophysiological and imaging techniques and computational modeling to elucidate the mechanisms underlying these spatial and temporal properties of waves in developing mouse retina. Waves initiate via rare spontaneous depolarizations of SACs. Waves propagate through recurrent cholinergic connections between SACs and volume release of ACh as demonstrated using paired recordings and a cell-based ACh optical sensor. Perforated-patch recordings and two-photon calcium imaging reveal that individual SACs have slow afterhyperpolarizations that induce SACs to have variable depolarizations during sequential waves. Using a computational model in which the properties of SACs are based on these physiological measurements, we reproduce the slow frequency, speed, and finite size of recorded waves. This study represents a detailed description of the circuit that mediates cholinergic retinal waves and indicates that variability of the interneurons that generate this network activity may be critical for the robustness of waves across different species and stages of development.

From retinal waves to activity-dependent retinogeniculate map development

A neural model is described of how spontaneous retinal waves are formed in infant mammals, and how these waves organize activity-dependent development of a topographic map in the lateral geniculate nucleus, with connections from each eye segregated into separate anatomical layers. The model simulates the spontaneous behavior of starburst amacrine cells and retinal ganglion cells during the production of retinal waves during the first few weeks of mammalian postnatal development. It proposes how excitatory and inhibitory mechanisms within individual cells, such as Ca(2+)-activated K(+) channels, and cAMP currents and signaling cascades, can modulate the spatiotemporal dynamics of waves, notably by controlling the after-hyperpolarization currents of starburst amacrine cells. Given the critical role of the geniculate map in the development of visual cortex, these results provide a foundation for analyzing the temporal dynamics whereby the visual cortex itself develops.

Age-dependent homeostatic plasticity of GABAergic signaling in developing retinal networks

Developing retinal ganglion cells fire in correlated spontaneous bursts, resulting in propagating waves with robust spatiotemporal features preserved across development and species. Here we investigate the effects of homeostatic adaptation on the circuits controlling retinal waves. Mouse retinal waves were recorded in vitro for up to 35 h with a multielectrode array in presence of the GABA(A) antagonist bicuculline, allowing us to obtain a precise, time-resolved characterization of homeostatic effects in this preparation. Experiments were performed at P4-P6, when GABA(A) signaling is depolarizing in ganglion cells, and at P7-P10, when GABA(A) signaling is hyperpolarizing. At all ages, bicuculline initially increased the wave sizes and other activity metrics. At P5-P6, wave sizes decreased toward control levels within a few hours while firing remained strong, but this ability to compensate disappeared entirely from P7 onwards. This demonstrates that homeostatic control of spontaneous retinal activity maintains specific network dynamic properties in an age-dependent manner, and suggests that the underlying mechanism is linked to GABA(A) signaling.

Assembly and disassembly of a retinal cholinergic network

In the few weeks prior to the onset of vision, the retina undergoes a dramatic transformation. Neurons migrate into position and target appropriate synaptic partners to assemble the circuits that mediate vision. During this period of development, the retina is not silent but rather assembles and disassembles a series of transient circuits that use distinct mechanisms to generate spontaneous correlated activity called retinal waves. During the first postnatal week, this transient circuit is comprised of reciprocal cholinergic connections between starburst amacrine cells. A few days before the eyes open, these cholinergic connections are eliminated as the glutamatergic circuits involved in processing visual information are formed. Here, we discuss the assembly and disassembly of this transient cholinergic network and the role it plays in various aspects of retinal development.

Modeling developmental patterns of spontaneous activity

Spontaneous activity is found in many regions of the developing nervous system; such activity is thought to be instructive for guiding developmental processes. In particular, the developing retina generates correlated patterns of activity known as retinal waves. We review the main theoretical models that have been developed to study the mechanisms for generation and propagation of retinal waves. Much of the progress in this field has been due to the close interaction between experimentalists and theorists in analyzing and modeling spontaneous activity. We conclude by describing spontaneous activity models in other systems and suggestions for future modeling work.

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis).

Flexible, all-polymer microelectrode arrays for the capture of cardiac and neuronal signals

Microelectrode electrophysiology has become a widespread technique for the extracellular recording of bioelectrical signals. To date, electrodes are made of metals or inorganic semiconductors, or hybrids thereof. We demonstrate that these traditional conductors can be completely substituted by highly flexible electroconductive polymers. Pursuing a two-level replica-forming strategy, conductive areas for electrodes, leads and contact pads are defined as microchannels in poly(dimethylsiloxane) (PDMS) as a plastic carrier and track insulation material. These channels are coated by films of organic conductors such as polystyrenesulfonate-doped poly(3,4-ethylenedioxy-thiophene) (PEDOT:PSS) or filled with a graphite-PDMS (gPDMS) composite, either alone or in combination. The bendable, somewhat stretchable, non-cytotoxic and biostable all-polymer microelectrode arrays (polyMEAs) with a thickness below 500 μm and up to 60 electrodes reliably capture action potentials (APs) and local field potentials (LFPs) from acute preparations of heart muscle cells and retinal whole mounts, in vivo epicortical and epidural recordings as well as during long-term in vitro recordings from cortico-hippocampal co-cultures.

Spike avalanches exhibit universal dynamics across the sleep-wake cycle

BACKGROUND

Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function.

METHODOLOGY/PRINCIPAL FINDINGS

To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus.

CONCLUSIONS/SIGNIFICANCE

Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

Fractals in the nervous system: conceptual implications for theoretical neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power-law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review.

Optimal hierarchical modular topologies for producing limited sustained activation of neural networks

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show a wider parameter range for LSA than random or small-world networks not possessing hierarchical organization or multiple modules. Here we explored how variation in the number of hierarchical levels and modules per level influenced network dynamics and occurrence of LSA. We tested hierarchical configurations of different network sizes, approximating the large-scale networks linking cortical columns in one hemisphere of the rat, cat, or macaque monkey brain. Scaling of the network size affected the number of hierarchical levels and modules in the optimal networks, also depending on whether global edge density or the numbers of connections per node were kept constant. For constant edge density, only few network configurations, possessing an intermediate number of levels and a large number of modules, led to a large range of LSA independent of brain size. For a constant number of node connections, there was a trend for optimal configurations in larger-size networks to possess a larger number of hierarchical levels or more modules. These results may help to explain the trend to greater network complexity apparent in larger brains and may indicate that this complexity is required for maintaining stable levels of neural activation.

Can power-law scaling and neuronal avalanches arise from stochastic dynamics?

The presence of self-organized criticality in biology is often evidenced by a power-law scaling of event size distributions, which can be measured by linear regression on logarithmic axes. We show here that such a procedure does not necessarily mean that the system exhibits self-organized criticality. We first provide an analysis of multisite local field potential (LFP) recordings of brain activity and show that event size distributions defined as negative LFP peaks can be close to power-law distributions. However, this result is not robust to change in detection threshold, or when tested using more rigorous statistical analyses such as the Kolmogorov–Smirnov test. Similar power-law scaling is observed for surrogate signals, suggesting that power-law scaling may be a generic property of thresholded stochastic processes. We next investigate this problem analytically, and show that, indeed, stochastic processes can produce spurious power-law scaling without the presence of underlying self-organized criticality. However, this power-law is only apparent in logarithmic representations, and does not survive more rigorous analysis such as the Kolmogorov–Smirnov test. The same analysis was also performed on an artificial network known to display self-organized criticality. In this case, both the graphical representations and the rigorous statistical analysis reveal with no ambiguity that the avalanche size is distributed as a power-law. We conclude that logarithmic representations can lead to spurious power-law scaling induced by the stochastic nature of the phenomenon. This apparent power-law scaling does not constitute a proof of self-organized criticality, which should be demonstrated by more stringent statistical tests.

Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus

Spontaneous retinal activity (known as "waves") remodels synaptic connectivity to the lateral geniculate nucleus (LGN) during development. Analysis of retinal waves recorded with multielectrode arrays in mouse suggested that a cue for the segregation of functionally distinct (ON and OFF) retinal ganglion cells (RGCs) in the LGN may be a desynchronization in their firing, where ON cells precede OFF cells by one second. Using the recorded retinal waves as input, with two different modeling approaches we explore timing-based plasticity rules for the evolution of synaptic weights to identify key features underlying ON/OFF segregation. First, we analytically derive a linear model for the evolution of ON and OFF weights, to understand how synaptic plasticity rules extract input firing properties to guide segregation. Second, we simulate postsynaptic activity with a nonlinear integrate-and-fire model to compare findings with the linear model. We find that spike-time-dependent plasticity, which modifies synaptic weights based on millisecond-long timing and order of pre- and postsynaptic spikes, fails to segregate ON and OFF retinal inputs in the absence of normalization. Implementing homeostatic mechanisms results in segregation, but only with carefully-tuned parameters. Furthermore, extending spike integration timescales to match the second-long input correlation timescales always leads to ON segregation because ON cells fire before OFF cells. We show that burst-time-dependent plasticity can robustly guide ON/OFF segregation in the LGN without normalization, by integrating pre- and postsynaptic bursts irrespective of their firing order and over second-long timescales. We predict that an LGN neuron will become ON- or OFF-responsive based on a local competition of the firing patterns of neighboring RGCs connecting to it. Finally, we demonstrate consistency with ON/OFF segregation in ferret, despite differences in the firing properties of retinal waves. Our model suggests that diverse input statistics of retinal waves can be robustly interpreted by a burst-based rule, which underlies retinogeniculate plasticity across different species.

Mechanisms underlying spontaneous patterned activity in developing neural circuits

Patterned, spontaneous activity occurs in many developing neural circuits, including the retina, the cochlea, the spinal cord, the cerebellum and the hippocampus, where it provides signals that are important for the development of neurons and their connections. Despite there being differences in adult architecture and output across these various circuits, the patterns of spontaneous network activity and the mechanisms that generate it are remarkably similar. The mechanisms can include a depolarizing action of GABA (gamma-aminobutyric acid), transient synaptic connections, extrasynaptic transmission, gap junction coupling and the presence of pacemaker-like neurons. Interestingly, spontaneous activity is robust; if one element of a circuit is disrupted another will generate similar activity. This research suggests that developing neural circuits exhibit transient and tunable features that maintain a source of correlated activity during crucial stages of development.

A computational study on the role of gap junctions and rod Ih conductance in the enhancement of the dynamic range of the retina

Recent works suggest that one of the roles of gap junctions in sensory systems is to enhance their dynamic range by avoiding early saturation in the first processing stages. In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current I_h on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics. Our results show that for realistic values of I_h conductance the dynamic range is enhanced by rod-rod coupling, and that AII-AII coupling is less relevant to dynamic range amplification in comparison with receptor coupling. Furthermore, a plot of the retina output response versus input intensity for the optimal parameter configuration is well fitted by a power law with exponent . The results are consistent with predictions of more theoretical works and suggest that the earliest expression of gap junctions along the rod pathways, together with appropriate values of rod I_h conductance, has the highest impact on vertebrate retina dynamic range enhancement.

Theoretical models of spontaneous activity generation and propagation in the developing retina

Spontaneous neural activity is present in many parts of the developing nervous system, including visual, auditory and motor areas. In the developing retina, nearby neurons are spontaneously active and produce propagating patterns of activity, known as retinal waves. Such activity is thought to instruct the refinement of retinal axons. In this article we review several computational models used to help evaluate the mechanisms that might be responsible for the generation of retinal waves. We then discuss the models relative to the molecular mechanisms underlying wave activity, including gap junctions, neurotransmitters and second messenger systems. We examine how well the models represent these mechanisms and propose areas for future modelling research. The retinal wave models are also discussed in relation to models of spontaneous activity in other areas of the developing nervous system.

Self-tuned critical anti-Hebbian networks

It is widely recognized that balancing excitation and inhibition is important in the nervous system. When such a balance is sought by global strategies, few modes remain poised close to instability, and all other modes are strongly stable. Here we present a simple abstract model in which this balance is sought locally by units following "anti-Hebbian" evolution: all degrees of freedom achieve a close balance of excitation and inhibition and become "critical" in the dynamical sense. At long time scales, a complex "breakout" dynamics ensues in which different modes of the system oscillate between prominence and extinction; the model develops various long-tailed statistical behaviors and may become self-organized critical.

Carbon nanotube electrodes for effective interfacing with retinal tissue

We have investigated the use of carbon nanotube coated microelectrodes as an interface material for retinal recording and stimulation applications. Test devices were micro-fabricated and consisted of 60, 30 μm diameter electrodes at spacing of 200 μm. These electrodes were coated via chemical vapor deposition of carbon nanotubes, resulting in conducting, three dimensional surfaces with a high interfacial area. These attributes are important both for the quality of the cell-surface coupling as well as for electro-chemical interfacing efficiency. The entire chip was packaged to fit a commercial multielectrode recording and stimulation system. Electrical recordings of spontaneous spikes from whole-mount neonatal mouse retinas were consistently obtained minutes after retinas were placed over the electrodes, exhibiting typical bursting and propagating waves. Most importantly, the signals obtained with carbon nanotube electrodes have exceptionally high signal to noise ratio, reaching values as high as 75. Moreover, spikes are marked by a conspicuous gradual increase in amplitude recorded over a period of minutes to hours, suggesting improvement in cell-electrode coupling. This phenomenon is not observed in conventional commercial electrodes. Electrical stimulation using carbon nanotube electrodes was also achieved. We attribute the superior performances of the carbon nanotube electrodes to their three dimensional nature and the strong neuro-carbon nanotube affinity. The results presented here show the great potential of carbon nanotube electrodes for retinal interfacing applications. Specifically, our results demonstrate a route to achieve a reduction of the electrode down to few micrometers in order to achieve high efficacy local stimulation needed in retinal prosthetic devices.