Stephen A. Baccus

All Publications

• Interpreting the retinal neural code for natural scenes: From computations to neurons. Neuron Maheswaranathan, N., McIntosh, L. T., Tanaka, H., Grant, S., Kastner, D. B., Melander, J. B., Nayebi, A., Brezovec, L. E., Wang, J. H., Ganguli, S., Baccus, S. A. 2023

  Abstract

  Understanding the circuit mechanisms of the visual code for natural scenes is a central goal of sensory neuroscience. We show that a three-layer network model predicts retinal natural scene responses with an accuracy nearing experimental limits. The model's internal structure is interpretable, as interneurons recorded separately and not modeled directly are highly correlated with model interneurons. Models fitted only to natural scenes reproduce a diverse set of phenomena related to motion encoding, adaptation, and predictive coding, establishing their ethological relevance to natural visual computation. A new approach decomposes the computations of model ganglion cells into the contributions of model interneurons, allowing automatic generation of new hypotheses for how interneurons with different spatiotemporal responses are combined to generate retinal computations, including predictive phenomena currently lacking an explanation. Our results demonstrate a unified and general approach to study the circuit mechanisms of ethological retinal computations under natural visual scenes.

  View details for DOI 10.1016/j.neuron.2023.06.007

  View details for PubMedID 37451264

• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution Nguyen, E., Poli, M., Faizi, M., Thomas, A. W., Sykes, C., Wornow, M., Patel, A., Rabideau, C., Massaroli, S., Bengio, Y., Ermon, S., Baccus, S. A., Re, C., Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001224281505021

• Information Geometry of the Retinal Representation Manifold Ding, X., Lee, D., Melander, J. B., Sivulka, G., Ganguli, S., Baccus, S. A., Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001224281507024

• Synchronous inhibitory pathways create both efficiency and diversity in the retina. Proceedings of the National Academy of Sciences of the United States of America Manu, M., McIntosh, L. T., Kastner, D. B., Naecker, B. N., Baccus, S. A. 1800; 119 (4)

  Abstract

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

  View details for DOI 10.1073/pnas.2116589119

  View details for PubMedID 35064086

• How inhibitory neurons increase information transmission under threshold modulation. Cell reports Hsu, W. M., Kastner, D. B., Baccus, S. A., Sharpee, T. O. 2021; 35 (8): 109158

  Abstract

  Modulation of neuronal thresholds is ubiquitous in the brain. Phenomena such as figure-ground segmentation, motion detection, stimulus anticipation, and shifts in attention all involve changes in a neuron's threshold based on signals from larger scales than its primary inputs. However, this modulation reduces the accuracy with which neurons can represent their primary inputs, creating a mystery as to why threshold modulation is so widespread in the brain. We find that modulation is less detrimental than other forms of neuronal variability and that its negative effects can be nearly completely eliminated if modulation is applied selectively to sparsely responding neurons in a circuit by inhibitory neurons. We verify these predictions in the retina where we find that inhibitory amacrine cells selectively deliver modulation signals to sparsely responding ganglion cell types. Our findings elucidate the central role that inhibitory neurons play in maximizing information transmission under modulation.

  View details for DOI 10.1016/j.celrep.2021.109158

  View details for PubMedID 34038717

• A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics. Conference record. Asilomar Conference on Signals, Systems & Computers Ding, X., Lee, D., Grant, S., Stein, H., McIntosh, L., Maheswaranathan, N., Baccus, S. 2021; 2021: 287-291

  Abstract

  The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

  View details for DOI 10.1109/ieeeconf53345.2021.9723187

  View details for PubMedID 38013729

  View details for PubMedCentralID PMC10680971

• From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. Advances in neural information processing systems Tanaka, H., Nayebi, A., Maheswaranathan, N., McIntosh, L., Baccus, S. A., Ganguli, S. 2019; 32: 8537-8547

  Abstract

  Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (a biological circuit) with another (a deep network), without understanding either? Moreover, beyond neural representations, are the deep network's computational mechanisms for generating neural responses the same as those in the brain? Without a systematic approach to extracting and understanding computational mechanisms from deep neural network models, it can be difficult both to assess the degree of utility of deep learning approaches in neuroscience, and to extract experimentally testable hypotheses from deep networks. We develop such a systematic approach by combining dimensionality reduction and modern attribution methods for determining the relative importance of interneurons for specific visual computations. We apply this approach to deep network models of the retina, revealing a conceptual understanding of how the retina acts as a predictive feature extractor that signals deviations from expectations for diverse spatiotemporal stimuli. For each stimulus, our extracted computational mechanisms are consistent with prior scientific literature, and in one case yields a new mechanistic hypothesis. Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.

  View details for PubMedID 35283616

  View details for PubMedCentralID PMC8916592

• Adaptation of Inhibition Mediates Retinal Sensitization. Current biology : CB Kastner, D. B., Ozuysal, Y., Panagiotakos, G., Baccus, S. A. 2019

  Abstract

  In response to a changing sensory environment, sensory systems adjust their neural code for a number of purposes, including an enhanced sensitivity for novel stimuli, prediction of sensory features, and the maintenance of sensitivity. Retinal sensitization is a form of short-term plasticity that elevates local sensitivity following strong, local, visual stimulation and has been shown to create a prediction of the presence of a nearby localized object. The neural mechanism that generates this elevation in sensitivity remains unknown. Using simultaneous intracellular and multielectrode recording in the salamander retina, we show that a decrease in tonic amacrine transmission is necessary for and is correlated spatially and temporally with ganglion cell sensitization. Furthermore, introducing a decrease in amacrine transmission is sufficient to sensitize nearby ganglion cells. A computational model accounting for adaptive dynamics and nonlinear pathways confirms a decrease in steady inhibitory transmission can cause sensitization. Adaptation of inhibition enhances the sensitivity to the sensory feature conveyed by an inhibitory pathway, creating a prediction of future input.

  View details for DOI 10.1016/j.cub.2019.06.081

  View details for PubMedID 31378605

• Radiation force as a physical mechanism for ultrasonic neurostimulation of the ex vivo retina. The Journal of neuroscience : the official journal of the Society for Neuroscience Menz, M. D., Ye, P. n., Firouzi, K. n., Nikoozadeh, A. n., Butts Pauly, K. n., Khuri-Yakub, P. n., Baccus, S. A. 2019

  Abstract

  Focused ultrasound has been shown to be effective at stimulating neurons in many animal models, both in vivo and ex vivo. Ultrasonic neuromodulation is the only non-invasive method of stimulation that could reach deep in the brain with high spatial-temporal resolution, and thus has potential for use in clinical applications and basic studies of the nervous system. Understanding the physical mechanism by which energy in a high acoustic frequency wave is delivered to stimulate neurons will be important to optimize this technology. We imaged the isolated salamander retina of either sex during ultrasonic stimuli that drive ganglion cell activity and observed micron scale displacements, consistent with radiation force, the nonlinear delivery of momentum by a propagating wave. We recorded ganglion cell spiking activity and changed the acoustic carrier frequency across a broad range (0.5 - 43 MHz), finding that increased stimulation occurs at higher acoustic frequencies, ruling out cavitation as an alternative possible mechanism. A quantitative radiation force model can explain retinal responses and could potentially explain previous in vivo results in the mouse, suggesting a new hypothesis to be tested in vivo. Finally, we found that neural activity was strongly modulated by the distance between the transducer and the electrode array showing the influence of standing waves on the response. We conclude that radiation force is the dominant physical mechanism underlying ultrasonic neurostimulation in the ex vivo retina and propose that the control of standing waves is a new potential method to modulate these effects.SIGNIFICANCE STATEMENTUltrasonic neurostimulation is a promising noninvasive technology that has potential for both basic research and clinical applications. The mechanisms of ultrasonic neurostimulation are unknown, making it difficult to optimize in any given application. We studied the physical mechanism by which ultrasound is converted into an effective energy form to cause neurostimulation in the retina and find that ultrasound acts through radiation force leading to a mechanical displacement of tissue. We further show that standing waves have a strong modulatory effect on activity. Our quantitative model by which ultrasound generates radiation force and leads to neural activity will be important in optimizing ultrasonic neurostimulation across a wide range of applications.

  View details for DOI 10.1523/JNEUROSCI.2394-18.2019

  View details for PubMedID 31196935

• From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction Tanaka, H., Nayebi, A., Maheswaranathan, N., McIntosh, L., Baccus, S. A., Ganguli, S., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866900016

• Adaptive feature detection from differential processing in parallel retinal pathways. PLoS computational biology Ozuysal, Y., Kastner, D. B., Baccus, S. A. 2018; 14 (11): e1006560

  Abstract

  To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell's membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than synaptic release dynamics as has previously been proposed to underlie contrast adaptation. Different efficient codes are selected by different thresholds in two independently adapting neural pathways.

  View details for PubMedID 30457994

• Inferring hidden structure in multilayered neural circuits. PLoS computational biology Maheswaranathan, N., Kastner, D. B., Baccus, S. A., Ganguli, S. 2018; 14 (8): e1006291

  Abstract

  A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.

  View details for PubMedID 30138312

• Inferring hidden structure in multilayered neural circuits PLOS COMPUTATIONAL BIOLOGY Maheswaranathan, N., Kastner, D. B., Baccus, S. A., Ganguli, S. 2018; 14 (8)

  View details for DOI 10.1371/journal.pcbi.1006291

  View details for Web of Science ID 000443298500009

• Ultrasound Elicits Behavioral Responses through Mechanical Effects on Neurons and Ion Channels in a Simple Nervous System JOURNAL OF NEUROSCIENCE Kubanek, J., Shukla, P., Das, A., Baccus, S. A., Goodman, M. B. 2018; 38 (12): 3081–91

  Abstract

  Focused ultrasound has been shown to stimulate excitable cells, but the biophysical mechanisms behind this phenomenon remain poorly understood. To provide additional insight, we devised a behavioral-genetic assay applied to the well-characterized nervous system of Caenorhabditis elegans nematodes. We found that pulsed ultrasound elicits robust reversal behavior in wild-type animals in a pressure-, duration-, and pulse protocol-dependent manner. Responses were preserved in mutants unable to sense thermal fluctuations and absent in mutants lacking neurons required for mechanosensation. Additionally, we found that the worm's response to ultrasound pulses rests on the expression of MEC-4, a DEG/ENaC/ASIC ion channel required for touch sensation. Consistent with prior studies of MEC-4-dependent currents in vivo, the worm's response was optimal for pulses repeated 300-1000 times per second. Based on these findings, we conclude that mechanical, rather than thermal, stimulation accounts for behavioral responses. Further, we propose that acoustic radiation force governs the response to ultrasound in a manner that depends on the touch receptor neurons and MEC-4-dependent ion channels. Our findings illuminate a complete pathway of ultrasound action, from the forces generated by propagating ultrasound to an activation of a specific ion channel. The findings further highlight the importance of optimizing ultrasound pulsing protocols when stimulating neurons via ion channels with mechanosensitive properties.SIGNIFICANCE STATEMENT How ultrasound influences neurons and other excitable cells has remained a mystery for decades. Although it is widely understood that ultrasound can heat tissues and induce mechanical strain, whether or not neuronal activation depends on heat, mechanical force, or both physical factors is not known. We harnessed Caenorhabditis elegans nematodes and their extraordinary sensitivity to thermal and mechanical stimuli to address this question. Whereas thermosensory mutants respond to ultrasound similar to wild-type animals, mechanosensory mutants were insensitive to ultrasound stimulation. Additionally, stimulus parameters that accentuate mechanical effects were more effective than those producing more heat. These findings highlight a mechanical nature of the effect of ultrasound on neurons and suggest specific ways to optimize stimulation protocols in specific tissues.

  View details for DOI 10.1523/JNEUROSCI.1458-17.2018

  View details for Web of Science ID 000428156900016

  View details for PubMedID 29463641

  View details for PubMedCentralID PMC5864152

• Optimal Information Transmission by Overlapping Retinal Cell Mosaics. Proceedings of the ... Conference on Information Sciences and Systems. Conference on Information Sciences and Systems Zhang, Y., Kastner, D. B., Baccus, S. A., Sharpee, T. O. 2018; 2018

  Abstract

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

  View details for DOI 10.1109/ciss.2018.8362310

  View details for PubMedID 34746939

  View details for PubMedCentralID PMC8570562

• A Communication-Theoretic Formulation of a Continuous Linear-Nonlinear Model of Retinal Ganglion Cells Sorooshyari, S. K., Baccus, S. A., IEEE IEEE. 2018

  View details for Web of Science ID 000434867200065

• Optimal Information Transmission by Overlapping Retinal Cell Mosaics Zhang, Y., Kastner, D. B., Baccus, S. A., Sharpee, T. O., IEEE IEEE. 2018

  View details for Web of Science ID 000434867200080

• Deep Learning Models of the Retinal Response to Natural Scenes. Advances in neural information processing systems McIntosh, L. T., Maheswaranathan, N. n., Nayebi, A. n., Ganguli, S. n., Baccus, S. A. 2016; 29: 1369–77

  Abstract

  A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of accurate computational models of responses to natural stimuli. Here we demonstrate that deep convolutional neural networks (CNNs) capture retinal responses to natural scenes nearly to within the variability of a cell's response, and are markedly more accurate than linear-nonlinear (LN) models and Generalized Linear Models (GLMs). Moreover, we find two additional surprising properties of CNNs: they are less susceptible to overfitting than their LN counterparts when trained on small amounts of data, and generalize better when tested on stimuli drawn from a different distribution (e.g. between natural scenes and white noise). An examination of the learned CNNs reveals several properties. First, a richer set of feature maps is necessary for predicting the responses to natural scenes compared to white noise. Second, temporally precise responses to slowly varying inputs originate from feedforward inhibition, similar to known retinal mechanisms. Third, the injection of latent noise sources in intermediate layers enables our model to capture the sub-Poisson spiking variability observed in retinal ganglion cells. Fourth, augmenting our CNNs with recurrent lateral connections enables them to capture contrast adaptation as an emergent property of accurately describing retinal responses to natural scenes. These methods can be readily generalized to other sensory modalities and stimulus ensembles. Overall, this work demonstrates that CNNs not only accurately capture sensory circuit responses to natural scenes, but also can yield information about the circuit's internal structure and function.

  View details for PubMedID 28729779

• Synchronized amplification of local information transmission by peripheral retinal input ELIFE Jadzinsky, P. D., Baccus, S. A. 2015; 4

  Abstract

  Sensory stimuli have varying statistics influenced by both the environment and by active sensing behaviors that rapidly and globally change the sensory input. Consequently, sensory systems often adjust their neural code to the expected statistics of their sensory input to transmit novel sensory information. Here, we show that sudden peripheral motion amplifies and accelerates information transmission in salamander ganglion cells in a 50 ms time window. Underlying this gating of information is a transient increase in adaptation to contrast, enhancing sensitivity to a broader range of stimuli. Using a model and natural images, we show that this effect coincides with an expected increase in information in bipolar cells after a global image shift. Our findings reveal the dynamic allocation of energy resources to increase neural activity at times of expected high information content, a principle of adaptation that balances the competing requirements of conserving spikes and transmitting information.

  View details for DOI 10.7554/eLife.09266

  View details for Web of Science ID 000373848100001

  View details for PubMedID 26568312

  View details for PubMedCentralID PMC4749570

• Critical and maximally informative encoding between neural populations in the retina. Proceedings of the National Academy of Sciences of the United States of America Kastner, D. B., Baccus, S. A., Sharpee, T. O. 2015; 112 (8): 2533-2538

  Abstract

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

  View details for DOI 10.1073/pnas.1418092112

  View details for PubMedID 25675497

  View details for PubMedCentralID PMC4345597

• Building Blocks of Temporal Filters in Retinal Synapses PLOS BIOLOGY Suh, B., Baccus, S. A. 2014; 12 (10)

  View details for DOI 10.1371/journal.pbio.1001973

  View details for Web of Science ID 000344461700012

• Building blocks of temporal filters in retinal synapses. PLoS biology Suh, B., Baccus, S. A. 2014; 12 (10)

  Abstract

  Sensory systems must be able to extract features of a stimulus to detect and represent properties of the world. Because sensory signals are constantly changing, a critical aspect of this transformation relates to the timing of signals and the ability to filter those signals to select dynamic properties, such as visual motion. At first assessment, one might think that the primary biophysical properties that construct a temporal filter would be dynamic mechanisms such as molecular concentration or membrane electrical properties. However, in the current issue of PLOS Biology, Baden et al. identify a mechanism of temporal filtering in the zebrafish and goldfish retina that is not dynamic but is in fact a structural building block-the physical size of a synapse itself. The authors observe that small, bipolar cell synaptic terminals are fast and highly adaptive, whereas large ones are slower and adapt less. Using a computational model, they conclude that the volume of the synaptic terminal influences the calcium concentration and the number of available vesicles. These results indicate that the size of the presynaptic terminal is an independent control for the dynamics of a synapse and may reveal aspects of synaptic function that can be inferred from anatomical structure.

  View details for DOI 10.1371/journal.pbio.1001973

  View details for PubMedID 25333721

  View details for PubMedCentralID PMC4205117

• Insights from the retina into the diverse and general computations of adaptation, detection, and prediction CURRENT OPINION IN NEUROBIOLOGY Kastner, D. B., Baccus, S. A. 2014; 25: 63-69

  Abstract

  The retina performs a diverse set of complex, nonlinear, computations, beyond the simple linear photoreceptor weighting assumed in the classical understanding of ganglion cell receptive fields. Here we attempt to organize these computations and extract rules that correspond to three distinct goals of early sensory systems. First, the retina acts efficiently to transmit information to the higher brain for further processing. We observe that although the retina adapts to a number of complex statistics, many of these may be explained by local adaptation to the mean signal strength at that stage. Second, ganglion cells signal the detection of a diverse set of features. Recent results indicate that feature selectivity arises through the action of specific inhibition, rather than through the convergence of excitation as in classical cortical models. Finally, the retina conveys predictions about the stimulus, a function usually attributed only to the higher brain. We expect that computational and mechanistic rules associated with these classes of functions will be an important guide in the study of other neural circuits.

  View details for DOI 10.1016/j.conb.2013.11.012

  View details for Web of Science ID 000335628800013

  View details for PubMedID 24709602

• Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells. Neuron Kastner, D. B., Baccus, S. A. 2013; 79 (3): 541-554

  Abstract

  Sensory systems change their sensitivity based on recent stimuli to adjust their response range to the range of inputs and to predict future sensory input. Here, we report the presence of retinal ganglion cells that have antagonistic plasticity, showing central adaptation and peripheral sensitization. Ganglion cell responses were captured by a spatiotemporal model with independently adapting excitatory and inhibitory subunits, and sensitization requires GABAergic inhibition. Using a simple theory of signal detection, we show that the sensitizing surround conforms to an optimal inference model that continually updates the prior signal probability. This indicates that small receptive field regions have dual functionality-to adapt to the local range of signals but sensitize based upon the probability of the presence of that signal. Within this framework, we show that sensitization predicts the location of a nearby object, revealing prediction as a functional role for adapting inhibition in the nervous system.

  View details for DOI 10.1016/j.neuron.2013.06.011

  View details for PubMedID 23932000

• Transformation of visual signals by inhibitory interneurons in retinal circuits. Annual review of neuroscience Jadzinsky, P. D., Baccus, S. A. 2013; 36: 403-428

  Abstract

  One of the largest mysteries of the brain lies in understanding how higher-level computations are implemented by lower-level operations in neurons and synapses. In particular, in many brain regions inhibitory interneurons represent a diverse class of cells, the individual functional roles of which are unknown. We discuss here how the operations of inhibitory interneurons influence the behavior of a circuit, focusing on recent results in the vertebrate retina. A key role in this understanding is played by a common representation of the visual stimulus that can be applied at different stages. By considering how this stimulus representation changes at each location in the circuit, we can understand how neuron-level operations such as thresholds and inhibition yield circuit-level computations such as how stimulus selectivity and gain are controlled by local and peripheral visual stimuli. Expected final online publication date for the Annual Review of Neuroscience Volume 36 is July 08, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

  View details for DOI 10.1146/annurev-neuro-062012-170315

  View details for PubMedID 23724996

• Precise Neural Stimulation in the Retina Using Focused Ultrasound JOURNAL OF NEUROSCIENCE Menz, M. D., Oralkan, O., Khuri-Yakub, P. T., Baccus, S. A. 2013; 33 (10): 4550-?

  Abstract

  Focused ultrasound is a promising noninvasive technology for neural stimulation. Here we use the isolated salamander retina to characterize the effect of ultrasound on an intact neural circuit and compared these effects with those of visual stimulation of the same retinal ganglion cells. Ultrasound stimuli at an acoustic frequency of 43 MHz and a focal spot diameter of 90 μm delivered from a piezoelectric transducer evoked stable responses with a temporal precision equal to strong visual responses but with shorter latency. By presenting ultrasound and visual stimulation together, we found that ultrasonic stimulation rapidly modulated visual sensitivity but did not change visual temporal filtering. By combining pharmacology with ultrasound stimulation, we found that ultrasound did not directly activate retinal ganglion cells but did in part activate interneurons beyond photoreceptors. These results suggest that, under conditions of strong localized stimulation, timing variability is largely influenced by cells beyond photoreceptors. We conclude that ultrasonic stimulation is an effective and spatiotemporally precise method to activate the retina. Because the retina is the most accessible part of the CNS in vivo, ultrasonic stimulation may have diagnostic potential to probe remaining retinal function in cases of photoreceptor degeneration, and therapeutic potential for use in a retinal prosthesis. In addition, because of its noninvasive properties and spatiotemporal resolution, ultrasound neurostimulation promises to be a useful tool to understand dynamic activity in pharmacologically defined neural pathways in the retina.

  View details for DOI 10.1523/JNEUROSCI.3521-12.2013

  View details for Web of Science ID 000315926300033

  View details for PubMedID 23467371

• Linking the Computational Structure of Variance Adaptation to Biophysical Mechanisms NEURON Ozuysal, Y., Baccus, S. A. 2012; 73 (5): 1002-1015

  Abstract

  In multiple sensory systems, adaptation to the variance of a sensory input changes the sensitivity, kinetics, and average response over timescales ranging from < 100 ms to tens of seconds. Here, we present a simple, biophysically relevant model of retinal contrast adaptation that accurately captures both the membrane potential response and all adaptive properties. The adaptive component of this model is a first-order kinetic process of the type used to describe ion channel gating and synaptic transmission. From the model, we conclude that all adaptive dynamics can be accounted for by depletion of a signaling mechanism, and that variance adaptation can be explained as adaptation to the mean of a rectified signal. The model parameters show strong similarity to known properties of bipolar cell synaptic vesicle pools. Diverse types of adaptive properties that implement theoretical principles of efficient coding can be generated by a single type of molecule or synapse with just a few microscopic states.

  View details for DOI 10.1016/j.neuron.2011.12.029

  View details for Web of Science ID 000301558600014

  View details for PubMedID 22405209

• Disinhibitory gating of retinal output by transmission from an amacrine cell PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Manu, M., Baccus, S. A. 2011; 108 (45): 18447-18452

  Abstract

  Inhibitory interneurons help transform the input of a neural circuit into its output. Such interneurons are diverse, and most have unknown function. To study the function of single amacrine cells in the intact salamander retina, we recorded extracellularly from a population of ganglion cells with a multielectrode array, while simultaneously recording from or injecting current into single Off-type amacrine cells that had linear responses. We measured how visual responses of the amacrine cell interacted both with other visual input to the ganglion cell and with transmission between the two cells. We found that on average, visual responses from Off-type amacrine cells inhibited nearby Off-type ganglion cells. By recording and playing back the light-driven membrane potential fluctuations of amacrine cells during white noise visual stimuli, we found that paradoxically, increasing the light-driven modulations of inhibitory amacrine cells increased the firing rate of nearby Off-type ganglion cells. By measuring the correlations and transmission between amacrine and ganglion cells, we found that, on average, the amacrine cell hyperpolarizes before the ganglion cell fires, generating timed disinhibition just before the ganglion cell spikes. In addition, we found that amacrine to ganglion cell transmission is nonlinear in that increases in ganglion cell activity produced by amacrine hyperpolarization were greater than decreases in activity produced by amacrine depolarization. We conclude that the primary mode of action of this class of amacrine cell is to actively gate the ganglion cell response by a timed release from inhibition.

  View details for DOI 10.1073/pnas.1107994108

  View details for Web of Science ID 000296700000059

  View details for PubMedID 22031693

  View details for PubMedCentralID PMC3215053

• Coordinated dynamic encoding in the retina using opposing forms of plasticity NATURE NEUROSCIENCE Kastner, D. B., Baccus, S. A. 2011; 14 (10): 1317-U135

  Abstract

  The range of natural inputs encoded by a neuron often exceeds its dynamic range. To overcome this limitation, neural populations divide their inputs among different cell classes, as with rod and cone photoreceptors, and adapt by shifting their dynamic range. We report that the dynamic behavior of retinal ganglion cells in salamanders, mice and rabbits is divided into two opposing forms of short-term plasticity in different cell classes. One population of cells exhibited sensitization-a persistent elevated sensitivity following a strong stimulus. This newly observed dynamic behavior compensates for the information loss caused by the known process of adaptation occurring in a separate cell population. The two populations divide the dynamic range of inputs, with sensitizing cells encoding weak signals and adapting cells encoding strong signals. In the two populations, the linear, threshold and adaptive properties are linked to preserve responsiveness when stimulus statistics change, with one population maintaining the ability to respond when the other fails.

  View details for DOI 10.1038/nn.2906

  View details for Web of Science ID 000295254200021

  View details for PubMedID 21909086

  View details for PubMedCentralID PMC3359137

• Architecture and activity-mediated refinement of axonal projections from a mosaic of genetically identified retinal ganglion cells NEURON Huberman, A. D., Manu, M., Koch, S. M., Susman, M. W., Lutz, A. B., Ullian, E. M., Baccus, S. A., Barres, B. A. 2008; 59 (3): 425-438

  Abstract

  Our understanding of how mammalian sensory circuits are organized and develop has long been hindered by the lack of genetic markers of neurons with discrete functions. Here, we report a transgenic mouse selectively expressing GFP in a complete mosaic of transient OFF-alpha retinal ganglion cells (tOFF-alphaRGCs). This enabled us to relate the mosaic spacing, dendritic anatomy, and electrophysiology of these RGCs to their complete map of projections in the brain. We find that tOFF-alphaRGCs project exclusively to the superior colliculus (SC) and dorsal lateral geniculate nucleus and are restricted to a specific laminar depth within each of these targets. The axons of tOFF-alphaRGC are also organized into columns in the SC. Both laminar and columnar specificity develop through axon refinement. Disruption of cholinergic retinal waves prevents the emergence of columnar- but not laminar-specific tOFF-alphaRGC connections. Our findings reveal that in a genetically identified sensory map, spontaneous activity promotes synaptic specificity by segregating axons arising from RGCs of the same subtype.

  View details for DOI 10.1016/j.neuron.2008.07.018

  View details for Web of Science ID 000258565500011

  View details for PubMedID 18701068

• A retinal circuit that computes object motion JOURNAL OF NEUROSCIENCE Baccus, S. A., Oelveczky, B. P., Manu, M., Meister, M. 2008; 28 (27): 6807-6817

  Abstract

  Certain ganglion cells in the retina respond sensitively to differential motion between the receptive field center and surround, as produced by an object moving over the background, but are strongly suppressed by global image motion, as produced by the observer's head or eye movements. We investigated the circuit basis for this object motion sensitive (OMS) response by recording intracellularly from all classes of retinal interneurons while simultaneously recording the spiking output of many ganglion cells. Fast, transient bipolar cells respond linearly to motion in the receptive field center. The synaptic output from their terminals is rectified and then pooled by the OMS ganglion cell. A type of polyaxonal amacrine cell is driven by motion in the surround, again via pooling of rectified inputs, but from a different set of bipolar cell terminals. By direct intracellular current injection, we found that these polyaxonal amacrine cells selectively suppress the synaptic input of OMS ganglion cells. A quantitative model of these circuit elements and their interactions explains how an important visual computation is accomplished by retinal neurons and synapses.

  View details for DOI 10.1523/JNEUROSCI.4206-07.2008

  View details for Web of Science ID 000257418300006

  View details for PubMedID 18596156

• Retinal adaptation to object motion NEURON Oelveczky, B. P., Baccus, S. A., Meister, M. 2007; 56 (4): 689-700

  Abstract

  Due to fixational eye movements, the image on the retina is always in motion, even when one views a stationary scene. When an object moves within the scene, the corresponding patch of retina experiences a different motion trajectory than the surrounding region. Certain retinal ganglion cells respond selectively to this condition, when the motion in the cell's receptive field center is different from that in the surround. Here we show that this response is strongest at the very onset of differential motion, followed by gradual adaptation with a time course of several seconds. Different subregions of a ganglion cell's receptive field can adapt independently. The circuitry responsible for differential motion adaptation lies in the inner retina. Several candidate mechanisms were tested, and the adaptation most likely results from synaptic depression at the synapse from bipolar to ganglion cell. Similar circuit mechanisms may act more generally to emphasize novel features of a visual stimulus.

  View details for Web of Science ID 000251306600011

  View details for PubMedID 18031685

• Image processing for a high-resolution optoelectronic retinal prosthesis IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Asher, A., Segal, W. A., Baccus, S. A., Yaroslavsky, L. P., Palanker, D. V. 2007; 54 (6): 993-1004

  Abstract

  In an effort to restore visual perception in retinal diseases such as age-related macular degeneration or retinitis pigmentosa, a design was recently presented for a high-resolution optoelectronic retinal prosthesis having thousands of electrodes. This system requires real-time image processing fast enough to convert a video stream of images into electrical stimulus patterns that can be properly interpreted by the brain. Here, we present image-processing and tracking algorithms for a subretinal implant designed to stimulate the second neuron in the visual pathway, bypassing the degenerated first synaptic layer. For this task, we have developed and implemented: 1) A tracking algorithm that determines the implant's position in each frame. 2) Image cropping outside of the implant boundaries. 3) A geometrical transformation that distorts the image appropriate to the geometry of the fovea. 4) Spatio-temporal image filtering to reproduce the visual processing normally occurring in photoceptors and at the photoreceptor-bipolar cell synapse. 5) Conversion of the filtered visual information into a pattern of electrical current. Methods to accelerate real-time transformations include the exploitation of data redundancy in the time domain, and the use of precomputed lookup tables that are adjustable to retinal physiology and allow flexible control of stimulation parameters. A software implementation of these algorithms processes natural visual scenes with sufficient speed for real-time operation. This computationally efficient algorithm resembles, in some aspects, biological strategies of efficient coding in the retina and could provide a refresh rate higher than fifty frames per second on our system.

  View details for DOI 10.1109/TBME.2007.894828

  View details for Web of Science ID 000246821500005

  View details for PubMedID 17554819

• Timing and computation in inner retinal circuitry ANNUAL REVIEW OF PHYSIOLOGY Baccus, S. A. 2007; 69: 271-290

  Abstract

  In the vertebrate inner retina, the second stage of the visual system, different components of the visual scene are transformed, discarded, or selected before visual information is transmitted through the optic nerve. This review discusses the connections between higher-level functions of visual processing, mathematical descriptions of the neural code, inner retinal circuitry, and visual computations. In the inner plexiform layer, bipolar cells deliver spatially and temporally filtered input to approximately ten anatomical strata. These layers receive a unique combination of excitation and inhibition, causing cells in different layers to respond with different kinetics to visual input. These distinct temporal channels interact through amacrine cells, a diverse class of inhibitory interneurons, which transmit signals within and between layers. In particular, wide-field amacrine cells transmit transient inhibition over long distances within a layer. These mechanisms and properties are combined into computations to detect the presence of differential motion and suppress the visual effects of eye movements.

  View details for DOI 10.1146/annurev.physiol.69.120205.124451

  View details for Web of Science ID 000245334100015

  View details for PubMedID 17059359

• From a whisper to a roar: Adaptation to the mean and variance of naturalistic sounds NEURON Baccus, S. A. 2006; 51 (6): 682-684

  Abstract

  In this issue of Neuron, Nagel and Doupe make a quantitative assessment of temporal adaptation in the avian auditory forebrain, capturing seemingly complex responses with a simple linear-nonlinear (LN) model of kinetics and gain. A comparison of these findings with similar results in the early visual system shows an important unifying picture of efficient sensory processing and adaptation.

  View details for DOI 10.1016/j.neuron.2006.09.007

  View details for Web of Science ID 000240997900007

  View details for PubMedID 16982414

• Dynamic predictive coding by the retina NATURE Hosoya, T., Baccus, S. A., Meister, M. 2005; 436 (7047): 71-77

  Abstract

  Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.

  View details for DOI 10.1038/nature03689

  View details for Web of Science ID 000230296600041

  View details for PubMedID 16001064

• Design of a high-resolution optoelectronic retinal prosthesis. Journal of neural engineering Palanker, D., Vankov, A., Huie, P., Baccus, S. 2005; 2 (1): S105-20

  Abstract

  It has been demonstrated that electrical stimulation of the retina can produce visual percepts in blind patients suffering from macular degeneration and retinitis pigmentosa. However, current retinal implants provide very low resolution (just a few electrodes), whereas at least several thousand pixels would be required for functional restoration of sight. This paper presents the design of an optoelectronic retinal prosthetic system with a stimulating pixel density of up to 2500 pix mm(-2) (corresponding geometrically to a maximum visual acuity of 20/80). Requirements on proximity of neural cells to the stimulation electrodes are described as a function of the desired resolution. Two basic geometries of sub-retinal implants providing required proximity are presented: perforated membranes and protruding electrode arrays. To provide for natural eye scanning of the scene, rather than scanning with a head-mounted camera, the system operates similar to 'virtual reality' devices. An image from a video camera is projected by a goggle-mounted collimated infrared LED-LCD display onto the retina, activating an array of powered photodiodes in the retinal implant. The goggles are transparent to visible light, thus allowing for the simultaneous use of remaining natural vision along with prosthetic stimulation. Optical delivery of visual information to the implant allows for real-time image processing adjustable to retinal architecture, as well as flexible control of image processing algorithms and stimulation parameters.

  View details for PubMedID 15876646

• Design of a high-resolution optoelectronic retinal prosthesis JOURNAL OF NEURAL ENGINEERING Palanker, D., Vankov, A., Huie, P., Baccus, S. 2005; 2 (1): S105-S120

  View details for DOI 10.1088/1741-2560/2/1/012

  View details for Web of Science ID 000209672200012

• Towards high-resolution optoelectronic retinal prosthesis 15th Conference on Ophthalmic Technologies Palanker, D., Huie, P., Vankov, A., Asher, A., Baccus, S. SPIE-INT SOC OPTICAL ENGINEERING. 2005: 223–233

  View details for DOI 10.1117/12.590964

  View details for Web of Science ID 000229038000028

• Retina versus cortex: Contrast adaptation in parallel visual pathways NEURON Baccus, S. A., Meister, M. 2004; 42 (1): 5-7

  Abstract

  Human vision adapts to the contrast of patterns by changing its sensitivity, but the origins of this perceptual adaptation have been disputed. In this issue of Neuron, Solomon et al. show that contrast adaptation in the primate arises mostly in the retina for the magnocellular pathway and mostly in the cortex for the parvocellular pathway. It appears that adaptation arises most strongly at sites that pool over many inputs.

  View details for Web of Science ID 000221458400003

  View details for PubMedID 15066260

• Segregation of object and background motion in the retina NATURE Olveczky, B. P., Baccus, S. A., Meister, M. 2003; 423 (6938): 401-408

  Abstract

  An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects.

  View details for DOI 10.1038/nature01652

  View details for Web of Science ID 000183012000033

  View details for PubMedID 12754524

• Fast and slow contrast adaptation in retinal circuitry NEURON Baccus, S. A., Meister, M. 2002; 36 (5): 909-919

  Abstract

  The visual system adapts to the magnitude of intensity fluctuations, and this process begins in the retina. Following the switch from a low-contrast environment to one of high contrast, ganglion cell sensitivity declines in two distinct phases: a fast change occurs in <0.1 s, and a slow decrease over approximately 10 s. To examine where these modulations arise, we recorded intracellularly from every major cell type in the salamander retina. Certain bipolar and amacrine cells, and all ganglion cells, adapted to contrast. Generally, these neurons showed both fast and slow adaptation. Fast effects of a contrast increase included accelerated kinetics, decreased sensitivity, and a depolarization of the baseline membrane potential. Slow adaptation did not affect kinetics, but produced a gradual hyperpolarization. This hyperpolarization can account for slow adaptation in the spiking output of ganglion cells.

  View details for Web of Science ID 000179667800015

  View details for PubMedID 12467594