@article {68938, title = {Learning produces an orthogonalized state machine in the hippocampus.}, journal = {Nature}, volume = {640}, year = {2025}, month = {2025 February 12}, abstract = {

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

}, doi = {10.1038/s41586-024-08548-w}, url = {https://doi.org/10.1038/s41586-024-08548-w}, author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson} } @article {70183, title = {Stability through plasticity: Finding robust memories through representational drift.}, journal = {Proc Natl Acad Sci U S A}, volume = {122}, year = {2025}, month = {2025 Nov 11}, pages = {e2500077122}, abstract = {

Memories are believed to be stored in synapses and retrieved by reactivating neural ensembles. Learning alters synaptic weights, which can interfere with previously stored memories that share the same synapses, creating a trade-off between plasticity and stability. Interestingly, neural representations change even in stable environments, without apparent learning or forgetting-a phenomenon known as representational drift. Theoretical studies have suggested that multiple neural representations can correspond to a memory, with postlearning exploration of these representation solutions driving drift. However, it remains unclear whether representations explored through drift differ from those learned or offer unique advantages. Here, we show that representational drift uncovers noise-robust representations that are otherwise difficult to learn. We first define the nonlinear solution space manifold of synaptic weights for fixed input-output mappings, which allows us to disentangle drift from learning and forgetting and simulate drift as diffusion within this manifold. Solutions explored by drift have many inactive and saturated neurons, making them robust to weight perturbations due to noise or continual learning. Such solutions are prevalent and entropically favored by drift, but their lack of gradients makes them difficult to learn and nonconducive to future learning. To overcome this, we introduce an allocation procedure that selectively shifts representations for new stimuli into a learning-conducive regime. By combining allocation with drift, we resolve the trade-off between learnability and robustness.

}, keywords = {Humans, Learning, Memory, Models, Neurological, Neuronal Plasticity, Neurons, Synapses}, issn = {1091-6490}, doi = {10.1073/pnas.2500077122}, url = {https://doi.org/10.1073/pnas.2500077122}, author = {Natrajan, Maanasa and Fitzgerald, James E} } @article {70081, title = {Supraspinal commands have a modular organization that is behavioral context specific.}, journal = {Curr Biol}, year = {2025}, month = {2025 Aug 21}, abstract = {

Animals generate a range of locomotor patterns that subserve diverse behaviors, and in vertebrates, the required supraspinal commands derive from reticulospinal neurons in the brainstem. Yet how these commands are encoded across the reticulospinal population is unknown, making it unclear whether a universal control logic generates the full locomotor repertoire or if distinct sets of command modules might compose movement in different behavioral contexts. Here, we used calcium imaging, high-resolution behavior tracking, and statistical modeling to comprehensively survey reticulospinal activity and relate single-cell activity to movement kinematics as larval zebrafish generated a broad diversity of swim types. We found that reticulospinal population activity had a low-dimensional organization and identified 8 functional archetypes that provided a succinct and robust encoding of the full range of locomotor actions. Across much of locomotor space, 5 functional archetypes supported multiplexed control of swim speed and independent control of direction, whereas an independent set of 3 functional archetypes controlled the specialized swims that zebrafish use during hunting to orient toward prey. Overall, our study reveals a modular supraspinal control architecture that is partitioned according to behavioral context.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2025.07.066}, author = {Lau, Joanna Y N and Fitzgerald, James E and Bianco, Isaac H} } @article {69399, title = {Optimization in Visual Motion Estimation.}, journal = {Annu Rev Vis Sci}, year = {2024}, month = {2024 Apr 25}, abstract = {

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

}, issn = {2374-4650}, doi = {10.1146/annurev-vision-101623-025432}, author = {Clark, Damon A and Fitzgerald, James E} } @article {65808, title = {A geometric framework to predict structure from function in neural networks}, journal = {Physical Review Research}, volume = {4}, year = {2022}, month = {2022 Jun 29}, pages = {023255}, abstract = {

Neural computation in biological and artificial networks relies on nonlinear synaptic integration. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of rectified-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. We then use this analytical characterization to rigorously analyze the solution space geometry and derive certainty conditions guaranteeing a non-zero synapse between neurons. Numerical simulations of feedforward and recurrent networks verify our analytical results. Our theoretical framework could be applied to neural activity data to make anatomical predictions that follow generally from the model architecture. It thus provides novel opportunities for discerning what model features are required to accurately relate neural network structure and function.

}, doi = {10.1103/PhysRevResearch.4.023255}, url = {https://doi.org/10.1103/PhysRevResearch.4.023255}, author = {Biswas, Tirthabir and Fitzgerald, James E} } @article {65372, title = {Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Mar 24}, abstract = {

Breakthrough technologies for monitoring and manipulating single-neuron activity provide unprecedented opportunities for whole-brain neuroscience in larval zebrafish1{\textendash}9. Understanding the neural mechanisms of visually guided behavior also requires precise stimulus control, but little prior research has accounted for physical distortions that result from refraction and reflection at an air-water interface that usually separates the projected stimulus from the fish10{\textendash}12. Here we provide a computational tool that transforms between projected and received stimuli in order to detect and control these distortions. The tool considers the most commonly encountered interface geometry, and we show that this and other common configurations produce stereotyped distortions. By correcting these distortions, we reduced discrepancies in the literature concerning stimuli that evoke escape behavior13,14, and we expect this tool will help reconcile other confusing aspects of the literature. This tool also aids experimental design, and we illustrate the dangers that uncorrected stimuli pose to receptive field mapping experiments.

}, doi = {10.7554/eLife.53684}, url = {https://elifesciences.org/articles/53684https://cdn.elifesciences.org/articles/53684/elife-53684-v1.pdfhttps://cdn.elifesciences.org/articles/53684/elife-53684-v1.xml}, author = {Dunn, Timothy W and Fitzgerald, James E} } @article {65598, title = {A neural representation of naturalistic motion-guided behavior in the zebrafish brain.}, journal = {Current Biology}, volume = {30}, year = {2020}, month = {2020 Jun 22}, pages = {2321-33}, abstract = {

All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2020.04.043}, author = {Yildizoglu, Tugce and Riegler, Clemens and Fitzgerald, James E and Portugues, Ruben} } @article {65856, title = {Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Nov 25}, pages = {138-145}, abstract = {

Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.021}, author = {Biswas, Tirthabir and Bishop, William E and Fitzgerald, James E} } @article {65180, title = {Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Oct 15}, abstract = {

Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world{\textquoteright}s light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

}, issn = {2050-084X}, doi = {10.7554/eLife.47579}, author = {Chen, Juyue and Mandel, Holly B and Fitzgerald, James E and Clark, Damon A} } @article {56201, title = {Threshold-based ordering of sequential actions during Drosophila courtship.}, journal = {Current Biology : CB}, volume = {29}, year = {2019}, month = {2019 Jan 07}, pages = {426-34}, abstract = {

Goal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2018.12.019}, author = {McKellar, Claire E and Lillvis, Joshua L and Bath, Daniel E and Fitzgerald, James E and Cannon, John G and Simpson, Julie H and Dickson, Barry J} } @article {49473, title = {Long-Term Consolidation of Ensemble Neural Plasticity Patterns in Hippocampal Area CA1.}, journal = {Cell reports}, volume = {25}, year = {2018}, month = {2018 Oct 16}, pages = {640-650.e2}, abstract = {

Neural network remodeling underpins the ability to remember life experiences, but little is known about the long-term plasticity of neural populations. To study how the brain encodes episodic events, we used time-lapse two-photon microscopy and a fluorescent reporter of neural plasticity based on an enhanced form of the synaptic activity-responsive element (E-SARE) within the Arc promoter to track thousands of CA1 hippocampal pyramidal cells over weeks in mice that repeatedly encountered different environments. Each environment evokes characteristic patterns of ensemble neural plasticity, but with each encounter, the set of activated cells gradually evolves. After repeated exposures, the plasticity patterns evoked by an individual environment progressively stabilize. Compared with young adults, plasticity patterns in aged mice are less specific to individual environments and less stable across repeat experiences. Long-term consolidation of hippocampal plasticity patterns may support long-term memory formation, whereas weaker consolidation in aged subjects might reflect declining memory function.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.09.064}, author = {Attardo, Alessio and Lu, Ju and Kawashima, Takashi and Okuno, Hiroyuki and Fitzgerald, James E and Bito, Haruhiko and Schnitzer, Mark J} } @article {49528, title = {The neuronal basis of an illusory motion percept is explained by decorrelation of parallel motion pathways.}, journal = {Current Biology : CB}, volume = {28}, year = {2018}, month = {2018 Oct 31}, pages = {3748-78}, abstract = {

Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila{\textquoteright}s elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2018.10.007}, author = {Salazar-Gatzimas, Emilio and Agrochao, Margarida and Fitzgerald, James E and Clark, Damon A} } @article {48386, title = {From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.}, journal = {Cell}, volume = {167}, year = {2016}, month = {2016 11 03}, pages = {947-960.e20}, abstract = {

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit\ model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

}, keywords = {Animals, Brain, Feedback, Sensory, Neural Pathways, Neuroimaging, Neurons, Swimming, Visual Perception, Zebrafish}, issn = {1097-4172}, doi = {10.1016/j.cell.2016.10.019}, author = {Naumann, Eva A and Fitzgerald, James E and Dunn, Timothy W and Rihel, Jason and Sompolinsky, Haim and Engert, Florian} } @article {48389, title = {Impermanence of dendritic spines in live adult CA1 hippocampus.}, journal = {Nature}, volume = {523}, year = {2015}, month = {2015 Jul 30}, pages = {592-6}, abstract = {

The mammalian hippocampus is crucial for episodic memory formation and transiently retains information for about 3-4 weeks in adult mice and longer in humans. Although neuroscientists widely believe that neural synapses are elemental sites of information storage, there has been no direct evidence that hippocampal synapses persist for time intervals commensurate with the duration of hippocampal-dependent memory. Here we tested the prediction that the lifetimes of hippocampal synapses match the longevity of hippocampal memory. By using time-lapse two-photon microendoscopy in the CA1 hippocampal area of live mice, we monitored the turnover dynamics of the pyramidal neurons{\textquoteright} basal dendritic spines, postsynaptic structures whose turnover dynamics are thought to reflect those of excitatory synaptic connections. Strikingly, CA1 spine turnover dynamics differed sharply from those seen previously in the neocortex. Mathematical modelling revealed that the data best matched kinetic models with a single population of spines with a mean lifetime of approximately 1-2 weeks. This implies \~{}100\% turnover in \~{}2-3 times this interval, a near full erasure of the synaptic connectivity pattern. Although N-methyl-d-aspartate (NMDA) receptor blockade stabilizes spines in the neocortex, in CA1 it transiently increased the rate of spine loss and thus lowered spine density. These results reveal that adult neocortical and hippocampal pyramidal neurons have divergent patterns of spine regulation and quantitatively support the idea that the transience of hippocampal-dependent memory directly reflects the turnover dynamics of hippocampal synapses.

}, keywords = {Animals, CA1 Region, Hippocampal, Dendritic Spines, Endoscopy, Kinetics, Male, Memory, Episodic, Mice, Neocortex, Neuronal Plasticity, Photons, Pyramidal Cells, Receptors, N-Methyl-D-Aspartate, Synapses, Time Factors}, issn = {1476-4687}, doi = {10.1038/nature14467}, author = {Attardo, Alessio and Fitzgerald, James E and Schnitzer, Mark J} } @article {48387, title = {Nonlinear circuits for naturalistic visual motion estimation.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Oct 24}, pages = {e09123}, abstract = {

Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila{\textquoteright}s visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly{\textquoteright}s motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

}, keywords = {Animals, Drosophila, Locomotion, Models, Neurological, Motion Perception, Neurons, Visual Pathways}, issn = {2050-084X}, doi = {10.7554/eLife.09123}, author = {Fitzgerald, James E and Clark, Damon A} } @article {48388, title = {Whole-brain activity mapping onto a zebrafish brain atlas.}, journal = {Nature methods}, volume = {12}, year = {2015}, month = {2015 Nov}, pages = {1039-46}, abstract = {

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal{\textendash}regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

}, keywords = {Algorithms, Animals, Automation, Behavior, Animal, Brain, Brain Mapping, Calcium, Extracellular Signal-Regulated MAP Kinases, Image Processing, Computer-Assisted, Immunohistochemistry, Microscopy, Confocal, Neurites, Neurons, Phosphorylation, Principal Component Analysis, Reproducibility of Results, Software, Swimming, Zebrafish}, issn = {1548-7105}, doi = {10.1038/nmeth.3581}, author = {Randlett, Owen and Wee, Caroline L and Naumann, Eva A and Nnaemeka, Onyeka and Schoppik, David and Fitzgerald, James E and Portugues, Ruben and Lacoste, Alix M B and Riegler, Clemens and Engert, Florian and Schier, Alexander F} } @article {48390, title = {Flies and humans share a motion estimation strategy that exploits natural scene statistics.}, journal = {Nature neuroscience}, volume = {17}, year = {2014}, month = {2014 Feb}, pages = {296-303}, abstract = {

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.

}, keywords = {Adaptation, Physiological, Animals, Contrast Sensitivity, Drosophila, Electroencephalography, Evoked Potentials, Visual, Genotype, Humans, Models, Neurological, Motion Perception, Pattern Recognition, Visual, Photic Stimulation, Psychophysics}, issn = {1546-1726}, doi = {10.1038/nn.3600}, author = {Clark, Damon A and Fitzgerald, James E and Ales, Justin M and Gohl, Daryl M and Silies, Marion A and Norcia, Anthony M and Clandinin, Thomas R} } @article {48391, title = {Photon shot noise limits on optical detection of neuronal spikes and estimation of spike timing.}, journal = {Biophysical journal}, volume = {104}, year = {2013}, month = {2013 Jan 08}, pages = {51-62}, abstract = {

Optical approaches for tracking neural dynamics are of widespread interest, but a theoretical framework quantifying the physical limits of these techniques has been lacking. We formulate such a framework by using signal detection and estimation theory to obtain physical bounds on the detection of neural spikes and the estimation of their occurrence times as set by photon counting statistics (shot noise). These bounds are succinctly expressed via a discriminability index that depends on the kinetics of the optical indicator and the relative fluxes of signal and background photons. This approach facilitates quantitative evaluations of different indicators, detector technologies, and data analyses. Our treatment also provides optimal filtering techniques for optical detection of spikes. We compare various types of Ca(2+) indicators and show that background photons are a chief impediment to voltage sensing. Thus, voltage indicators that change color in response to membrane depolarization may offer a key advantage over those that change intensity. We also examine fluorescence resonance energy transfer indicators and identify the regimes in which the widely used ratiometric analysis of signals is substantially suboptimal. Overall, by showing how different optical factors interact to affect signal quality, our treatment offers a valuable guide to experimental design and provides measures of confidence to assess optically extracted traces of neural activity.

}, keywords = {Action Potentials, Animals, Computer Simulation, Fluorescence Resonance Energy Transfer, Neurons, Optical Imaging, Photons, Signal Processing, Computer-Assisted, Time Factors}, issn = {1542-0086}, doi = {10.1016/j.bpj.2012.07.058}, author = {Wilt, Brian A and Fitzgerald, James E and Schnitzer, Mark J} } @article {48392, title = {Estimation theoretic measure of resolution for stochastic localization microscopy.}, journal = {Physical review letters}, volume = {109}, year = {2012}, month = {2012 Jul 27}, pages = {048102}, abstract = {

One approach to super-resolution fluorescence microscopy, termed stochastic localization microscopy, relies on the nanometer scale spatial localization of individual fluorescent emitters that stochastically label specific features of the specimen. The precision of emitter localization is an important determinant of the resulting image resolution but is insufficient to specify how well the derived images capture the structure of the specimen. We address this deficiency by considering the inference of specimen structure based on the estimated emitter locations. By using estimation theory, we develop a measure of spatial resolution that jointly depends on the density of the emitter labels, the precision of emitter localization, and prior information regarding the spatial frequency content of the labeled object. The Nyquist criterion does not set the scaling of this measure with emitter number. Given prior information and a fixed emitter labeling density, our resolution measure asymptotes to a finite value as the precision of emitter localization improves. By considering the present experimental capabilities, this asymptotic behavior implies that further resolution improvements require increases in labeling density above typical current values. Our treatment also yields algorithms to enhance reliable image features. Overall, our formalism facilitates the rigorous statistical interpretation of the data produced by stochastic localization imaging techniques.

}, keywords = {Microscopy, Fluorescence, Models, Theoretical, Stochastic Processes}, issn = {1079-7114}, doi = {10.1103/PhysRevLett.109.048102}, author = {Fitzgerald, James E and Lu, Ju and Schnitzer, Mark J} } @article {48393, title = {Symmetries in stimulus statistics shape the form of visual motion estimators.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {108}, year = {2011}, month = {2011 Aug 02}, pages = {12909-14}, abstract = {

The estimation of visual motion has long been studied as a paradigmatic neural computation, and multiple models have been advanced to explain behavioral and neural responses to motion signals. A broad class of models, originating with the Reichardt correlator model, proposes that animals estimate motion by computing a temporal cross-correlation of light intensities from two neighboring points in visual space. These models provide a good description of experimental data in specific contexts but cannot explain motion percepts in stimuli lacking pairwise correlations. Here, we develop a theoretical formalism that can accommodate diverse stimuli and behavioral goals. To achieve this, we treat motion estimation as a problem of Bayesian inference. Pairwise models emerge as one component of the generalized strategy for motion estimation. However, correlation functions beyond second order enable more accurate motion estimation. Prior expectations that are asymmetric with respect to bright and dark contrast use correlations of both even and odd orders, and we show that psychophysical experiments using visual stimuli with symmetric probability distributions for contrast cannot reveal whether the subject uses odd-order correlators for motion estimation. This result highlights a gap in previous experiments, which have largely relied on symmetric contrast distributions. Our theoretical treatment provides a natural interpretation of many visual motion percepts, indicates that motion estimation should be revisited using a broader class of stimuli, demonstrates how correlation-based motion estimation is related to stimulus statistics, and provides multiple experimentally testable predictions.

}, keywords = {Algorithms, Animals, Computer Simulation, Models, Neurological, Motion, Motion Perception, Photic Stimulation, Time Factors}, issn = {1091-6490}, doi = {10.1073/pnas.1015680108}, author = {Fitzgerald, James E and Katsov, Alexander Y and Clandinin, Thomas R and Schnitzer, Mark J} } @article {48394, title = {Mimicking the folding pathway to improve homology-free protein structure prediction.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {106}, year = {2009}, month = {2009 Mar 10}, pages = {3734-9}, abstract = {

Since the demonstration that the sequence of a protein encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines, including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse-grained model without information concerning homology or explicit side chains can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (phi,psi) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.

}, keywords = {Algorithms, Amino Acid Sequence, Molecular Mimicry, Molecular Sequence Data, Protein Folding, Protein Structure, Secondary, Protein Structure, Tertiary, Proteins, Structural Homology, Protein}, issn = {1091-6490}, doi = {10.1073/pnas.0811363106}, author = {DeBartolo, Joe and Colubri, Andr{\'e}s and Jha, Abhishek K and Fitzgerald, James E and Freed, Karl F and Sosnick, Tobin R} } @article {48395, title = {Benchmarking implicit solvent folding simulations of the amyloid beta(10-35) fragment.}, journal = {The journal of physical chemistry. B}, volume = {112}, year = {2008}, month = {2008 May 15}, pages = {6175-86}, abstract = {

A pathogenetic feature of Alzhemier disease is the aggregation of monomeric beta-amyloid proteins (Abeta) to form oligomers. Usually these oligomers of long peptides aggregate on time scales of microseconds or longer, making computational studies using atomistic molecular dynamics models prohibitively expensive and making it essential to develop computational models that are cheaper and at the same time faithful to physical features of the process. We benchmark the ability of our implicit solvent model to describe equilibrium and dynamic properties of monomeric Abeta(10-35) using all-atom Langevin dynamics (LD) simulations, since Alphabeta(10-35) is the only fragment whose monomeric properties have been measured. The accuracy of the implicit solvent model is tested by comparing its predictions with experiment and with those from a new explicit water MD simulation, (performed using CHARMM and the TIP3P water model) which is approximately 200 times slower than the implicit water simulations. The dependence on force field is investigated by running multiple trajectories for Alphabeta(10-35) using the CHARMM, OPLS-aal, and GS-AMBER94 force fields, whereas the convergence to equilibrium is tested for each force field by beginning separate trajectories from the native NMR structure, a completely stretched structure, and from unfolded initial structures. The NMR order parameter, S2, is computed for each trajectory and is compared with experimental data to assess the best choice for treating aggregates of Alphabeta. The computed order parameters vary significantly with force field. Explicit and implicit solvent simulations using the CHARMM force fields display excellent agreement with each other and once again support the accuracy of the implicit solvent model. Alphabeta(10-35) exhibits great flexibility, consistent with experiment data for the monomer in solution, while maintaining a general strand-loop-strand motif with a solvent-exposed hydrophobic patch that is believed to be important for aggregation. Finally, equilibration of the peptide structure requires an implicit solvent LD simulation as long as 30 ns.

}, keywords = {Amyloid beta-Peptides, Computer Simulation, Diffusion, Magnetic Resonance Spectroscopy, Models, Molecular, Peptide Fragments, Protein Folding, Protein Structure, Tertiary, Solvents, Surface Properties, Water}, issn = {1520-6106}, doi = {10.1021/jp077099h}, author = {Kent, Andrew and Jha, Abhishek K and Fitzgerald, James E and Freed, Karl F} } @article {48397, title = {Polypeptide motions are dominated by peptide group oscillations resulting from dihedral angle correlations between nearest neighbors.}, journal = {Biochemistry}, volume = {46}, year = {2007}, month = {2007 Jan 23}, pages = {669-82}, abstract = {

To identify basic local backbone motions in unfolded chains, simulations are performed for a variety of peptide systems using three popular force fields and for implicit and explicit solvent models. A dominant "crankshaft-like" motion is found that involves only a localized oscillation of the plane of the peptide group. This motion results in a strong anticorrelated motion of the phi angle of the ith residue (phi(i)) and the psi angle of the residue i - 1 (psi(i-1)) on the 0.1 ps time scale. Only a slight correlation is found between the motions of the two backbone dihedral angles of the same residue. Aside from the special cases of glycine and proline, no correlations are found between backbone dihedral angles that are separated by more than one torsion angle. These short time, correlated motions are found both in equilibrium fluctuations and during the transit process between Ramachandran basins, e.g., from the beta to the alpha region. A residue{\textquoteright}s complete transit from one Ramachandran basin to another, however, occurs in a manner independent of its neighbors{\textquoteright} conformational transitions. These properties appear to be intrinsic because they are robust across different force fields, solvent models, nonbonded interaction routines, and most amino acids.

}, keywords = {Alanine, Computer Simulation, Glycine, Models, Molecular, Molecular Conformation, Motion, Peptides, Proline, Protein Folding}, issn = {0006-2960}, doi = {10.1021/bi061575x}, author = {Fitzgerald, James E and Jha, Abhishek K and Sosnick, Tobin R and Freed, Karl F} } @article {48396, title = {Reduced C(beta) statistical potentials can outperform all-atom potentials in decoy identification.}, journal = {Protein science : a publication of the Protein Society}, volume = {16}, year = {2007}, month = {2007 Oct}, pages = {2123-39}, abstract = {

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.

}, keywords = {Carbon, Computational Biology, Models, Statistical, Protein Folding, Protein Structure, Secondary, Proteins}, issn = {0961-8368}, doi = {10.1110/ps.072939707}, author = {Fitzgerald, James E and Jha, Abhishek K and Colubri, Andr{\'e}s and Sosnick, Tobin R and Freed, Karl F} }