The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Interpreting the retinal neural code for natural scenes: From computations to neurons.

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.

Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task.

Understanding the complexities of behavior is necessary to interpret neurophysiological data and establish animal models of neuropsychiatric disease. This understanding requires knowledge of the underlying information-processing structure-something often hidden from direct observation. Commonly, one assumes that behavior is solely governed by the experimenter-controlled rules that determine tasks. For example, differences in tasks that require memory of past actions are often interpreted as exclusively resulting from differences in memory. However, such assumptions are seldom tested. Here, we provide a comprehensive examination of multiple processes that contribute to behavior in a prevalent experimental paradigm. Using a combination of behavioral automation, hypothesis-driven trial design, and reinforcement learning modeling, we show that rats learn a spatial alternation task consistent with their drawing upon spatial preferences in addition to memory. Our approach also distinguishes learning based on established preferences from generalization of task structure, providing further insights into learning dynamics.

Inhibition, but not excitation, recovers from partial cone loss with greater spatiotemporal integration, synapse density, and frequency.

Neural circuits function in the face of changing inputs, either caused by normal variation in stimuli or by cell death. To maintain their ability to perform essential computations with partial inputs, neural circuits make modifications. Here, we study the retinal circuit's responses to changes in light stimuli or in photoreceptor inputs by inducing partial cone death in the mature mouse retina. Can the retina withstand or recover from input loss? We find that the excitatory pathways exhibit functional loss commensurate with cone death and with some aspects predicted by partial light stimulation. However, inhibitory pathways recover functionally from lost input by increasing spatiotemporal integration in a way that is not recapitulated by partially stimulating the control retina. Anatomically, inhibitory synapses are upregulated on secondary bipolar cells and output ganglion cells. These findings demonstrate the greater capacity for inhibition, compared with excitation, to modify spatiotemporal processing with fewer cone inputs.

Synchronous inhibitory pathways create both efficiency and diversity in the retina.

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.

Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice.

Executing memory-guided behavior requires storage of information about experience and later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, facilitating planning based on evaluation of those experiences, or both. We designed a dynamic spatial task that promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been visited recently, indicating a role in memory storage rather than in directly guiding subsequent behavior.

How inhibitory neurons increase information transmission under threshold modulation.

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.

Robust and replicable measurement for prepulse inhibition of the acoustic startle response.

Measuring animal behavior in the context of experimental manipulation is critical for modeling, and understanding neuropsychiatric disease. Prepulse inhibition of the acoustic startle response (PPI) is a behavioral phenomenon studied extensively for this purpose, but the results of PPI studies are often inconsistent. As a result, the utility of this phenomenon remains uncertain. Here, we deconstruct the phenomenon of PPI and confirm several limitations of the methodology traditionally utilized to describe PPI, including that the underlying startle response has a non-Gaussian distribution, and that the traditional PPI metric changes with different stimuli. We then develop a novel model that reveals PPI to be a combination of the previously appreciated scaling of the startle response, as well as a scaling of sound processing. Using our model, we find no evidence for differences in PPI in a rat model of Fragile-X Syndrome (FXS) compared with wild-type controls. These results in the rat provide a reliable methodology that could be used to clarify inconsistent PPI results in mice and humans. In contrast, we find robust differences between wild-type male and female rats. Our model allows us to understand the nature of these differences, and we find that both the startle-scaling and sound-scaling components of PPI are a function of the baseline startle response. Males and females differ specifically in the startle-scaling, but not the sound-scaling, component of PPI. These findings establish a robust experimental and analytical approach that has the potential to provide a consistent biomarker of brain function.

Scalable method for micro-CT analysis enables large scale quantitative characterization of brain lesions and implants.

Anatomic evaluation is an important aspect of many studies in neuroscience; however, it often lacks information about the three-dimensional structure of the brain. Micro-CT imaging provides an excellent, nondestructive, method for the evaluation of brain structure, but current applications to neurophysiological or lesion studies require removal of the skull as well as hazardous chemicals, dehydration, or embedding, limiting their scalability and utility. Here we present a protocol using eosin in combination with bone decalcification to enhance contrast in the tissue and then employ monochromatic and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure with the preservation of the surrounding skull. Instead of relying on descriptive, time-consuming, or subjective methods, we develop simple quantitative analyses to map the locations of recording electrodes and to characterize the presence and extent of hippocampal brain lesions.

Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task.

Animal behavior provides context for understanding disease models and physiology. However, that behavior is often characterized subjectively, creating opportunity for misinterpretation and misunderstanding. For example, spatial alternation tasks are treated as paradigmatic tools for examining memory; however, that link is actually an assumption. To test this assumption, we simulated a reinforcement learning (RL) agent equipped with a perfect memory process. We found that it learns a simple spatial alternation task more slowly and makes different errors than a group of male rats, illustrating that memory alone may not be sufficient to capture the behavior. We demonstrate that incorporating spatial biases permits rapid learning and enables the model to fit rodent behavior accurately. Our results suggest that even simple spatial alternation behaviors reflect multiple cognitive processes that need to be taken into account when studying animal behavior.SIGNIFICANCE STATEMENT Memory is a critical function for cognition whose impairment has significant clinical consequences. Experimental systems aimed at testing various sorts of memory are therefore also central. However, experimental designs to test memory are typically based on intuition about the underlying processes. We tested this using a popular behavioral paradigm: a spatial alternation task. Using behavioral modeling, we show that the straightforward intuition that these tasks just probe spatial memory fails to account for the speed at which rats learn or the types of errors they make. Only when memory-independent dynamic spatial preferences are added can the model learn like the rats. This highlights the importance of respecting the complexity of animal behavior to interpret neural function and validate disease models.

Mature Retina Compensates Functionally for Partial Loss of Rod Photoreceptors.

Loss of primary neuronal inputs inevitably strikes every neural circuit. The deafferented circuit could propagate, amplify, or mitigate input loss, thus affecting the circuit's output. How the deafferented circuit contributes to the effect on the output is poorly understood because of lack of control over loss of and access to circuit elements. Here, we control the timing and degree of rod photoreceptor ablation in mature mouse retina and uncover compensation. Following loss of half of the rods, rod bipolar cells mitigate the loss by preserving voltage output. Such mitigation allows partial recovery of ganglion cell responses. We conclude that rod death is compensated for in the circuit because ganglion cell responses to stimulation of half of the rods in an unperturbed circuit are weaker than responses after death of half of the rods. The dominant mechanism of such compensation includes homeostatic regulation of inhibition to balance the loss of excitation.

Adaptation of Inhibition Mediates Retinal Sensitization.

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.

Partial Cone Loss Triggers Synapse-Specific Remodeling and Spatial Receptive Field Rearrangements in a Mature Retinal Circuit.

Resilience of neural circuits has been observed in the persistence of function despite neuronal loss. In vision, acuity and sensitivity can be retained after 50% loss of cones. While neurons in the cortex can remodel after input loss, the contributions of cell-type-specific circuits to resilience are unknown. Here, we study the effects of partial cone loss in mature mouse retina where cell types and connections are known. At first-order synapses, bipolar cell dendrites remodel and synaptic proteins diminish at sites of input loss. Sites of remaining inputs preserve synaptic proteins. Second-order synapses between bipolar and ganglion cells remain stable. Functionally, ganglion cell spatio-temporal receptive fields retain center-surround structure following partial cone loss. We find evidence for slower temporal filters and expanded receptive field surrounds, derived mainly from inhibitory inputs. Surround expansion is absent in partially stimulated control retina. Results demonstrate functional resilience to input loss beyond pre-existing mechanisms in control retina.

Adaptive feature detection from differential processing in parallel retinal pathways.

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.

Inferring hidden structure in multilayered neural circuits.

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.

Optimal Information Transmission by Overlapping Retinal Cell Mosaics.

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.

A Model of Synaptic Reconsolidation.

Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interpreted as reconsolidation at the synaptic level. The model implements reconsolidation through stabilization of consolidated synapses by stabilizing entities combined with an activity-dependent reservoir of stabilizing entities that are immune to protein synthesis inhibition (PSI). We derive a reduced version of our model to explore the conditions under which synaptic reconsolidation does or does not occur, often referred to as the boundary conditions of reconsolidation. We find that our computational model of synaptic reconsolidation displays complex boundary conditions. Our results suggest that a limited resource of hypothetical stabilizing molecules or complexes, which may be implemented by protein phosphorylation or different receptor subtypes, can underlie the phenomenon of synaptic reconsolidation.

Critical and maximally informative encoding between neural populations in the retina.

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.

Synaptic consolidation: from synapses to behavioral modeling.

Synaptic plasticity, a key process for memory formation, manifests itself across different time scales ranging from a few seconds for plasticity induction up to hours or even years for consolidation and memory retention. We developed a three-layered model of synaptic consolidation that accounts for data across a large range of experimental conditions. Consolidation occurs in the model through the interaction of the synaptic efficacy with a scaffolding variable by a read-write process mediated by a tagging-related variable. Plasticity-inducing stimuli modify the efficacy, but the state of tag and scaffold can only change if a write protection mechanism is overcome. Our model makes a link from depotentiation protocols in vitro to behavioral results regarding the influence of novelty on inhibitory avoidance memory in rats.

Insights from the retina into the diverse and general computations of adaptation, detection, and prediction.

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.