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Results for “Animal Behavior and Cognition”

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bioRxiv2017

Cortical Tonic Inhibition Regulates the Expression of Spike-and-Wave Discharges Associated with Absence Epilepsy.

Synchronous and bilateral spike-and-wave discharges accompany nonconvulsive behavioral and cognitive arrest during seizures associated with absence epilepsy. Previous investigation of multiple absence animal models suggests that the underlying cause of absence seizures is an increase in thalamic inhibitory tonic currents. In contrast, in this study we provide evidence that the level of cortical tonic inhibition also regulates absence seizure expression. Using continuous video-EEG recordings to monitor absence seizures and spike-and-wave discharge expression we show that pharmacological blockade of cortical tonic inhibition provokes absence seizures in wild-type mice. Furthermore, we show that pharmacological rescue of cortical tonic inhibition in an absence mouse ({gamma}2R43Q) model, which lacks tonic inhibition, suppresses absence seizure and spike-and-wave discharge expression. Collectively, these results suggest an optimum level of tonic inhibition in the thalamocortical circuit is required for normal functioning and that a deviation from this optimum results in aberrant thalamocortical function, SWDs and absence seizures.

neuroscience↗

bioRxiv2017

Common features in plastic changes rather than constructed structures in recurrent neural network prefrontal cortex models

We have flexible control over our cognition depending on the context or surrounding environments. The prefrontal cortex (PFC) controls this cognitive flexibility; however, the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques have allowed simple recurrent neural network PFC models to perform human- or animal-like behavioral tasks. These systems allow us to acquire parameters, which we could not in biological experiments, for performing the tasks. We compared four models, in which a flexible cognition task, called context-dependent integration task, was performed; subsequently, we searched for common features. In all the models, we observed that high plastic synapses were concentrated in the small neuronal population and the more concentrated neuronal units contributed further to the performance. However, there were no common properties in the constructed structures. These results suggest that plastic changes can be more general and important to accomplish cognitive tasks than features of the constructed structures.

neuroscience↗

bioRxiv2016

Searching for Principles of Brain Computation

Highlights O_LIHints for computational principles from experimental data\nC_LIO_LIComputational role of diverse network components\nC_LIO_LIEmergence and computational role of assemblies\nC_LIO_LIProbabilistic inference through stochastic network dynamics\nC_LIO_LIOngoing network rewiring and compensation through synaptic sampling\nC_LI\n\nAbstractExperimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.

neuroscience↗

bioRxiv2017

The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks

Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here we explore one such bias, namely the bias to perceive animacy, and used the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (how an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow-mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were strongly biased towards object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, this bias interfered with proper object identification, such as failing to signal that a cow-mug is a mug. The bias in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to veridically represent objects when visual appearance is dissociated from animacy, probably due to a biased processing of visual features typical of animate objects.

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bioRxiv2017

Selective attention controls olfaction in rodents

Critical animal behaviors, especially among rodents, are guided by odors in remarkably well-coordinated manners. While many extramodal sensory cues compete for cognitive resources in these ecological contexts, that rodents can engage in such odor-guided behaviors suggests that they selectively attend to odors. We developed a behavioral paradigm to reveal that rats are indeed capable of selectively attending to odors in the presence of competing extramodal stimuli and found that this selective attention facilitates accurate odor-guided decisions. Further, we uncovered that attention to odors adaptively sharpens their representation among neurons in a brain region considered integral for odor-driven behaviors. Thus, selective attention contributes to olfaction by enhancing the coding of odors in a manner analogous to that observed among other sensory systems.

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bioRxiv2017

Prefrontal D1 dopamine-receptor neurons and delta resonance in interval timing

Considerable evidence has shown that prefrontal neurons expressing D1-type dopamine receptors (D1DRs) are critical for working memory, flexibility, and timing. This line of work predicts that frontal neurons expressing D1DRs mediate cognitive processing. During timing tasks, one form this cognitive processing might take is time-dependent ramping activity -- monotonic changes in firing rate over time. Thus, we hypothesized the prefrontal D1DR+ neurons would strongly exhibited time-dependent ramping during interval timing. We tested this idea using an interval-timing task in which we used optogenetics to tag D1DR+ neurons in the mouse medial frontal cortex (MFC). While 23% of MFC D1DR+ neurons exhibited ramping, this was significantly less than untagged MFC D1DR+ neurons. By contrast, MFC D1DR+ neurons had strong delta-frequency (1-4 Hz) coherence with other MFC ramping neurons. This coherence was phase-locked to cue onset and was strongest early in the interval. To test the significance of these interactions, we optogenetically stimulated MFC D1DR+ neurons early vs. late in the interval. We found that 2-Hz stimulation early in the interval was particularly effective in rescuing timing-related behavioral performance deficits in dopamine-depleted animals. These findings provide insight into MFC networks and have relevance for disorders such as Parkinsons disease and schizophrenia.\n\nSignificance StatementPrefrontal D1DRs are involved in cognitive processing and cognitive dysfunction in human diseases such as Parkinsons disease and schizophrenia. We use optogenetics to identify these neurons, as well as neurons that are putatively connected to MFC D1DR+ neurons. We study these neurons in detail during an elementary cognitive task. These data could have relevance for cognitive deficits for Parkinsons disease, schizophrenia, and other diseases involving frontal dopamine.

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bioRxiv2016

Reward-based training of recurrent neural networks for cognitive and value-based tasks

Trained neural network models, which exhibit many features observed in neural recordings from behaving animals and whose activity and connectivity can be fully analyzed, may provide insights into neural mechanisms. In contrast to commonly used methods for supervised learning from graded error signals, however, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when the optimal behavior depends on an animals internal judgment of confidence or subjective preferences. Here, we describe reward-based training of recurrent neural networks in which a value network guides learning by using the selected actions and activity of the policy network to predict future reward. We show that such models capture both behavioral and electrophysiological findings from well-known experimental paradigms. Our results provide a unified framework for investigating diverse cognitive and value-based computations, including a role for value representation that is essential for learning, but not executing, a task.

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bioRxiv2017

How Cognitive and Reactive Fear Circuits Optimize Escape Decisions in Humans

Flight initiation distance (FID), the distance at which an organism flees from an approaching threat, is an ecological metric of cost-benefit functions of escape decisions. We adapted the FID paradigm to investigate how fast or slow attacking virtual predators constrain escape decisions. We show that rapid escape decisions rely on reactive fear circuits in the periaqueductal gray and midcingulate cortex (MCC), while protracted escape decisions, defined by larger buffer zones, were associated with cognitive fear circuits which include posterior cingulate cortex, hippocampus and the ventromedial prefrontal cortex, circuits implicated in strategic avoidance and behavioral flexibility. Using a Bayesian Decision Model, we further show that optimization of escape decisions under rapid flight were localized to the MCC, a region involved in adaptive motor control, while the hippocampus is implicated in optimizing decisions that update and control slower escape initiation. These results demonstrate an unexplored link between defensive survival circuits and their role in adaptive escape decisions.\n\nSignificanceHumans, like other animals, have evolved a set of circuits whose primary function is survival. In the case of predation, these circuits include reactive fear circuits involved in fast and immediate escape decisions and cognitive fear circuits that are involved in the conscious feeling of threat as well as slow strategic escape. Using neuroimaging combined with computational modeling, we support this differentiation of fear circuits by showing that fast escape decisions are elicited by the periaqueductal gray and MCC, regions involved in reactive flight. Conversely, slower escape decisions rely on the hippocampus, posterior cingulate cortex and prefrontal cortex, a circuit implicated in behavioral flexibility. These results support the role of the defensive survival circuitry in escape decisions and a separation of fear into reactive and cognitive circuits.

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bioRxiv2017

Effects of altered excitation-inhibition balance on decision making in a cortical circuit model

BackgroundDisruption of the synaptic balance between excitation and inhibition (E/I balance) in cortical circuits is a leading hypothesis for pathophysiologies of neuropsychiatric disorders, such as schizophrenia. However, it is poorly understood how synaptic E/I disruptions propagate upward to induce cognitive deficits, including impaired decision making (DM).\n\nMethodsWe investigated how E/I perturbations may impair temporal integration of evidence during perceptual DM in a biophysically-based model of association cortical microcircuits. Using multiple psychophysical task paradigms, we characterized effects of NMDA receptor hypofunction at two key synaptic sites: inhibitory interneurons (elevating E/I ratio, via disinhibition), versus excitatory pyramidal neurons (lowering E/I ratio).\n\nResultsDisruption of E/I balance in either direction can similarly impair DM as assessed by psychometric performance, following inverted-U dependence. Nonetheless, these regimes make dissociable predictions for task paradigms that characterize the time course of evidence accumulation. Under elevated E/I ratio, DM is impulsive: evidence early in time is weighted much more than late evidence. In contrast, under lowered E/I ratio, DM is indecisive: evidence integration and winner-take-all competition between options are weakened. These effects are well captured by an extended drift-diffusion model with self-coupling.\n\nConclusionsOur findings characterize critical roles of cortical E/I balance in cognitive functions, the utility of timing-sensitive psychophysical paradigms, and relationships between circuit and psychological models. The model makes specific predictions for behavior and neural activity that are testable in humans or animals under causal manipulations of E/I balance and in disease states.

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bioRxiv2018

Recalibration of path integration in hippocampal place cells

Hippocampal place cells are spatially tuned neurons that serve as elements of a \"cognitive map\" in the mammalian brain1. To detect the animals location, place cells are thought to rely upon two interacting mechanisms: sensing the animals position relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4-7. The latter mechanism, known as path integration, requires a finely tuned gain factor that relates the animals self-movement to the updating of position on the internal cognitive map, with external landmarks necessary to correct positional error that eventually accumulates8,9. Path-integration-based models of hippocampal place cells and entorhinal grid cells treat the path integration gain as a constant9-14, but behavioral evidence in humans suggests that the gain is modifiable15. Here we show physiological evidence from hippocampal place cells that the path integration gain is indeed a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In a novel, augmented reality system, visual landmarks were moved in proportion to the animals movement on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path integration gain, as estimated from the place cells after the landmarks were extinguished. We propose that this rapid plasticity keeps the positional update in register with the animals movement in the external world over behavioral timescales (mean 50 laps over 35 minutes). These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path integration system, as has been previously shown4,8,16-19, but also rapidly fine-tune the integration computation itself.

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bioRxiv2018

State conditional optogenetic entrainment of the septo-hippocampal circuit reveals the importance of endogenous theta oscillations as a unit of spatial metric

The manipulation of pattern generators in order to impose temporal organization on target nodes while accounting for dynamic changes in behavior and cognitive demand remains a significant challenge for the use of neurostimulation as a therapeutic treatment option. While perturbation through optogentic stimulation can reveal circuit mechanisms that create and locally integrate temporal organization, it is unclear whether superseding endogenous signals with artificial oscillations would benefit or impede hippocampus-dependent cognition or how cognitive demand might affect artificial septo-hippocampal entrainment. Optogenetic MS stimulation in wild-type rats in 3 conditions showed that septal input is more likely to supersede endogenous hippocampal LFP oscillations when animals are at rest or performing a hippocampus-dependent spatial accuracy task. Stimulation during a hippocampus-independent task, however, resulted in compensatory endogenous oscillations. Although stimulation effects on the inter-spike interval of hippocampal pyramidal cells mirrored task-conditional theta entrainment of the LFP, place field properties were unaffected. Analyses of spatial behavior indicate that optogenetic stimulation can attenuate performance and specific measures of goal zone estimation accuracy but otherwise does not affect the rats ability to navigate to the target quadrant. The results suggest that the behavioral effect of temporally organizing the septo-hippocampal circuit relative to an artificial theta signal is limited to the accuracy of the rats approximation of the goal zone location. These results have significant implications for the therapeutic use of optogenetic stimulation as a means of attenuating cognitive deficits associated with temporal discoordination.

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bioRxiv2017

GABAergic deficits and schizophrenia-like behaviors in a mouse model carrying patient-derived neuroligin-2 R215H mutation

Schizophrenia (SCZ) is a severe mental disorder characterized by delusion, hallucination, and cognitive deficits. We have previously identified from schizophrenia patients a loss-of-function mutation Arg215 [->] His215 (R215H) of neuroligin 2 (NLGN2) gene, which encodes a cell adhesion molecule critical for GABAergic synapse formation and function. Here, we generated a novel transgenic mouse line with neuroligin-2 (NL2) R215H mutation, which showed a significant loss of NL2 protein, reduced GABAergic transmission, and impaired hippocampal activation. Importantly, R215H KI mice displayed anxiety-like behaviors, impaired pre-pulse inhibition (PPI), cognition deficits and abnormal stress responses, recapitulating several key aspects of schizophrenia-like behavior. Our results demonstrate a significant impact of a single point mutation NL2 R215H on brain functions, providing a novel animal model for the study of schizophrenia and neuropsychiatric disorders.

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bioRxiv2017

Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by prefrontal cortex (PFC). Neural activity in PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear mixed selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which PFC exhibits computationally relevant properties such as mixed selectivity and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data shows significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and allows the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results give intuition about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.\n\nSignificance StatementPrefrontal cortex (PFC) is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli--and in particular, to combinations of stimuli (\"mixed selectivity\")--is a topic of interest. Despite the fact that models with random feedforward connectivity are capable of creating computationally-relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.

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bioRxiv2016

The foraging brain: evidence of Levy dynamics in brain networks

In this research we have analyzed functional magnetic resonance imaging (fMRI) signals of different networks in the brain under resting state condition.\n\nTo such end, the dynamics of signal variation, have been conceived as a stochastic motion, namely it has been modelled through a generalized Langevin stochastic differential equation, which combines a deterministic drift component with a stochastic component where the Gaussian noise source has been replaced with -stable noise.\n\nThe parameters of the deterministic and stochastic parts of the model have been fitted from fluctuating data. Results show that the deterministic part is characterized by a simple, linear decreasing trend, and, most important, the -stable noise, at varying characteristic index , is the source of a spectrum of activity modes across the networks, from those originated by classic Gaussian noise ( = 2), to longer tailed behaviors generated by the more general Levy noise (1 [≤] < 2).\n\nLevy motion is a specific instance of scale-free behavior, it is a source of anomalous diffusion and it has been related to many aspects of human cognition, such as information foraging through memory retrieval or visual exploration.\n\nFinally, some conclusions have been drawn on the functional significance of the dynamics corresponding to different values.\n\nAuthor SummaryIt has been argued, in the literature, that to gain intuition of brain fluctuations one can conceive brain activity as the motion of a random walker or, in the continuous limit, of a diffusing macroscopic particle.\n\nIn this work we have substantiated such metaphor by modelling the dynamics of the fMRI signal of different brain regions, gathered under resting state condition, via a Langevin-like stochastic equation of motion where we have replaced the white Gaussian noise source with the more general -stable noise.\n\nThis way we have been able to show the existence of a spectrum of modes of activity in brain areas. Such modes can be related to the kind of \"noise\" driving the Langevin equation in a specific region. Further, such modes can be parsimoniously distinguished through the stable characteristic index , from Gaussian noise ( = 2) to a range of sharply peaked, long tailed behaviors generated by Levy noise (1 [≤] < 2).\n\nInterestingly enough, random walkers undergoing Levy motion have been widely used to model the foraging behaviour of a range of animal species and, remarkably, Levy motion patterns have been related to many aspects of human cognition.

Neuroscience↗

bioRxiv2018

Covert spatial selection in primate basal ganglia

The basal ganglia are important for action selection. They are also implicated in perceptual and cognitive functions that seem far removed from motor control. Here, we tested whether the role of the basal ganglia in selection extends to non-motor aspects of behavior by recording neuronal activity in the caudate nucleus while animals performed a covert spatial attention task. We found that caudate neurons strongly select the spatial location of the relevant stimulus throughout the task even in the absence of any overt action. This spatially selective activity was dependent on task and visual conditions, and could be dissociated from goal-directed actions. Caudate activity was also sufficient to correctly identify every epoch in the covert attention task. These results provide a novel perspective on mechanisms of attention by demonstrating that the basal ganglia are involved in spatial selection and tracking of behavioral states even in the absence of overt orienting movements.

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bioRxiv2017

Genetic networks of the oxytocin system in the human brain: A gene expression and large-scale fMRI meta-analysis study

Oxytocin is a neuropeptide involved in animal and human reproductive and social behaviour, with potential implications for a range of psychiatric disorders. However, the therapeutic potential of oxytocin in mental health care suggested by animal research has not been successfully translated into clinical practice, partly due to a poor understanding of the expression and distribution of the oxytocin signaling pathway in the human brain, and its complex interactions with other biological systems. Among the genes involved in the oxytocin signaling pathway, three genes have been frequently implicated in human social behavior: OXT (structural gene for oxytocin), OXTR (oxytocin receptor), and CD38 (central oxytocin secretion). We characterized the distribution of the OXT, OXTR, and CD38 mRNA across the brain, identified putative gene pathway interactions by comparing gene expression patterns across 29131 genes, and assessed associations between gene expression patterns and cognitive states via large-scale fMRI meta-analysis. In line with the animal literature, oxytocin pathway gene expression was enriched in central, temporal, and olfactory regions. Across the brain, there was high co-expression of the oxytocin pathway genes with both dopaminergic (DRD2) and muscarinic acetylcholine (CHRM4) genes, reflecting an anatomical basis for critical gene pathway interactions. Finally, fMRI meta-analysis revealed that oxytocin pathway maps correspond with motivation and emotion processing, demonstrating the value of probing gene expression maps to identify brain functional targets for future pharmacological trials.

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bioRxiv2017

Loss of the intellectual disability and autism gene Cc2d1a and its homolog Cc2d1b differentially affect spatial memory, anxiety, and hyperactivity

Hundreds of genes are mutated in non-syndromic intellectual disability (ID) and autism spectrum disorder (ASD), with each gene often involved in only a handful of cases. Such heterogeneity can be daunting, but rare recessive loss of function (LOF) mutations can be a good starting point to provide insight into the mechanisms of neurodevelopmental disease. Biallelic LOF mutations in the signaling scaffold CC2D1A cause a rare form of autosomal recessive ID, sometimes associated with ASD and seizures. In parallel, we recently reported that Cc2d1a-deficient mice present with cognitive and social deficits, hyperactivity and anxiety. In Drosophila loss of the only ortholog of Cc2d1a, lgd, is embryonic lethal, while in vertebrates Cc2d1a has a homolog Cc2d1b which appears to be compensating, indicating that Cc2d1a and Cc2d1b have redundant function in humans and mice. Here, we generate an allelic series of Cc2d1a and Cc2d1b loss of function to determine the relative role of these genes during behavioral development. We generated Cc2d1b knockout (KO), Cc2d1a/1b double heterozygous and double KO mice, then performed behavioral studies to analyze learning and memory, social interactions, anxiety, and hyperactivity. We found that Cc2d1a and Cc2d1b have partially overlapping roles. Overall, loss of Cc2d1b is less severe than loss of Cc2d1a, only leading to cognitive deficits, while Cc2d1a/1b double heterozygous animals are similar to Cc2d1a-deficient mice. These results will help us better understand the deficits in individuals with CC2D1A mutations, suggesting that recessive CC2D1B mutations and trans-heterozygous CC2D1A and CC2D1B mutations could also contribute to the genetics of ID.

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bioRxiv2017

Learning And Attention Reveal A General Relationship Between Neuronal Variability And Perception

The trial-to-trial response variability that is shared between pairs of neurons (termed spike count correlations1 or rSC) has been the subject of many recent studies largely because it might limit the amount of information that can be encoded by neuronal populations. Spike count correlations are flexible and change depending on task demands2-7. However, the relationship between correlated variability and information coding is a matter of current debate2-14. This debate has been difficult to resolve because testing the theoretical predictions would require simultaneous recordings from an experimentally unfeasible number of neurons. We hypothesized that if correlated variability limits population coding, then spike count correlations in visual cortex should a) covary with subjects performance on visually guided tasks and b) lie along the dimensions in neuronal population space that contain information that is used to guide behavior. We focused on two processes that are known to improve visual performance: visual attention, which allows observers to focus on important parts of a visual scene15-17, and perceptual learning, which slowly improves observers ability to discriminate specific, well-practiced stimuli18-20. Both attention and learning improve performance on visually guided tasks, but the two processes operate on very different timescales and are typically studied using different perceptual tasks. Here, by manipulating attention and learning in the same task, subjects, trials, and neuronal populations, we show that there is a single, robust relationship between correlated variability in populations of visual neurons and performance on a change-detection task. We also propose an explanation for the mystery of how correlated variability might affect performance: it is oriented along the dimensions of population space used by the animal to make perceptual decisions. Our results suggest that attention and learning affect the same aspects of the neuronal population activity in visual cortex, which may be responsible for learning- and attention-related improvements in behavioral performance. More generally, our study provides a framework for leveraging the activity of simultaneously recorded populations of neurons, cognitive factors, and perceptual decisions to understand the neuronal underpinnings of behavior.

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