Neural compass or epiphenomenon? Experimental and theoretical investigations into the rodent head direction cell system

Institute for Adaptive and Neural ComputationHow does the brain convert sensory information into abstract representations that can support complex behaviours? The rodent head-direction (HD) system, whose cell ensembles represent head direction in the horizontal plane, is a striking example of a “cognitive” representation without a direct sensory correlate. It can be updated by sensory inputs fromdifferentmodalities, yet persists in the absence of external input. Together with cells tuned for place, the HD system is thought to be fundamental for navigation and spatial information processing. However, relatively few studies have sought to characterise the connection between the HD system and spatial behaviour directly, and their overall outcome has been inconclusive. In the experiments that make up the first part of this thesis, we approach this issue by isolating the self-motion component of the HDsystem. We developed an (angular) path integration task in which we show that rats rely on their internal sense of direction to return to a trial-unique starting location, allowing us to investigate the contribution of the HD system to this behaviour without influences from uncontrolled external cues. Using this path integration task, we show that rats with bilateral lesions of the lateral mammillary nuclei (LMN) are significantly impaired compared to sham-operated controls. Lesions of the LMN, which contains HDcells, are known to abolish directional firing in downstream HD areas, suggesting that impairment on the task is due to loss of HD activity. We also recorded HD cell activity as rats are performing the path integration task, and found the HD representation to correlate with the rats’ choice of return journey. Thus, we provide both causal and correlational experimental evidence for a critical role of the HD system in path integration. For the second part of this thesis, we implemented a computational model of how the HD system is updated by head movements during path integration, providing a novel explanation for HD cells’ ability to anticipate the animal’s head direction. The model predicts that such anticipatory time intervals (ATIs) should depend on the frequency spectrum of the rats’ head movements. In direct comparison with experimental recording data, we show that the model can explain up to 80% of the experimentally observed variance, where none was explained by previous models. We also consider the effects of propagating the HD signal through multiple layers, identifying several potential sources of anticipation and lag. In summary, this thesis provides behavioural, lesion, and unit-recording evidence that during path integration, rats use a directional signal provided by the head direction system. The neural mechanisms responsible for the generation and maintenance of this signal are explored computationally. The finding that ATIs depend on the statistics of head movements has methodological implications and constrains models of the HD system

Low and High Gamma Oscillations in Rat Ventral Striatum have Distinct Relationships to Behavior, Reward, and Spiking Activity on a Learned Spatial Decision Task

Local field potential (LFP) oscillations in the brain reflect organization thought to be important for perception, attention, movement, and memory. In the basal ganglia, including dorsal striatum, dysfunctional LFP states are associated with Parkinson's disease, while in healthy subjects, dorsal striatal LFPs have been linked to decision-making processes. However, LFPs in ventral striatum have been less studied. We report that in rats running a spatial decision task, prominent gamma-50 (45–55 Hz) and gamma-80 (70–85 Hz) oscillations in ventral striatum had distinct relationships to behavior, task events, and spiking activity. Gamma-50 power increased sharply following reward delivery and before movement initiation, while in contrast, gamma-80 power ramped up gradually to reward locations. Gamma-50 power was low and contained little structure during early learning, but rapidly developed a stable pattern, while gamma-80 power was initially high before returning to a stable level within a similar timeframe. Putative fast-spiking interneurons (FSIs) showed phase, firing rate, and coherence relationships with gamma-50 and gamma-80, indicating that the observed LFP patterns are locally relevant. Furthermore, in a number of FSIs such relationships were specific to gamma-50 or gamma-80, suggesting that partially distinct FSI populations mediate the effects of gamma-50 and gamma-80

Covert Expectation-of-Reward in Rat Ventral Striatum at Decision Points

Flexible decision-making strategies (such as planning) are a key component of adaptive behavior, yet their neural mechanisms have remained resistant to experimental analysis. Theories of planning require prediction and evaluation of potential future rewards, suggesting that reward signals may covertly appear at decision points. To test this idea, we recorded ensembles of ventral striatal neurons on a spatial decision task, in which hippocampal ensembles are known to represent future possibilities at decision points. We found representations of reward which were not only activated at actual reward delivery sites, but also at a high-cost choice point and before error correction. This expectation-of-reward signal at decision points was apparent at both the single cell and the ensemble level, and vanished with behavioral automation. We conclude that ventral striatal representations of reward are more dynamic than suggested by previous reports of reward- and cue-responsive cells, and may provide the necessary signal for evaluation of internally generated possibilities considered during flexible decision-making

Expectancies in Decision Making, Reinforcement Learning, and Ventral Striatum

Decisions can arise in different ways, such as from a gut feeling, doing what worked last time, or planful deliberation. Different decision-making systems are dissociable behaviorally, map onto distinct brain systems, and have different computational demands. For instance, “model-free” decision strategies use prediction errors to estimate scalar action values from previous experience, while “model-based” strategies leverage internal forward models to generate and evaluate potentially rich outcome expectancies. Animal learning studies indicate that expectancies may arise from different sources, including not only forward models but also Pavlovian associations, and the flexibility with which such representations impact behavior may depend on how they are generated. In the light of these considerations, we review the results of van der Meer and Redish (2009a), who found that ventral striatal neurons that respond to reward delivery can also be activated at other points, notably at a decision point where hippocampal forward representations were also observed. These data suggest the possibility that ventral striatal reward representations contribute to model-based expectancies used in deliberative decision making

Risk-Responsive Orbitofrontal Neurons Track Acquired Salience

SummaryDecision making is impacted by uncertainty and risk (i.e., variance). Activity in the orbitofrontal cortex, an area implicated in decision making, covaries with these quantities. However, this activity could reflect the heightened salience of situations in which multiple outcomes—reward and reward omission—are expected. To resolve these accounts, rats were trained to respond to cues predicting 100%, 67%, 33%, or 0% reward. Consistent with prior reports, some orbitofrontal neurons fired differently in anticipation of uncertain (33% and 67%) versus certain (100% and 0%) reward. However, over 90% of these neurons also fired differently prior to 100% versus 0% reward (or baseline) or prior to 33% versus 67% reward. These responses are inconsistent with risk but fit well with the representation of acquired salience linked to the sum of cue-outcome and cue-no-outcome associative strengths. These results expand our understanding of how the orbitofrontal cortex might regulate learning and behavior.Video Abstrac

Adaptive integration in the visual cortex by depressing recurrent cortical circuits

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliabilit

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

The impact of cannabidiol treatment on resting state functional connectivity, prefrontal metabolite levels and reward processing in recent-onset patients with a psychotic disorder

The first clinical trials with cannabidiol (CBD) as treatment for psychotic disorders have shown its potential as an effective and well-tolerated antipsychotic agent. However, the neurobiological mechanisms underlying the antipsychotic profile of CBD are currently unclear. Here we investigated the impact of 28-day adjunctive CBD or placebo treatment (600 mg daily) on brain function and metabolism in 31 stable recent-onset psychosis patients (&lt;5 years after diagnosis). Before and after treatment, patients underwent a Magnetic Resonance Imaging (MRI) session including resting state functional MRI, proton Magnetic Resonance Spectroscopy (1H-MRS) and functional MRI during reward processing. Symptomatology and cognitive functioning were also assessed. CBD treatment significantly changed functional connectivity in the default mode network (DMN; time × treatment interaction p = 0.037), with increased connectivity in the CBD (from 0.59 ± 0.39 to 0.80 ± 0.32) and reduced connectivity in the placebo group (from 0.77 ± 0.37 to 0.62 ± 0.33). Although there were no significant treatment effects on prefrontal metabolite concentrations, we showed that decreased positive symptom severity over time was associated with both diminishing glutamate (p = 0.029) and N-acetyl-aspartate (NAA; neuronal integrity marker) levels (p = 0.019) in the CBD, but not the placebo group. CBD treatment did not have an impact on brain activity patterns during reward anticipation and receipt or functional connectivity in executive and salience networks. Our results show that adjunctive CBD treatment of recent-onset psychosis patients induced changes in DMN functional connectivity, but not prefrontal metabolite concentrations or brain activity during reward processing. These findings suggest that DMN connectivity alteration may be involved in the therapeutic effects of CBD.</p

Integrating Early Results on Ventral Striatal Gamma Oscillations in the Rat

A vast literature implicates the ventral striatum in the processing of reward-related information and in mediating the impact of such information on behavior. It is characterized by heterogeneity at the local circuit, connectivity, and functional levels. A tool for dissecting this complex structure that has received relatively little attention until recently is the analysis of ventral striatal local field potential oscillations, which are more prominent in the gamma band compared to the dorsal striatum. Here we review recent results on gamma oscillations recorded from freely moving rats. Ventral striatal gamma separates into distinct frequency bands (gamma-50 and gamma-80) with distinct behavioral correlates, relationships to different inputs, and separate populations of phase-locked putative fast-spiking interneurons. Fast switching between gamma-50 and gamma-80 occurs spontaneously but is influenced by reward delivery as well as the application of dopaminergic drugs. These results provide novel insights into ventral striatal processing and highlight the importance of considering fast-timescale dynamics of ventral striatal activity