Search

SummaryHow mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) is not known. We addressed this using the spared nerve injury model of neuropathic pain andin vivoelectrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons was not observed. We did, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns were recapitulated by silencing DH parvalbumin+(PV+) inhibitory interneurons, previously implicated in mechanical allodynia, as were allodynic pain-like behaviors in mice. These findings reveal decorrelated DH network activity, driven by alterations in PV+interneurons, as a prominent feature of neuropathic pain, and suggest that restoration of proper temporal activity is a potential treatment for chronic neuropathic pain.

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.

Gaze change can misalign spatial reference frames encoding visual and vestibular signals in cortex, which may affect the heading discrimination. Here, by systematically manipulating the eye-in-head and head-on-body positions to change the gaze direction of subjects, the performance of heading discrimination was tested with visual, vestibular, and combined stimuli in a reaction-time task in which the reaction time is under the control of subjects. We found the gaze change induced substantial biases in perceived heading, increased the threshold of discrimination and reaction time of subjects in all stimulus conditions. For the visual stimulus, the gaze effects were induced by changing the eye-in-world position, and the perceived heading was biased in the opposite direction of gaze. In contrast, the vestibular gaze effects were induced by changing the eye-in-head position, and the perceived heading was biased in the same direction of gaze. Although the bias was reduced when the visual and vestibular stimuli were combined, integration of the 2 signals substantially deviated from predictions of an extended diffusion model that accumulates evidence optimally over time and across sensory modalities. These findings reveal diverse gaze effects on the heading discrimination and emphasize that the transformation of spatial reference frames may underlie the effects.

A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of Bayesian Causal Inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief over (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modeling results, we show that humans report targets as stationary and steer toward their initial rather than final position more often when they are themselves moving, suggesting a misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results confirm both of these predictions. Lastly, analysis of eye-movements show that, while initial saccades toward targets are largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI.

Significance From choosing among the many courses offered in graduate school to dividing budget into research programs, the breadth–depth is a commonplace dilemma that arises when finite resources (e.g., time, money, cognitive capabilities) need to be allocated among a large range of alternatives. For such problems, decision makers need to trade off breadth—allocating little capacity to each of many alternatives—and depth—focusing capacity on a few options. We found that little available capacity (less than 10 samples for search) promotes allocating resources broadly, and thus breadth search is favored. Increased capacity results in an abrupt transition toward favoring a balance between breadth and depth. We finally describe a rich casuistic and heuristics for metareasoning with finite resources., In multialternative risky choice, we are often faced with the opportunity to allocate our limited information-gathering capacity between several options before receiving feedback. In such cases, we face a natural trade-off between breadth—spreading our capacity across many options—and depth—gaining more information about a smaller number of options. Despite its broad relevance to daily life, including in many naturalistic foraging situations, the optimal strategy in the breadth–depth trade-off has not been delineated. Here, we formalize the breadth–depth dilemma through a finite-sample capacity model. We find that, if capacity is small (∼10 samples), it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, which roughly decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, is a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multialternative decisions with bounded capacity using close-to-optimal heuristics.

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce the first rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next we show that the recurrent connections within a conventional ring attractor can be re-tuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system, as well as any neural system that tracks direction, orientation, or periodic rhythms.Significance StatementData from human subjects as well as animals shows that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new evidence against their current memory. However, we do not understand how the brain tracks uncertainty. Here we describe a simple and biologically plausible network model that can track the uncertainty associated with a working memory. The representation of uncertainty in this model improves the accuracy of its working memory, as compared to conventional models, because it assigns the proper weight to new conflicting evidence. Our model provides a new interpretation for observed fluctuations in brain activity, and it makes testable new predictions.

Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for new psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates a novel class of experiments to reveal the neural representations of latent structure.

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behavior. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence, and explains human behavior better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations, but counter-intuitively increases the accuracy of resulting decisions.

During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches have revealed that a small fraction of SWRs appear to "simulate" trajectories through the environment, called awake hippocampal replay. However, the functional role of a majority of these SWRs remains unclear. We find, using Bayesian model comparison of state-space models to characterize the spatiotemporal dynamics embedded in SWRs, that almost all SWRs of foraging rodents simulate such trajectories. Furthermore, these trajectories feature momentum, or inertia in their velocities, that mirrors the animals' natural movement, in contrast to replay events during sleep, which lack such momentum. Last, we show that past analyses of replayed trajectories for navigational planning were biased by the heuristic SWR sub-selection. Our findings thus identify the dominant function of awake SWRs as simulating trajectories with momentum and provide a principled foundation for future work on their computational function.

During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches to their analysis have revealed that a small fraction of SWRs appear to “simulate” trajectories through the environment—called awake hippocampal replay—while the functional role of a majority of these SWRs remains unclear. Applying a novel probabilistic approach to characterize the spatio-temporal dynamics embedded in SWRs, we instead show that almost all SWRs of foraging rodents simulate such trajectories through the environment. Furthermore, these trajectories feature momentum, that is, inertia in their velocities, that mirrors the animals’ natural movement. This stands in contrast to replay events during sleep which seem to follow Brownian motion without such momentum. Lastly, interpreting the replay trajectories in the context of navigational planning revealed that similar past analyses were biased by the heuristic SWR sub-selection. Overall, our approach provides a more complete characterization of the spatio-temporal dynamics within SWRs, highlights qualitative differences between sleep and awake replay, and ought to support future, more detailed, and less biased analysis of the role of awake replay in navigational planning.

Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations., Information regarding a sensory stimulus is distributed in activity of neuronal populations. Here the authors show stimulus information scales sub-linearly with the number of neurons in mouse visual cortex due to correlated noise and may saturate in far fewer numbers of neurons than the total in V1.

SummaryIn uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography (MEG), and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one’s own actions.HighlightsExploration-exploitation dilemmas confound information seeking with changes-of-mind.Unlike changes-of-mind, information seeking requires control over information sampling.Information seeking is associated with reduced confidence and active hypothesis testing.Stronger neurophysiological correlates of attention and arousal during information seeking.

Angular path integration is the ability of a system to estimate its own heading direction from potentially noisy angular velocity (or increment) observations. Non-probabilistic algorithms for angular path integration, which rely on a summation of these noisy increments, do not appropriately take into account the reliability of such observations, which is essential for appropriately weighing one's current heading direction estimate against incoming information. In a probabilistic setting, angular path integration can be formulated as a continuous-time nonlinear filtering problem (circular filtering) with observed state increments. The circular symmetry of heading direction makes this inference task inherently nonlinear, thereby precluding the use of popular inference algorithms such as Kalman filters, rendering the problem analytically inaccessible. Here, we derive an approximate solution to circular continuous-time filtering, which integrates state increment observations while maintaining a fixed representation through both state propagation and observational updates. Specifically, we extend the established projection-filtering method to account for observed state increments and apply this framework to the circular filtering problem. We further propose a generative model for continuous-time angular-valued direct observations of the hidden state, which we integrate seamlessly into the projection filter. Applying the resulting scheme to a model of probabilistic angular path integration, we derive an algorithm for circular filtering, which we term the circular Kalman filter. Importantly, this algorithm is analytically accessible, interpretable, and outperforms an alternative filter based on a Gaussian approximation., Comment: 15 pages, 3 figures, accepted at IEEE Transactions on Signal Processing

In the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: when we see a person walking on a train or an animal running in a herd, we recognize the individual’s movement as nested within a reference frame that is itself moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a novel class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.

Learning about rewards and punishments is critical for survival. Classical studies have demonstrated an impressive correspondence between the firing of dopamine neurons in the mammalian midbrain and the reward prediction errors of reinforcement learning algorithms, which express the difference between actual reward and predicted mean reward. However, it may be advantageous to learn not only the mean but also the complete distribution of potential rewards. Recent advances in machine learning have revealed a biologically plausible set of algorithms for reconstructing this reward distribution from experience. Here, we review the mathematical foundations of these algorithms as well as initial evidence for their neurobiological implementation. We conclude by highlighting outstanding questions regarding the circuit computation and behavioral readout of these distributional codes.

Decision-makers are often faced with limited information about the outcomes of their choices. Current formalizations of uncertain choice, such as the explore-exploit dilemma, do not apply well to decisions in which search capacity can be allocated to each option in variable amounts. Such choices confront decision-makers with the need to tradeoff between breadth - allocating a small amount of capacity to each of many options – and depth - focusing capacity on a few options. We formalize the breadth-depth dilemma through a finite sample capacity model. We find that, if capacity is smaller than 4-7 samples, it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, that decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, reflects a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multi-alternative decisions with bounded capacity.