For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers---efficient balanced spiking networks that simulate Langevin sampling, and networks with probabilistic spike rules that implement Metropolis-Hastings sampling---can be unified within a common framework. We then show that careful choice of population geometry, corresponding to the natural space of parameters, enables rapid inference of parameters drawn from strongly-correlated high-dimensional distributions in both networks. Our results suggest design principles for algorithms for sampling-based probabilistic inference in spiking neural networks, yielding potential inspiration for neuromorphic computing and testable predictions for neurobiology.
Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.
The brain displays a remarkable ability to sustain stable memories, allowing animals to execute precise behaviors or recall stimulus associations years after they were first learned. Yet, recent long-term recording experiments have revealed that single-neuron representations continuously change over time, contravening the classical assumption that learned features remain static. How do unstable neural codes support robust perception, memories, and actions? Here, we review recent experimental evidence for such representational drift across brain areas, as well as dissections of its functional characteristics and underlying mechanisms. We emphasize theoretical proposals for how drift need not only be a form of noise for which the brain must compensate. Rather, it can emerge from computationally beneficial mechanisms in hierarchical networks performing robust probabilistic computations. \copyright 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more-whether time, effort, or money-when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions. Copyright \copyright 2020 Elsevier Inc. All rights reserved.
Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world. \copyright 2020, Lak et al.
Individual neurons in many cortical regions have been found to encode specific, identifiable features of the environment or body that pertain to the function of the region1-3. However, in frontal cortex, which is involved in cognition, neural responses display baffling complexity, carrying seemingly disordered mixtures of sensory, motor and other task-related variables4-13. This complexity has led to the suggestion that representations in individual frontal neurons are randomly mixed and can only be understood at the neural population level14,15. Here we show that neural activity in rat orbitofrontal cortex (OFC) is instead highly structured: single neuron activity co-varies with individual variables in computational models that explain choice behaviour. To characterize neural responses across a large behavioural space, we trained rats on a behavioural task that combines perceptual and value-guided decisions. An unbiased, model-free clustering analysis identified distinct groups of OFC neurons, each with a particular response profile in task-variable space. Applying a simple model of choice behaviour to these categorical response profiles revealed that each profile quantitatively corresponds to a specific decision variable, such as decision confidence. Additionally, we demonstrate that a connectivity-defined cell type, orbitofrontal neurons projecting to the striatum, carries a selective and temporally sustained representation of a single decision variable: integrated value. We propose that neurons in frontal cortex, as in other cortical regions, form a sparse and overcomplete representation of features relevant to the region's function, and that they distribute this information selectively to downstream regions to support behaviour.
How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior. \copyright 2018 Ott et al.; Published by Cold Spring Harbor Laboratory Press.
In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.
Studies of categorical decision-making attempt to understand behavior by probing how different features of complex and changing environments guide the selection of choices. While the parameters underlying these features often span a continuous range, the potential set of possible behavioral options is discrete. The neuroscientific study of decision-making draws heavily on the fields of psychology, economics, statistics, and ecology. Neuroscientific approaches to decision-making aim to reveal computational principles that can be mapped onto their neurobiological implementation.
There are two dominant traditions in neuroscience and psychology to study categorical decisions: perceptual and value-based decision-making. Perceptual decision-making focuses on how accurate decisions are reached by resolving perceptual uncertainty. In value-based decision-making, the stimuli themselves are not ambiguous, rather the value or utility of different options needs to be estimated based on prior experience. In both cases, the goal is to systematically manipulate different features of the environment in order to understand how they guide behavior.
paul_masset@TonyZador@GaryMarcus We could! One can design prompts of varying difficulty and ask for uncertainty estimates on a graded scale (GPT3 should be able to). The confidence signatures would tell us something about the internal uncertainty model