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Biomedical subjects

Peter Dayan

Publications and source records attributed to Peter Dayan.

At least 19 recordsLinked to original sources
PubMed2007

Clinical prediction rule for identifying children with cerebrospinal fluid pleocytosis at very low risk of bacterial meningitis.

CONTEXT: Children with cerebrospinal fluid (CSF) pleocytosis are routinely admitted to the hospital and treated with parenteral antibiotics, although few have bacterial meningitis. We previously developed a clinical prediction rule, the Bacterial Meningitis Score, that classifies patients at very low risk of bacterial meningitis if they lack all of the following criteria: positive CSF Gram stain, CSF absolute neutrophil count (ANC) of at least 1000 cells/microL, CSF protein of at least 80 mg/dL, peripheral blood ANC of at least 10,000 cells/microL, and a history of seizure before or at the time of presentation. OBJECTIVE: To validate the Bacterial Meningitis Score in the era of widespread pneumococcal conjugate vaccination. DESIGN, SETTING, AND PATIENTS: A multicenter, retrospective cohort study conducted in emergency departments of 20 US academic medical centers through the Pediatric Emergency Medicine Collaborative Research Committee of the American Academy of Pediatrics. All children aged 29 days to 19 years who presented at participating emergency departments between January 1, 2001, and June 30, 2004, with CSF pleocytosis (CSF white blood cells > or =10 cells/microL) and who had not received antibiotic treatment before lumbar puncture. MAIN OUTCOME MEASURE: The sensitivity and negative predictive value of the Bacterial Meningitis Score. RESULTS: Among 3295 patients with CSF pleocytosis, 121 (3.7%; 95% confidence interval [CI], 3.1%-4.4%) had bacterial meningitis and 3174 (96.3%; 95% CI, 95.5%-96.9%) had aseptic meningitis. Of the 1714 patients categorized as very low risk for bacterial meningitis by the Bacterial Meningitis Score, only 2 had bacterial meningitis (sensitivity, 98.3%; 95% CI, 94.2%-99.8%; negative predictive value, 99.9%; 95% CI, 99.6%-100%), and both were younger than 2 months old. A total of 2518 patients (80%) with aseptic meningitis were hospitalized. CONCLUSIONS: This large multicenter study validates the Bacterial Meningitis Score prediction rule in the era of conjugate pneumococcal vaccine as an accurate decision support tool. The risk of bacterial meningitis is very low (0.1%) in patients with none of the criteria. The Bacterial Meningitis Score may be helpful to guide clinical decision making for the management of children presenting to emergency departments with CSF pleocytosis.

Adolescent↗
PubMed2007

Fast population coding.

Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. For the most obvious, simple, instantaneous encoder, the correlations induced by natural, smooth stimuli engender a decoder that requires access to information that is nonlocal both in time and across neurons. This formally amounts to a ruinous representation. We show that there is an alternative encoder that is computationally and representationally powerful in which each spike contributes independent information; it is independently decodable, in other words. We suggest this as an appropriate foundation for understanding time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple decoding.

Action Potentials↗
PubMed2006

Tonic dopamine: opportunity costs and the control of response vigor.

RATIONALE: Dopamine neurotransmission has long been known to exert a powerful influence over the vigor, strength, or rate of responding. However, there exists no clear understanding of the computational foundation for this effect; predominant accounts of dopamine's computational function focus on a role for phasic dopamine in controlling the discrete selection between different actions and have nothing to say about response vigor or indeed the free-operant tasks in which it is typically measured. OBJECTIVES: We seek to accommodate free-operant behavioral tasks within the realm of models of optimal control and thereby capture how dopaminergic and motivational manipulations affect response vigor. METHODS: We construct an average reward reinforcement learning model in which subjects choose both which action to perform and also the latency with which to perform it. Optimal control balances the costs of acting quickly against the benefits of getting reward earlier and thereby chooses a best response latency. RESULTS: In this framework, the long-run average rate of reward plays a key role as an opportunity cost and mediates motivational influences on rates and vigor of responding. We review evidence suggesting that the average reward rate is reported by tonic levels of dopamine putatively in the nucleus accumbens. CONCLUSIONS: Our extension of reinforcement learning models to free-operant tasks unites psychologically and computationally inspired ideas about the role of tonic dopamine in striatum, explaining from a normative point of view why higher levels of dopamine might be associated with more vigorous responding.

Animals↗
PubMed2006

The misbehavior of value and the discipline of the will.

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

Animals↗
PubMed2006

A normative perspective on motivation.

Understanding the effects of motivation on instrumental action selection, and specifically on its two main forms, goal-directed and habitual control, is fundamental to the study of decision making. Motivational states have been shown to 'direct' goal-directed behavior rather straightforwardly towards more valuable outcomes. However, how motivational states can influence outcome-insensitive habitual behavior is more mysterious. We adopt a normative perspective, assuming that animals seek to maximize the utilities they achieve, and viewing motivation as a mapping from outcomes to utilities. We suggest that habitual action selection can direct responding properly only in motivational states which pertained during behavioral training. However, in novel states, we propose that outcome-independent, global effects of the utilities can 'energize' habitual actions.

Animals↗
PubMed2006

Cortical substrates for exploratory decisions in humans.

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

Choice Behavior↗
PubMed2006

Dopamine modulation in the basal ganglia locks the gate to working memory.

The prefrontal cortex and basal ganglia are deeply implicated in working memory. Both structures are subject to dopaminergic neuromodulation in a way that exerts a critical influence on the proper operation of working memory. We present a novel network model to elucidate the role of phasic dopamine in the interaction of these two structures in initiating and maintaining mnemonic activity. We argue that neuromodulation plays a critical role in protecting memories against both internal and external sources of noise. Increases in cortical gain engendered by prefrontal dopamine release help make memories robust against external distraction, but do not offer protection against internal noise accompanying recurrent cortical activity. Rather, the output of the basal ganglia provides the gating function of stabilization against noise and distraction by enhancing select memories through targeted disinhibition of cortex. Dopamine in the basal ganglia effectively locks this gate by influencing the stability of up and down states in the striatum. Dopamine's involvement in affective processing endows this gating with specificity to motivational salience. We model a spatial working memory task and show that these combined effects of dopamine lead to superior performance.

Action Potentials↗
PubMed2006

Choice values.

Explore the source record for details and available documents.

Algorithms↗
PubMed2006

Phasic norepinephrine: a neural interrupt signal for unexpected events.

Extensive animal studies indicate that the neuromodulator norepinephrine plays an important role in specific aspects of vigilance, attention and learning, putatively serving as a neural interrupt or reset function. The activity of norepinephrine-releasing neurons in the locus coeruleus during attentional tasks is modulated not only by the animal's level of engagement and the sensory inputs, but also by temporally rich aspects of internal decision-making processes. Here, we propose that it is unexpected changes in the world within the context of a task that activate the noradrenergic interrupt signal. We quantify this idea in a Bayesian model of a well-studied visual discrimination task, demonstrating that the model captures a rich repertoire of noradrenergic responses at the sub-second temporal resolution.

Animals↗
PubMed2006

Images, frames, and connectionist hierarchies.

The representation of hierarchically structured knowledge in systems using distributed patterns of activity is an abiding concern for the connectionist solution of cognitively rich problems. Here, we use statistical unsupervised learning to consider semantic aspects of structured knowledge representation. We meld unsupervised learning notions formulated for multilinear models with tensor product ideas for representing rich information. We apply the model to images of faces.

Classification↗
PubMed2006

Soft mixer assignment in a hierarchical generative model of natural scene statistics.

Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.

Animals↗
PubMed2006

Risk of intracranial pathologic conditions requiring emergency intervention after a first complex febrile seizure episode among children.

OBJECTIVE: To determine the likelihood of intracranial pathologic conditions requiring emergency neurosurgical or medical intervention among children without meningitis who presented to the pediatric emergency department after a first complex febrile seizure. METHODS: We performed a retrospective review of prospectively collected data for children in neurologically normal condition who presented to a single pediatric emergency department after a first complex febrile seizure (focal, multiple, or prolonged). The complex febrile seizure classification was determined independently by 2 epileptologists. The presence of intracranial pathologic conditions was determined through review of neuroimaging results, telephone interviews, or medical record review. RESULTS: Data for 71 children with first complex febrile seizures were analyzed. Fifty-one (72%) had a single complex feature (20 focal, 22 multiple, and 9 prolonged), and 20 (28%) had multiple complex features. None of the 71 patients (1-sided 95% confidence interval: 4%) had intracranial pathologic conditions that required emergency neurosurgical or medical intervention. CONCLUSIONS: For children with first complex febrile seizures, the risk of intracranial pathologic conditions that require emergency neurosurgical or medical intervention is low, which suggests that routine emergency neuroimaging for this population is unnecessary.

Brain↗
PubMed2005

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.

A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Animals↗
PubMed2005

Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves.

Hippocampal area CA3 is widely considered to function as an autoassociative memory. However, it is insufficiently understood how it does so. In particular, the extensive experimental evidence for the importance of carefully regulated spiking times poses the question as to how spike timing-based dynamics may support memory functions. Here, we develop a normative theory of autoassociative memory encompassing such network dynamics. Our theory specifies the way that the synaptic plasticity rule of a memory constrains the form of neuronal interactions that will retrieve memories optimally. If memories are stored by spike timing-dependent plasticity, neuronal interactions should be formalized in terms of a phase response curve, indicating the effect of presynaptic spikes on the timing of postsynaptic spikes. We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory.

Action Potentials↗
PubMed2005

Uncertainty, neuromodulation, and attention.

Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.

Acetylcholine↗
PubMed2005

Dopamine, uncertainty and TD learning.

Substantial evidence suggests that the phasic activities of dopaminergic neurons in the primate midbrain represent a temporal difference (TD) error in predictions of future reward, with increases above and decreases below baseline consequent on positive and negative prediction errors, respectively. However, dopamine cells have very low baseline activity, which implies that the representation of these two sorts of error is asymmetric. We explore the implications of this seemingly innocuous asymmetry for the interpretation of dopaminergic firing patterns in experiments with probabilistic rewards which bring about persistent prediction errors. In particular, we show that when averaging the non-stationary prediction errors across trials, a ramping in the activity of the dopamine neurons should be apparent, whose magnitude is dependent on the learning rate. This exact phenomenon was observed in a recent experiment, though being interpreted there in antipodal terms as a within-trial encoding of uncertainty.

Journal Article↗