Biomedical subjects

Angela J Yu

Publications and source records attributed to Angela J Yu.

4 recordsLinked to original sources

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↗

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↗

PubMed2002

Biophysiologically plausible implementations of the maximum operation.

Visual processing in the cortex can be characterized by a predominantly hierarchical architecture, in which specialized brain regions along the processing pathways extract visual features of increasing complexity, accompanied by greater invariance in stimulus properties such as size and position. Various studies have postulated that a nonlinear pooling function such as the maximum (MAX) operation could be fundamental in achieving such selectivity and invariance. In this article, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Different canonical models are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we compare the performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each model and discuss the relevant physiological considerations.

Animals↗

PubMedDate not supplied

Acetylcholine in cortical inference.

Acetylcholine (ACh) plays an important role in a wide variety of cognitive tasks, such as perception, selective attention, associative learning, and memory. Extensive experimental and theoretical work in tasks involving learning and memory has suggested that ACh reports on unfamiliarity and controls plasticity and effective network connectivity. Based on these computational and implementational insights, we develop a theory of cholinergic modulation in perceptual inference. We propose that ACh levels reflect the uncertainty associated with top-down information, and have the effect of modulating the interaction between top-down and bottom-up processing in determining the appropriate neural representations for inputs. We illustrate our proposal by means of an hierarchical hidden Markov model, showing that cholinergic modulation of contextual information leads to appropriate perceptual inference.

Acetylcholine↗