When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.
Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks
Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations
Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making
The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks
Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task
Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment
Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits
Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder
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Decomposing attention-deficit/hyperactivity disorder (ADHD)-related effects in response speed and variability
Uncertainty about mapping future actions into rewards may underlie performance on multiple measures of impulsivity in behavioral addiction: evidence from Parkinson's disease
High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teaching
Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field
Decision confidence and uncertainty in diffusion models with partially correlated neuronal integrators
Efficient "communication through coherence" requires oscillations structured to minimize interference between signals
Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task
Reward optimization in the primate brain: a probabilistic model of decision making under uncertainty
Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making
Neural correlates of prior expectations of motion in the lateral intraparietal and middle temporal areas
Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns
Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity
Hebbian learning in linear-nonlinear networks with tuning curves leads to near-optimal, multi-alternative decision making
Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder
Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations
Action selection: a race model for selected and non-selected actions distinguishes the contribution of premotor and prefrontal areas
There are things that we know that we know, and there are things that we do not know we do not know: Confidence in decision-making
The meaning of spikes from the neuron's point of view: predictive homeostasis generates the appearance of randomness
Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system
Decoupling speed and accuracy in an urgent decision-making task reveals multiple contributions to their trade-off
Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity
Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment
Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective
Estimating Sensorimotor Mapping From Stimuli to Behaviors to Infer C. elegans Movements by Neural Transmission Ability Through Connectome Databases
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics
The integration of probabilistic information during sensorimotor estimation is unimpaired in children with Cerebral Palsy
Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons
Tactile orientation perception: an ideal observer analysis of human psychophysical performance in relation to macaque area 3b receptive fields
Visuo-manual tracking: does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?
Hierarchical and Nonlinear Dynamics in Prefrontal Cortex Regulate the Precision of Perceptual Beliefs
Simultaneous encoding of the direction and orientation of potential targets during reach planning: evidence of multiple competing reach plans
Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
Converging Evidence From Electrocorticography and BOLD fMRI for a Sharp Functional Boundary in Superior Temporal Gyrus Related to Multisensory Speech Processing
Confidence Predictions Affect Performance Confidence and Neural Preparation in Perceptual Decision Making
Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions.
Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning
Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty.
Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation.
Higher performers upregulate brain signal variability in response to more feature-rich visual input.
Brain developing: Influences & Outcomes
This feed focuses on influences that affect the developing brain including genetics, fetal development, prenatal care, and gene-environment interactions. Here is the latest research in this field.