Probabilistic Population Codes for Bayesian Decision Making

Probabilistic Population Codes for Bayesian Decision Making

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 in...

Probabilistic Decision Making of Robot Behavior Based on Bayesian Network

This paper presents a technique for an intelligent software robot (sobot) Rity to behave in uncertain environment in an appropriate manner. The intelligence of a robot is necessary to infer an appropriate behavior when various sensor data (stimuli) exist simultaneously and a specific behavior can be decided by a behavior controller. There could be various methods to build a behavior controller....

Combining Probabilistic Population Codes

We study the problem of statistically correct inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the simultaneous act ivi ty of many units coding for some low dimensional quantity. A classic example are place cells in the rat hippocampus: these fire when the animal is at a particular place in an environment, so the u...

Probabilistic Population Codes

Currently there are two main working hypotheses that purport to answer the first of these questions: what do neural populations represent? The first (standard model) claims that populations encode the value of a stimulus. Whilst the second, more recent perspective, claims they encode a probability distribution over the possible values of a stimulus. The standard model can be caricatured in the ...

Dynamic Bayesian Decision - Making :