Sampling based inference with linear probabilistic population codes
نویسندگان
چکیده
منابع مشابه
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...
متن کاملProbabilistic 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 ...
متن کاملBayesian Inference with Efficient Neural Population Codes
The accuracy with which the brain can infer the value of a stimulus variable depends on both the amount of stimulus information that is represented in sensory neurons (encoding) and the mechanism by which this information is subsequently retrieved from the responses of these neurons (decoding). Previous studies have mainly focused on either the encoding or the decoding aspect. Here, we present ...
متن کاملProbabilistic inference for phrase-based machine translation : a sampling approach
Recent advances in statistical machine translation (SMT) have used dynamic programming (DP) based beam search methods for approximate inference within probabilistic translation models. Despite their success, these methods compromise the probabilistic interpretation of the underlying model thus limiting the application of probabilistically defined decision rules during training and decoding. As ...
متن کاملNeuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability
It has been argued that perceptual multistability reflects probabilistic inference performed by the brain when sensory input is ambiguous. Alternatively, more traditional explanations of multistability refer to low-level mechanisms such as neuronal adaptation. We employ a Deep Boltzmann Machine (DBM) model of cortical processing to demonstrate that these two different approaches can be combined...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2010
ISSN: 1662-453X
DOI: 10.3389/conf.fnins.2010.03.00258