Although we highlighted a number of important computations necessary for optimal choice, many of these value representations are highly correlated, and thus disambiguating these representations to infer functional specialization between areas continues to be a challenge (Behrens et al., 2007; Hare, O'Doherty, Camerer, Schultz, & Rangel, 2008).
We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data.
An individual's Theory of Mind is "the capacity to infer mental states of others... a person's ability to form representations of other people's mental states and to use the representations to understand, predict and judge utterances and behaviors" [ 130].
For this purpose, we proposed a novel data assimilation algorithm utilizing a simple nonlinear model, termed the combinatorial transcription model [ 5, 30], and a state space representation [ 31, 32], to infer GRNs by restoring networks that is inferred by some GRNs inference methods or derived from the literature.
Such methods require the use of machine learning techniques to infer expressive representations from low/mid-level features.
However, when using RS to infer neural representations in FFA, it is also important to consider the FFA's role within a hierarchy of face-processing regions.
It is shown how complex temporal patterns that combine several variables and representation schemes can be used to infer process states and/or conditions.
Almost all methods that detect single nucleotide variants (SNVs) from NGS data use a representation of digital allelic counts to infer allelic abundance in the sample.
At the other end of the spectrum is continuous time representation: algorithms were recently developed to infer logic-based differential equations [ 5], and then simulations become similar to those of traditional kinetic models.
For example, if a user is interested in simulations of nucleic acids, a hierarchical representation of biomolecules could be used to infer that the user is actually looking for any simulation of DNA or RNA.
The basic idea we pursue is to embed perceptual inference in a generative model of decision-making that enables us, as experimenters, to infer the probabilistic representation of sensory contingencies and outcomes used by subjects.
