Encoding and decoding are important concepts in both theoretical and experimental neuroscience. This paper addresses experimentalists and focuses on the interpre-tation of empirical results obtained by fitting encoding and decoding models to brain-activity data.

Part 1: Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition Part 2: A Hierarchical Model for Time-Varying Functional Connectivity

A probabilistic decoder is a reinterpretation of model likelihoods based on coding theory. It is a distribution p (\mathbf {x}_n\mid \mathbf {z}_n) p(xn ∣ zn) over each value \mathbf {x}_n\in\mathbb {R}^D xn ∈ RD given a code \mathbf {z}_n zn.

Feb 29, 2024 · We introduce Generalized Encoding-Decoding Diffusion Probabilistic Models (EDDPMs) which integrate the core capabilities for broad applicability and enhanced performance. EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding.

Jan 28, 2019 · Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity.

Does including correlations improve decoding? — Including network terms improves decoding accuracy.

A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding–decoding scheme is exploited to convert the measurement signal into the digital format.

Jan 1, 2014 · In the probabilistic Bayesian inference and stochastic state-space framework adopted here, this population decoding question is answered by computing the posterior probability density.

Feb 15, 1998 · We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population.

We define Bayesian Encoding as the view that the primary function of sensory neurons is to compute and represent an approximation to some predefined probability distribution over relevant variables. We use the term “encoding” because the distribution that neurons are hypothesized to represent conceptually precedes the actual neural responses.