Neurally plausible, non-combinatorial iterative independent process analysis

Neurally plausible, non-combinatorial iterative independent process analysis

It has been shown recently that the identi cation of mixed hidden independent autoregressive processes (Independent Process Analysis, IPA), under certain conditions, can be free from combinatorial explosion. The key is that IPA can be reduced (i) to Independent Subspace Analysis and then, via a novel decomposition technique called Separation Theorem, (ii) to Independent Component Analysis. Here...

Toward A Neurally Plausible Language Of Thought

Independent Process Approximations for Random Combinatorial Structures

Many random combinatorial objects have a component structure whose joint distribution is equal to that of a process of mutually independent random variables, conditioned on the value of a weighted sum of the variables. It is interesting to compare the combinatorial structure directly to the independent discrete process, without renormalizing. The quality of approximation can often be convenient...

Neurally Plausible Reinforcement Learning of Working Memory Tasks

A key function of brains is undoubtedly the abstraction and maintenance of information from the environment for later use. Neurons in association cortex play an important role in this process: by learning these neurons become tuned to relevant features and represent the information that is required later as a persistent elevation of their activity [1]. It is however not well known how such neur...

Neurally plausible sparse coding via thresholding and local competition

While evidence indicates that neural systems may be employing sparse approximations to represent sensed stimuli, the mechanisms underlying this ability are not understood. We present a class of neurally plausible locally competitive algorithms (LCAs) that correspond to a collection of sparse coding principles minimizing a weighted combination of mean-squared error (MSE) and a coefficient cost f...