Primary olfactory cortical areas receive direct input from the olfactory bulb, but also have extensive associational connections that have been mainly studied with classical anatomical methods. Here, we shed light on the functional properties of associational connections in the anterior and posterior piriform cortices (aPC and pPC) using optophysiological methods. We found that the aPC receives...

The diicult task of nding a recurrent representation of an input shape is called the inverse problem of fractal geometry. Previous attempts at solving this problem have applied techniques from numerical minimization, heuristic search and image compression. The most appropriate domain from which to attack this problem is not numerical analysis nor signal processing, but model-based computer visi...

A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive training samples. A well-trained neural net 1 is then presented once again with the training set ...

This paper presents a novel Wiener-type recurrent neural network with the observer/Kalman filter identification (OKID) algorithm for unknown dynamic nonlinear system identification. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our approach include: (1) t...

Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as vi...

This paper introduces a new concept of the connection weight to the standard recurrent neural networks— Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, unlike the original recurrent neural networks whose connection weight is a single real number, in the modified networks the weight of each connection is...

This study proposes two straightforward yet effective approaches to reduce the required computational time of training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale data as input. One approach provides coarse and fine temporal resolutions input RNN in parallel. The other concatenates over before considering them RNN. In both approaches, first, ...