We demonstrate a network visualization technique to analyze the recurrent state inside the LSTMs/GRUs used commonly in language and acoustic models. Interpreting intermediate state and network activations inside end-to-end models remains an open challenge. Our method allows users to understand exactly how much and what history is encoded inside recurrent state in grapheme sequence models. Our p...

In many regions of the vertebrate brain, microcircuits generate local recurrent activity that aids in the processing and encoding of incoming afferent inputs. Local recurrent activity can amplify, filter, and temporally and spatially parse out incoming input. Determining how these microcircuits function is of great interest because it provides glimpses into fundamental processes underlying brai...

Recurrent Self Organizing Map RSOM is studied in three di erent time series prediction cases RSOM is used to cluster the series into local data sets for which corresponding local linear models are estimated RSOM includes recurrent di erence vector in each unit which allows storing con text from the past input vectors Multilayer perceptron MLP network and autoregressive AR model are used to comp...

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that does not require a task-dependent construction of neural circuits. Instead it is based on principles of high dimensional dynami...

Safety and security have been a prime priority in people’s lives, having surveillance system at home keeps people their property more secured. In this paper, an audio has proposed that does both the detection localization of or sound events. The combined task detecting localizing events is known as Sound Event Localization (SELD). SELD work executed through Convolutional Recurrent Neural Networ...

In this paper, we systematically analyse the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graphtheoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN’s over-time nonlinear compl...

Recurrent neural networks unlike feed-forward networks are able to process inputs with time context. The key role in this process is played by the dynamics of the network, which transforms input data to the recurrent layer states. Several authors have described and analyzed dynamics of small sized recurrent neural networks with two or three hidden units. In our work we introduce techniques that...

We describe a system of thousands of binary perceptrons with coarse-oriented edges as input that is able to recognize shapes, even in a context with hundreds of classes. The perceptrons have randomized feedforward connections from the input layer and form a recurrent network among themselves. Each class is represented by a prelearned attractor (serving as an associative hook) in the recurrent n...

The loading problem is the problem to decide if a neural architecture can map a training set correctly with an appropriate choice of the weights. The following results will be shown: The loading problem is NP-complete for any feedforward perceptron architecture with at least two neurons in the rst hidden layer and varying input dimension. Further, it is NP-complete if the input dimension is xed...