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- P. S. Sastry, G. Santharam, K. P. Unnikrishnan
- IEEE Trans. Neural Networks
- 1994

This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainableâ€¦ (More)

- Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan
- IEEE Transactions on Knowledge and Dataâ€¦
- 2005

This paper establishes a formal connection between two common, but previously unconnected methods for analyzing data streams: discovering frequent episodes in a computer science framework andâ€¦ (More)

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of anâ€¦ (More)

- K. P. Unnikrishnan, Kootala P. Venugopal
- Neural Computation
- 1994

t tions about transfer functions of individual neurons, and does not explicitly depend on he functional form of the error measure. Hence, it can be used in networks with arbii trary transferâ€¦ (More)

- Debprakash Patnaik, P. S. Sastry, K. P. Unnikrishnan
- Scientific Programming
- 2008

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one toâ€¦ (More)

- Tiffany C. Veinot, Emily A Bosk, K. P. Unnikrishnan, Theodore J. Iwashyna
- Social science & medicine
- 2012

Heart attack, or acute myocardial infarction (AMI), is a leading cause of death in the United States (U.S.). The most effective therapy for AMI is rapid revascularization: the mechanical opening ofâ€¦ (More)

- Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan
- IEEE Transactions on Knowledge and Dataâ€¦
- 2007

This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined, which extends this framework byâ€¦ (More)

- P. S. Sastry, K. P. Unnikrishnan
- Neural Computation
- 2010

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from differentâ€¦ (More)

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set ofâ€¦ (More)

- P. S. Sastry, M. Magesh, K. P. Unnikrishnan
- Neural Computation
- 2002

Alopex is a correlation-based gradient-free optimization technique useful in many learning problems. However, there are no analytical results on the asymptotic behavior of this algorithm. Thisâ€¦ (More)