Abstract
This paper reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context, we motivate the use of nonconvergent dynamics for the signal to symbol translator. We then describe a biologically realistic model of the olfactory system proposed by Walter Freeman that has locally stable dynamics but is globally chaotic. We show how we can discretize Freemans model using digital signal processing techniques, providing an alternative to the more conventional Runge-Kutta integration. This analysis leads to a direct mixed-signal {analog amplitude/discrete time) implementation of the dynamical building block that simplifies the implementation of the interconnect. We present results of simulations and measurements obtained from a fabricated analog very large scale integration (VLSI) chip. © 2001 IEEE.

Abstract
This work addresses a built-in self-test methodology for circuit cell identification under specific matching conditions. The proposed technique is applied to the CMOS realization of a reduced-KII network, which is a system model of the biological olfactory cortex. This model behaves as an associative memory, a useful tool for information and adaptive processes. Based on a mixed-signal approach, the test strategy makes proper use of the circuits comprising the network structure, and provides self reconfiguration as well. Both testing procedures and design of essential building blocks are described in this paper. Simulation results are presented for a reduced-KII network comprising 128-cells, sequentially tested for matching in terms of offsets and their dynamic performances.

Abstract
A new technique for designing filters with long time constants in the discrete time domain is presented. The F&H (filter and hold) methodology halts the state of a continuous time filter every T seconds resulting in a filter implementation with time constants that can be controlled in three distinct ways: by the sampling period T, the duty cycle k = t/T or the time constant of the continuous time filter prototype. The final filter can be constructed from a typical Gm-C technique with very low power consumption.

Abstract
Our main interest is the development of a computational efficient VLSI implementation of a highly complex system proposed by Freeman to explain chaotic dynamics in the olfactory cortex using discrete time techniques. One of the issues involved with this implementation approach is the offset. This paper presents a simulation of a novel circuit for offset cancellation in Switch Current circuits. According to simulation results, this new technique is able to cancel the offset and has a low sensitivity to changes in feedback parameters associated with the delay (z-1) operation.

Abstract
This paper presents results concerning a full digital implementation of the KII and KIII model proposed by Freeman to explain the chaotic behavior of the mammalian's Olfactory Cortex. The environment used for the task is a commercial neural network package. The immediate goal is to offer a computational efficient alternative to Runge-Kutta methods of solving the system's differential equations. To accomplish the goal the differential equations impulse response were sampled and decomposed over the gamma basis. Both models, KII and KIII, are presently working as associative memories, where stored patterns can be recalled from an input pattern.

Abstract
This paper presents a low power MOS-VLSI implementation of an oscillator proposed by Freeman to model a very important component of the olfactory cortex. The model dynamics has time constants in the order of 1/220 (s). To accomplish the long time constants a new filtering technique recently proposed was utilized. All the blocks involving information processing were designed to operate below threshold (weak inversion).