Synthesis and analysis of nonlinear, analog, ultra low power, Bernoulli cell based CytoMimetic circuits for biocomputation

A novel class of analog BioElectronics is introduced for the systematic implementation of ultra-low power microelectronic circuits, able to compute nonlinear biological dynamics. This class of circuits is termed ``CytoMimetic Circuits'', in an attempt to highlight their actual function, which is mimicking biological responses, as observed experimentally. Inspired by the ingenious Bernoulli Cell Formalism (BCF), which was originally formulated for the modular synthesis and analysis of linear, time-invariant, high-dynamic range, logarithmic filters, a new, modified mathematical framework has been conceived, termed Nonlinear Bernoulli Cell Formalism (NBCF), which forms the core mathematical framework, characterising the operation of CytoMimetic circuits. The proposed nonlinear, transistor-level mathematical formulation exploits the striking similarities existing between the NBCF and coupled ordinary differential equations, typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating with good accuracy cellular and molecular dynamics and found to be in very good agreement with their biological counterparts. They usually occupy an area of a fraction of a square millimetre, while consuming between hundreds of nanowatts and few tenths of microwatts of power. The systematic nature of the NBCF led to the transformation of a wide variety of biochemical reactions into nonlinear Log-domain circuits, which span a large area of different biological model types. Moreover, a detailed analysis of the robustness and performance of the proposed circuit class is also included in this thesis. The robustness examination has been conducted via post-layout simulations of an indicative CytoMimetic circuit and also by providing fabrication-related variability simulations, obtained by means of analog Monte Carlo statistical analysis for each one of the proposed circuit topologies. Furthermore, a detailed mathematical analysis that is carefully addressing the effect of process-parameters and MOSFET geometric properties upon subthreshold translinear circuits has been conducted for the fundamental translinear blocks, CytoMimetic topologies are comprised of. Finally, an interesting sub-category of Neuromorphic circuits, the ``Log-Domain Silicon Synapses'' is presented and representative circuits are thoroughly analysed by a novel, generalised BC operator framework. This leads to the conclusion that the BC operator consists the heart of such Log-domain circuits, therefore, allows the establishment of a general class of BC-based silicon synaptic circuits, which includes most of the synaptic circuits, implemented so far in Log-domain.Open Acces

Synthesis of Translinear Analog Signal Processing Systems

Even in the predominantly digital world of today, analog circuits maintain a significant and necessary role in the way electronic signals are generated and processed. A straightforward method for synthesizing analog circuits would greatly improve the way that analog circuits are currently designed. In this dissertation, I build upon a synthesis methodology for translinear circuits originally introduced by Bradley Minch that uses multiple-input translinear elements (MITEs) as its fundamental building block. Introducing a graphical representation for the way that MITEs are connected, the designer can get a feel for how the equations relate to the physical circuit structure and allows for a visual method for reducing the number of transistors in the final circuit. Having refined some of the synthesis steps, I illustrate the methodology with many examples of static and dynamic MITE networks. For static MITE networks, I present a squaring reciprocal circuit and two versions of a vector magnitude circuit. A first-order log-domain filter and an RMS-to-DC converter are synthesized showing two first-order systems, both linear and non-linear. Higher order systems are illustrated with the synthesis of a second-order log-domain filter and a quadrature oscillator. The resulting circuits from several of these examples are combined to form a phase-locked loop (PLL). I present simulated and experimental results from many of these examples. Additionally, I present information related to the process of programming the floating-gate charge for the MITEs through the use of Fowler-Nordheim tunneling and hot-electron injection. I also include code for a Perl program that determines the optimum connections to minimize the total number of MITEs for a given circuit.NSF Career award CCR-998462

The stochastic neural network in VLSI for studying noise communication in crayfish

L'attivita neurale in natura presenta un andamento stocastico e gioca un ruolo significativo nel cervello. Tuttavia, la maggior parte degli articoli si limitano alla simulazione di neuroni stocastici. In questa tesi, proponiamo un nuovo modello stocastico secondo il formalismo di Hodgkin-Huxley basato su equazioni dierenziali stocastiche e moto browniano. Il nuovo modello di equazione dierenziale stocastiche riproduce una vasta gamma di dinamiche in modo piu realistico rispetto ai precedenti modelli deterministici. Tale modello stocastico e stato applicata a una semplice rete neurale che si trova sulla coda di un gambero chiamato CPR (caudal photoreceptor). Presentiamo una libreria di operatori analogici stocastici utilizzati per il calcolo analogico in tempo reale. Questa libreria permette di ottenere una implementazione in silicio della rete stocastica CPR che sarà collegata alle cellule nervose del gambero. L'interazione vivente-articiali permettera ai biologisti di comprendere meglio i fenomeni nervosi // The Neural activity in nature presents a stochastic trend and plays an important role in the brain. However, most papers are limited simulating stochastic neurons. In this thesis, we propose a novel stochastic model according to the Hodgkin{Huxley formalism using stochastic dierential equations and Brownian motion. The new stochastic dierential equation model reproduces a large range of dynamics more realistically than previous deterministic models. Such stochastic model has been applied to simple neural network that is located on the tail of the craysh called CPR (caudal photoreceptor). We present a library of stochastic analog operators used for the analog real-time computation. This library allows to obtain a silicon implementation of the CPR stochastic network that will be connected to the nerve cells of the craysh. The living-articial interaction will allow biologists to better understand the nervous phenomen