Programmable neural logic

Circuits of threshold elements (Boolean input, Boolean output neurons) have been shown to be surprisingly powerful. Useful functions such as XOR, ADD and MULTIPLY can be implemented by such circuits more efficiently than by traditional AND/OR circuits. In view of that, we have designed and built a programmable threshold element. The weights are stored on polysilicon floating gates, providing long-term retention without refresh. The weight value is increased using tunneling and decreased via hot electron injection. A weight is stored on a single transistor allowing the development of dense arrays of threshold elements. A 16-input programmable neuron was fabricated in the standard 2 μm double-poly, analog process available from MOSIS. We also designed and fabricated the multiple threshold element introduced in [5]. It presents the advantage of reducing the area of the layout from O(n^2) to O(n); (n being the number of variables) for a broad class of Boolean functions, in particular symmetric Boolean functions such as PARITY. A long term goal of this research is to incorporate programmable single/multiple threshold elements, as building blocks in field programmable gate arrays

A Programmable Neural Oscillator Cell

A programmable analog neural oscillator cell architecture is presented. The proposed neuron circuit is of hysteretic neural nature with its implementation based on operational transconductance amplifiers (OTA's). The hysteresis loop as well as the frequency of oscillation are voltage (or current) dependent. The architecture, which involves two OTA's, a current mirror, a capacitor, a diode, and a resistor is very suitable for monolithic integrated circuits. Experimental results confirm the expected flexibility of the synthetic neuron

Ternary to binary converter design in CMOS using multiple input floating gate MOSFETS

In this work, a ternary to binary converter circuit is designed in 0.5μm n-well CMOS technology. The circuit takes two inputs corresponding to the ternary bits and gives four outputs, which are the binary equivalent bits of the ternary inputs. The ternary inputs range from (-1,-1)3 to (1,1) 3 which are decimal -4 to 4 and the four binary output bits are the sign bit (SB), most significant bit (MSB), second significant bit (SSB) and the least significant bit (LSB). The ternary inputs (-1, 0 and 1) are represented in terms of voltages of -3V, 0V and 3V. Multiple input floating gate (MIFG) MOSFETS are used in the design of ternary to binary converter. The four circuits to generate the SB, MSB, SSB and LSB outputs are designed separately and then connected together to perform the entire conversion. The MIFG MOSFET takes multiple input signals, which are the ternary inputs in this case and calculates the weighted sum of the inputs. This weighted sum of the inputs is called floating gate voltage and is given as input to the CMOS inverter. The CMOS inverter gives a high or low binary output depending on if the floating gate voltage is higher or lower than the threshold voltage of the CMOS inverter. The circuits are simulated using MOSIS BSIM level 7 model parameters. LEDIT version 13 is used for the layout and a total of 22 transistors are used in the design of the converter circuit. The floating gate of the transistor is simulated by not giving the input directly to the gate of the transistor. Instead inputs are fed to one end of the capacitors and the other end of the capacitors are tied together and given as an input to the inverter. The converter chip occupies an area of 1140 × 2090 μm2

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems