Mario Simoni

A multiconductance silicon neuron with biologically matched dynamics

We have designed, fabricated, and tested an analog integrated-circuit architecture to implement the conductance-based dynamics that model the electrical activity of neurons. The dynamics of this architecture are in accordance with the Hodgkin-Huxley formalism, a widely exploited, biophysically plausible model of the dynamics of living neurons. Furthermore the architecture is modular and compact in size so that we can implement networks of silicon neurons, each of desired complexity, on a single integrated circuit. We present in this paper a six-conductance silicon-neuron implementation, and characterize it in relation to the Hodgkin-Huxley formalism. This silicon neuron incorporates both fast and slow ionic conductances, which are required to model complex oscillatory behaviors (spiking, bursting, subthreshold oscillations).

Adaptation in a VLSI model of a neuron

We have designed, fabricated, and tested an analog very large-scale integrated (VLSI) circuit model of a biological neuron that implements self-adaptation of its parameters. We show that the addition of this self-adaptation to our model neuron can facilitate: (1) single parameter control over a multiparameter system; (2) stability of the system to fluctuations in parameters; and (3) coordinated modulation of parameters to achieve a desired behavior.

Sensory Feedback in a Half-Center Oscillator Model

We hypothesize that one role of sensorimotor feedback for rhythmic movements in biological organisms is to synchronize the frequency of movements to the mechanical resonance of the body. Our hypothesis is based on recent studies that have shown the advantage of moving at mechanical resonance and how such synchronization may be possible in biology. We test our hypothesis by developing a physical system that consists of a silicon-neuron central pattern generator (CPG), which controls the motion of a beam, and position sensors that provide feedback information to the CPG. The silicon neurons that we use are integrated circuits that generate neural signals based on the Hodgkin-Huxley dynamics. We use this physical system to develop a model of the interaction between the sensory feedback and the complex dynamics of the neurons to create the closed-loop system behavior. This model is then used to describe the conditions under which our hypothesis is valid and the general effects of sensorimotor feedback on the rhythmic movements of this system

Using Tablet PCs and Interactive Software in IC Design Courses to Improve Learning

This paper describes an initial study of using tablet PCs and interactive course software in integrated circuit (IC) design courses. A rapidly growing community is demonstrating how this technology can improve learning and retention of material by facilitating interaction between faculty and students via cognitive exercises during lectures. While numerous examples of using this technology exist, there is not much literature regarding its application to electrical engineering and IC design in particular. This paper presents examples from three different IC design courses to describe the types of cognitive activities that can be facilitated with this technology. While these examples are specific to IC design, the basic techniques are valid for any application. This study does show some initial success, which, it is hoped, will spark interest at other universities and lead to a better examination of its effectiveness through larger sample sizes.

Two-Dimensional Variation of Bursting Properties in a Silicon-Neuron Half-Center Oscillator

We are developing hardware models of central pattern generators (CPGs) to enhance neural prostheses, create biologically based controllers for autonomous machines, and to better understand how biology creates stable and robust movements. Previously, we designed and implemented an analog integrated circuit model of a neuron with Hodgkin-Huxley like dynamics, the silicon neuron. In this work, we use silicon neurons to implement a half-center oscillator and show that the underlying dynamics of this CPG produce bursting behaviors that are well matched to the biological counterpart on which our model is based. In addition, we demonstrate the robustness of the bursting behavior by systematically varying two parameters in each silicon neuron and mapping the corresponding effects on the bursting

Analog VLSI model of the leech heartbeat elemental oscillator

Although significant complexity has been demonstrated in software-based neural system models, it is very difficult to simulate these models near real time. Such real time operation is critical both in the extraction of the maximal information from the models and in the utilization of these models in the creation of artificial systems. Analog very large-scale integrated (aVLSI) circuits have been shown to be a useful medium for implementing real-time neural system models1. Additionally, aVLSI circuits are compact and dissipate little power, facilitating the engineering of artificial systems based on biological principles. Much research has been performed in the aVLSI modeling of early sensory (e.g., visual and auditory) processing 2,3,4. Little application has been made, however, in the modeling of biological motor systems, even though the technology has significant potential in this area.

Work in progress — A hardware platform for a continuous-time signals and systems course

This paper describes a hardware platform that can provide hands-on learning experiences for introductory continuous-time signals and system courses. The signal inputs on the platform consist of a microphone, an instrumentation amplifier input with a right-leg drive for measuring ECGs, and a general voltage input, which enable the students to work with a wide variety of realistic signals. Other analog circuits perform multiplication, addition, filtering, and sampling of those input signals. The underlying hypothesis of this work is that students can improve their understanding of and interest in frequency domain concepts via manipulation of realistic continuous-time systems and observation of signals simultaneously in the time and frequency domains.

Control of bursting properties in a silicon neuron CPG

Abstract We have developed a silicon neuron based on a heart interneuron of the leech. We created a half-center oscillator composed of two silicon neurons connected via inhibitory synapses implemented through dynamic clamp. We investigated the effects of symmetrical variations of maximal conductances on bursting behavior. Burst period, average burst spike frequency, and duty cycle were chosen as major characteristics of the bursting waveform. Burst period and spike frequency showed similar dependencies on the varied parameters in both neurons; duty cycles of the two neurons, however, diverged when the parameters were varied, reflecting mismatch in parameters between chips.

Lessons Learned from Implementing Application-Oriented Hands-On Activities for Continuous-Time Signal Processing Courses [SP Education]

For the past eight years, we have used application-oriented activities in the introductory CTSS course. Such activities can help students to connect the mathematical theory learned in the course to how the theory is applied in real-world applications. The level of detail in the activity needs to be carefully monitored by the instructor to not overwhelm the students and make it easier for the students to connect the theory to the application. Using hardware-based activities can make it easier for students to associate the theory to the application by facilitating real-time cause-effect relationships. The manner in which the application-oriented activities are performed can have a tremendous impact in how well students are able to better understand the theory. This article describes our experiences with using these types of experiments and the lessons learned that increase their effectiveness.

Workshop: Understanding learning difficulties in continuous-time signals and systems courses and making these courses more accesible

The introductory continuous-time signals and systems (CTSS) course is widely considered to be one of the most difficult courses in electrical and computer engineering (ECE) curricula. This interactive workshop explores the sources of difficulty and presents some approaches to help improve learning and understanding. It will begin with a discussion about learning difficulties that are identified by both the participants and the presenters. This discussion will be encouraged and focused through directed questions and presentation of historical data that was gathered at Rose-Hulman. The second part of the workshop will describe hands-on activities that are being done at Bucknell and Rose-Hulman to help address some of the currently perceived learning difficulties. Attendees will have an opportunity to attempt some of these activities, use the technology to develop their own activity, and review the activities with regard to the earlier discussion about learning difficulties. Each attendee is highly encouraged to bring a laptop and a limited number of participants will receive a USB memory stick with the software and sample lesson plans and materials that are currently available.