Artificial Spiking Neurons and Analog-to-Digital-to-Analog Conversion

نویسندگان

  • Hiroyuki Torikai
  • Aya Tanaka
  • Toshimichi Saito
چکیده

This paper studies encoding/decoding function of artificial spiking neurons. First, we investigate basic characteristics of spiketrains of the neurons and fix parameter value that can minimize variation of spike-train length for initial value. Second we consider analog-to-digital encoding based upon spike-interval modulation that is suitable for simple and stable signal detection. Third we present a digital-to-analog decoder in which digital input is applied to switch the base signal of the spiking neuron. The system dynamics can be simplified into simple switched dynamical systems and precise analysis is possible. A simple circuit model is also presented. key words: spiking neurons, analog-to-digital converters, digital-toanalog converters

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving adaptive resolution of analog to digital converters using least squares mean method

This paper presents an adaptive digital resolution improvement method for extrapolating and recursive analog-to-digital converters (ADCs). The presented adaptively enhanced ADC (AE-ADC) digitally estimates the digital equivalent of the input signal by utilizing an adaptive digital filter (ADF). The least mean squares (LMS) algorithm also determines the coefficients of the ADF block. In this sch...

متن کامل

Digital Spiking Silicon Neuron: Concept and Behaviors in GJ-coupled Network

Silicon neuron is electrical circuit that is analogous to biological neurons. Most spiking silicon neurons comprise analog circuit technology. We propose a new concept of spiking silicon neuron that is composed of only digital circuit technology. The system equations were designed by a mathematical-model-based design method that we proposed for analog silicon neurons in previous works. This all...

متن کامل

Efficient Computation in Adaptive Artificial Spiking Neural Networks

Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of communication. This contrasts sharply with biological neurons that communicate sparingly and efficiently using...

متن کامل

Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity.

We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits. At the single-cell level, we use custom-designed analog circuits (ASICs) that simulate two types of spiking neurons based on Hodgkin-Huxley like dynamics: "regular spiking" excitatory neurons with spike-frequency adaptation, and "fast spiking" inhibitory neurons. Synaptic interaction...

متن کامل

On the E ect of Analog Noise in Discrete - Time

We introduce a model for analog computation with discrete time in the presence of analog noise that is exible enough to cover the most important concrete cases, such as noisy analog neural nets and networks of spiking neurons. This model subsumes the classical model for digital computation in the presence of noise. We show that the presence of arbitrarily small amounts of analog noise reduces t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • IEICE Transactions

دوره 91-A  شماره 

صفحات  -

تاریخ انتشار 2008