1. Neural Cross-Correlation For Radio Astronomy
Chipo N Ngongoni
Supervisor: Professor J Tapson
Department of Electrical Engineering, University of Cape Town
Rondebosch, 7701, South Africa

2. Neural Cross-Correlation For Radio Astronomy

3. Outline Description of Neural Computation Outline of Research
Relevance to SKA
Recommendations and Conclusions
Future work

4. Neural Computation...
Modeling of systems according to human brain response and neural system
Neuron behavior
Evolution: McCulloch and Pitts(1943),Minsky and Papert(1969), perceptrons
Modeling- mathematical,hardware and software

5. Outline of Research
ANNs- successful in signal processing in areas in need of computational efficiency
Wireless communications, biomedical prosthetics, pattern and speech recognition
Analysis of the auto/cross correlation functions
Interpretation using neurons

6. The Selected Model Conductance-based Integrate-and-fire model:
Integrate-and-fire membrane potential:
𝑣(𝑡 = 𝑚+𝜉(𝑡)+𝑔(𝑡)𝑑𝑡
drift
noise
signal

7. The Selected Model Equivalent circuit model
Leaky integrator which resets at hysteretic comparator thresholds

8. The Selected Model
Proposed Neural cross correlation basic unit and ISIH
x(t)‏
nx(t)‏ mx
y(t)‏
ny(t)‏ my

9. The Selected Model
Simulated result of membrane potential without any stimulating signal
ρ(τ) = interspike interval density (with drift and noise only)‏

10. The Selected Model
Simulated result of membrane potential without any stimulating signal
With sine input:
ρ(τ)(1 + f(t))‏

11. Model Results Cross correlation Neural Cross Correlation Signals
Mathematical
Cross Correlation

12. Proposed Architecture
Signal processor CMAC in correlator
Based on the functionalities of analog correlators and neurons N BU
Vxl
Vxh
x(t)‏
y(t)‏
Vyh
Vyl Rb

13. Problem areas- Proposed solutions
Problem:ASIC not reconfigurable
Solution: Field Programmable Analog Arrays(FPAA), FPGA,SoICs
FPGA and FPAA configurations of neural models already introduced

14. FPAAs Analog equivalent of FPGAs-Anadigm,Motorola
CAB- incorporate switched capacitor banks, CMOS operational amplifier, comparator, CMOS switches and SRAM.

15. Relevance to SKA
Alternative algorithm/method for correlation-Digital Hardware and Software correlators
Bandwidth expansion and not restriction (CBI:WASP2.. : A.I Harris and J Zmuidzinas)
Comparison of Analog Continuum Correlators for remote sensing and Radio Astronomy: Koistinen et al‏
Cost

16. Conclusions Neural Computation: what it offers
Mixed signal perspective , reconfigurable
Low power usage
Diverse neural architecture for parallel or serial processing
Counter dominates power consumption
SKA relevance:Diversity of applications not limited by the signal processing techniques