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 ngnchi002@uct.ac.za jonathan.tapson@uct.ac.za

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: 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)‏ n x (t)‏ m x y(t)‏ n y (t)‏ m y

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 Mathematical Cross Correlation Signals Neural Cross Correlation

12. Proposed Architecture Signal processor CMAC in correlator Based on the functionalities of analog correlators and neurons

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