The Random Neural Network: the model and some of its applications

1. The Random Neural Network: the model and some of its applications Erol Gelenbe1,2 and Varol Kaptan2 1www.ee.imperial.ac.uk/gelenbe Denis Gabor Professor Head of Intelligent Systems and Networks 2 Dept of Electrical and Electronic Engineering Imperial College London SW7 2BT

2. Prof. Gelenbe is grateful to his PhD students who have worked or are working with him on random neural networks and/or their applications - Univ. of Paris: Andreas Stafylopatis, Jean-Michel Fourneau, Volkan Atalay, Myriam Mokhtari, Vassilada Koubi, Ferhan Pekergin, Ali Labed, Christine Hubert • Duke University: Hakan Bakircioglu, Anoop Ghanwani, Yutao Feng, Chris Cramer, Yonghuan Cao, Hossam Abdelbaki, Taskin Kocak • UCF: Rong Wang, Pu Su, Peixiang Liu, Will Washington, Esin Seref, Zhiguang Xu, Khaled Hussain, Ricardo Lent • Imperial: Arturo Nunez, Varol Kaptan, Mike Gellman, Georgios Loukas, Yu Wang

3. Thank you to the agencies and companies who have generously supported RNN work over the last 15 yrs • France (1989-97): ONERA, CNRS C3, Esprit Projects QMIPS, EPOCH and LYDIA • USA (1993-): ONR, ARO, IBM, Sandoz, US Army Stricom, NAWCTSD, NSF, Schwartz Electro-Optics, Lucent • UK (2003-): EPSRC, MoD, General Dynamics UK Ltd, EU FP6 for grant awards for the next three years, hopefully more ..

4. Outline • Biological Inspiration for the RNN • The RNN Model • Some Applications • modeling biological neuronal systems • texture recognition and segmentation • image and video compression • multicast routing

5. Random Spiking Behaviour of Neurons

6. Random Spiking Behaviour of Neurons

7. Random Spiking Behaviour of Neurons • Work started as an individual basic research project, motivated by a critical look at modeling biological neurons, rather than usingpopular connectionist models • Biological characteristics of the model needed to include: • Action potential “Signals” in the form of spikes of fixed amplitude • Modeling recurrent networks • Random delays between spikes • Conveying information along axons via variable spike rates • Store and fire behaviour of the soma (head of the neuron) • Reduction of neuronal potential after firing • Possibility of representing axonal delays between neurons

8. Random Spiking Behaviour of Neurons • Mathematical properties that we hoped for, but did not always expect to obtain, but which were obtained • Existence and uniqueness of solution to the models • Closed form analytical solutions for large systems • Convergent learning for recurrent networks • Polynomial speed for recurrent gradient descent • Hebbian and reinforcement learning algorithms • Analytical annealing • We exploited the analogy with queuing networks, and this also opened a new chapter in queuing network theory now called “G-networks” where richer models were developed

9. Queuing Networks: Exploiting the Analogy • - Discrete state space, typically continuous time, stochastic models arising in studying populations, dams, production systems, communication networks .. • Important theoretical foundation for computer systems performance analysis • Open (external Arrivals and Departures), as in Telephony, or Closed (Finite Population) as in Compartment Models • Systems comprised of Customers and Servers • Theory is over 100 years old and still very active .. e.g. • Big activity at Bell Labs, AT&T Labs, IBM Research • - More than 100,000 papers on the subject ..

10. Queuing Network <-> Random Neural Network • Both Open and Closed Systems • Systems comprised of Customers and Servers • Servers = Neurons • Customer .. Arriving to server will increase the queue length by +1 • Excitatory spike arriving to neuron will increase its soma’s potential by +1 • Service completion (neuron firing) at server (neuron) will send out a customer (spike), and reduce queue length by 1 • Inhibitory spike arriving to neuron will decrease its soma’s potential by -1 • Spikes (customers) leaving neuron i (server i) will move to neuron j (server j) in a probabilistic manner

12. Mathematical Model: A “neural” network with n neurons • Internal State of Neuron i at time t, is an Integer xi(t)> 0 • Network State at time t is a Vector x(t) = (x1(t), … , xi(t), … , xk(t), … , xn(t)) • If xi(t)> 0, we say that Neuron i is excited and it may fire at t+ (in which case it will send out a spike) • Also, if xi(t)> 0, the Neuron i will fire with probability riDt in the interval [t,t+Dt] • If xi(t)=0, the Neuron cannot fire at t+ When Neuron i fires: - It sends a spike to some Neuron j, w.p. pij - Its internal state changes xi(t+) = xi(t) - 1

13. Mathematical Model: A “neural” network with n neurons The arriving spike at Neuron j is an: - Excitatory Spike w.p. pij+ - Inhibitory Spike w.p. pij - - pij = pij+ + pij- with Snj=1 pij< 1 for all i=1,..,n From Neuron i to Neuron j: - Excitatory Weight or Rate is wij+ = ripij+ - Inhibitory Weight or Rate is wij- = ri pij- - Total Firing Rate is ri= Snj=1 (wij+ + wij–) To Neuron i, from Outside the Network - External Excitatory Spikes arrive at rate Li - External Inhibitory Spikes arrive at rate li

14. State Equations

15. External Arrival Rate of Excitatory Spikes Probability that Neuron i is excited Firing Rate of Neuron i External Arrival Rate of Inhibitory Spikes

17. Random Neural Network • Neurons exchange Excitatory and Inhibitory Spikes (Signals) • Inter-neuronal Weights are Replaced by Firing Rates • Neuron Excitation Probabilities obtained from Non-Linear State Equations • Steady-State Probability is Product of Marginal Probabilities • Separability of the Stationary Solution based on Neuron Excitation Probabilities • Existence and Uniqueness of Solutions for Recurrent Network • Learning Algorithms for Recurrent Network are O(n3) • Multiple Classes (1998) and Multiple Class Learning (2002)

18. Some Applications • Modeling Cortico-Thalamic Response … • Supervised learning in RNN (Gradient Descent) • Texture based Image Segmentation • Image and Video Compression • Multicast Routing

19. Cortico-Thalamic Response to Somato-Sensory Input (i.e. what does the rat think when you tweak her/his whisker?) Input from the brain stem (PrV) and response at thalamus (VPM) and cortex (SI), reprinted from M.A.L. Nicollelis et al. “Reconstructing the engram: simultaneous, multiple site, many single neuron recordings”, Neuron vol. 18, 529-537, 1997

20. Rat Brain Modeling with the Random Neural Network

22. Network Architecture from Physiological Data

23. Comparing Measurements and Theory:Calibrated RNN Model and Cortico-Thalamic Oscillations Predictions of Calibrated RNN Mathematical Model (Gelenbe & Cramer ’98, ’99) Simultaneous Multiple Cell Recordings (Nicollelis et al., 1997)

24. Oscillations Disappear when Signaling Delay in Cortex is Decreased

25. Thalamic Oscillations Disappear when Positive Feedback from Cortex is removed

26. When Feedback in Cortex is Dominantly Negative, Cortico-Thalamic Oscillations Disappear

27. Summary of findings

28. Gradient Computation for the Recurrent RNN is O(n3)

29. Texture Based Object Identification Using the RNN, US Patent ’99 (E. Gelenbe, Y. Feng)

30. 1) MRI Image Segmentation

31. MRI Image Segmentation

32. Brain Image Segmentation with RNN

33. Extracting Abnormal Objects from MRI Images of the Brain Separating Healthy Tissue from Tumor Simulating and Planning Gamma Therapy & Surgery Extracting Tumors from MRI T1 and T2 Images

34. 2) RNN based Adaptive Video Compression:Combining Motion Detection and RNN Still Image Compression RNN

35. Neural Still Image CompressionFind RNN R that Minimizes|| R(I) - I ||Over a Training Set of Images {I }

36. RNN based Adaptive Video Compression

40. 3) Analytical Annealing with the RNN: Multicast Routing(Similar Results with the Traveling Salesman Problem) • Finding an optimal many-to-many communications path in a network is equivalent to finding a Minimal Steiner Tree which is an NP-Complete problem • The best heuristics are the Average Distance Heuristic (ADH) and the Minimal Spanning Tree (MSTH) for the network graph • RNN Analytical Annealing improves the number of optimal solutions found by ADH and MST by approximately 10%

41. Selected References E. Gelenbe, ``Random neural networks with negative and positive signals and product form solution,'' Neural Computation, vol. 1, no. 4, pp. 502-511, 1989. E. Gelenbe, ``Stability of the random neural network model,'‘Neural Computation, vol. 2, no. 2, pp. 239-247, 1990. E. Gelenbe, A. Stafylopatis, and A. Likas, ``Associative memory operation of the random network model,'' in {\it Proc. Int. Conf. Artificial Neural Networks}, Helsinki, pp. 307-312, 1991. E. Gelenbe, F. Batty, ``Minimum cost graph covering with the random neural network,'' Computer Science and Operations Research, O. Balci (ed.), New York, Pergamon, pp. 139-147, 1992. E. Gelenbe, ``Learning in the recurrent random neural network,'‘ Neural Computation, vol. 5, no. 1, pp. 154-164, 1993. E. Gelenbe, V. Koubi, F. Pekergin, ``Dynamical random neural network approach to the traveling salesman problem,'' Proc. IEEE Symp. Syst., Man, Cybern., pp. 630-635, 1993. A. Ghanwani, ``A qualitative comparison of neural network models applied to the vertex covering problem,'' Elektrik, vol. 2, no. 1, pp. 11-18, 1994. E. Gelenbe, C. Cramer, M. Sungur, P. Gelenbe ``Traffic and video quality in adaptive neural compression'', Multimedia Systems, Vol. 4, pp. 357--369, 1996. …

42. Selected References …  C. Cramer, E. Gelenbe, H. Bakircioglu ``Low bit rate video compression with neural networks and temporal subsampling,'' Proceedings of the IEEE, Vol. 84, No. 10, pp. 1529--1543, October 1996.  E. Gelenbe, T. Feng, K.R.R. Krishnan ``Neural network methods for volumetric magnetic resonance imaging of the human brain,'' Proceedings of the IEEE, Vol. 84, No. 10, pp. 1488--1496, October 1996.  E. Gelenbe, A. Ghanwani, V. Srinivasan, ``Improved neural heuristics for multicast routing,'' IEEE J. Selected Areas in Communications, vol. 15, no. 2, pp. 147-155, 1997.  E. Gelenbe, Z. H. Mao, and Y. D. Li, ``Function approximation with the random neural network,'' IEEE Trans. Neural Networks, vol. 10, no. 1, January 1999.  E. Gelenbe, J.M. Fourneau ``Random neural networks with multiple classes of signals,'' Neural Computation, vol. 11, pp. 721--731, 1999. E. Gelenbe, E. Seref, and Z. Xu, ``Simulation with learning agents’’ Proceedings of the IEEE, 89 (2) pp.148-157 (2001) E. Gelenbe, K. Hussain ``Learning in the multiple class random neural network’’, IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1257—1267, 2002. E. Gelenbe, R. Lent and A. Nunez ``Self-aware networks and QoS ‘’,Proceedings of the IEEE, 92( 9) pp. 1479-1490 (2004)