1. John Wordsworth, Peter Ashwin, Gabor Orosz, Stuart Townley Mathematics Research Institute University of Exeter

2. 1. Motivation & Base Model 1.Olfaction, the Honey Bee, Objectives. 2.The Model, Core Dynamics and Spatial Coding. 2. Stimuli and Spatio-Temporal Codes 1.Adding Input and Temporal Coding. 2.Temporal Coding and Spatio-Temporal Codes. 3. Future Work and Uses 1.Other Cases and Introducing Noise. 2.Classifying Inputs from a Spatio-Temporal Code.

3. A brief look at background and the system of coupled phase oscillators at the heart of this work.

4. Honey Bee experiments show neurons in the Glomeruli fire in clusters; C. Galizia, S. Sachse, A. Rappert & R. Menzel, 1999

5. Spatio-Temporal Coding Emulate Olfaction Classify olfactory information in an interesting fashion. Suggest components of combined olfactory encoding. Steady Stimuli Input Coupled Oscillator Model Distinct Coding Output

6. Phase (Ѳ), Natural Frequencies (ω i ), Coupling Strength (K), Number of Oscillators (N), Coupling Function (g), Noise (η), Stimuli (X). Global All-to-All Coupling Phase Oscillator Model

7. Dynamics (with Noise) Synchrony (Alter Alpha) Anti-synchrony Clustering Chaos

8. Synchrony (Alter Alpha) Anti-synchrony Clustering Chaos Dynamics (with Noise)

9. Synchrony Anti-synchrony (Alter Beta) Clustering Chaos Dynamics (with Noise)

10. Synchrony Anti-synchrony Clustering (Alpha:1.7, Beta: -2) Chaos Dynamics (with Noise)

11. After Transients, we have; Yellow ‘Stable’ Cluster. Blue ‘Unstable’ Cluster. One Lone Oscillator. Cluster States (with Noise)

12. What Happens? 1. Initial Transient. 2. First Switches Fast. 3. Residence Time Increases. 4. System Stalls. Note; Considered as a Neural System - the system is still firing! Memory Effect; System does not change unless stimulated. Cluster States (Without Noise)

13. Residence times increase exponentially. The system becomes stalled. We need some form of stimuli to force the system. Cluster States (Without Noise)

14. Time Oscillator 5 Oscillator 4 Oscillator 3 Oscillator 2 Oscillator 1 BBWYY WYYBB YBBYW BWYBY YYBWB Linearizing around a state and taking Eigenvalues; Yellow is ‘stable’; Yellow -> Blue White -> Yellow Blue is ‘unstable’; Blue -> ? Turns out that the ‘fastest’ blue oscillator -> white. Spatial Coding

15. Heteroclinic Network; 30 Cluster States, 60 Orbits. Or a Directed Graph? Sample Coding; BBWYY (S1) WYYBB (S2) YBBYW (S3) BWYBW (S4) A Network of Cluster States

16. System of 5 coupled phase oscillators. Find (2,2,1) ‘clusters’ for certain parameters. Only 2 possible states at the next step once at a given state – which one is random when the system is driven by noise. This generates a system with 30 states and 60 connections. A spatial code can be seen as a series of state identifiers.

17. Next we add input to the System and generate a temporal code which we can combine with our spatial coding.

18. Using different frequencies as input. Detuning Frequencies as Input

19. Where Delta is the Amplitude of Detuning (Strength of Odor?) Uniform Detuning (No Noise) Regular Residence Times, Repeated Pattern.

20. Time Oscillator 5 Oscillator4 Oscillator 3 Oscillator 2 Oscillator 1 Spatial Coding of the Detuned System

21. Reduced State Graph / Spatio-Coding of Data Spatial Coding of the Detuned System

22. Large Amplitude of Detuning (Top), Very Small Amplitude (Bottom) Temporal Coding of the Detuned System

23. Just to prove we’re still thinking about Spiking States are a statement of when spikes are together. Rethink the meaning of residence times A Neuroscience View

24. Post transient residence times are fixed (Red). Evaluation of Residence Times

25. Four ‘levels’ of residence times, not 6 – lost info. Evaluation of Residence Times

26. Direct relation between residence times and δ. Can determine δ from code. Detuning Amplitude vs. Residence Times

27. Original Graph driven by noise; Combined Spatio-Temporal Coding

28. Spatio-Temporal Coding of Detuned Inputs Large Amplitude of Detuning (Top), Very Small Amplitude (Bottom) Spatio-Temporal Coding

29. Detune the frequencies of the oscillators as a method of inputting data to the system. Alters the spatial coding by removing edges that the system can follow (minimal amount remain with uniform detuning). Differences in the magnitudes of frequencies determines residence times (temporal code). Should be able to determine input frequencies from a given spatio-temporal code.

30. Finally, a brief look at work that we are currently focussing on and some applications of the work.

31. Alternative Detunings

32. If we run the system with both Detuning and Bounded Noise, which wins? Compare residence time-windows. Residence Time Case 3 Case 2 Case 1 DetuningNoise Detuning Noise Detuning Detuning & Noise Together

33. Non-Periodic cycles appear when we have noise and certain types of detuning. Have seen cases with 2 potential cycles of 6, in which the system traverses one a seemingly random number of times then the other once. Essentially a sliding scale for number of paths with probability of traversal > 0 between (a) when uniform detuning is far greater than noise and (b) when there is no detuning and just noise. These are the most interesting cases

34. Consider Spatial and Temporal components. Temporal Component will dictate magnitude of differences between natural frequencies. Spatial Component will dictate ordering of natural frequencies. Could be complex for non-uniform detunings.

35. We can generate one of two unique spatio-temporal code / firing pattern for a given input, depending on initial conditions. Non-Uniform detunings -> Other Patterns. Analysis of residence times can tell us whether noise or detuning is driving the system. Data is lost when considering spatial or temporal code alone, but is probably complete using both combined. Conclusion

36. Peter Ashwin, Stuart Townley, Gabor Orosz, University of Exeter Thank You