1. 1 Spiking neuron models of the basal ganglia: dopaminergic modulation of selection and oscillatory properties Kevin Gurney, Mark Humphries, Rob Stewart Adaptive Behaviour Research Group University of Sheffield, UK

2. 2 Rationale: basal ganglia and action selection Aim: to understand underlying function of basal ganglia. While learning is crucial – what is being learned? Hypothesis: Main computational role of basal ganglia is to perform action selection Supported by high (systems) level model Simple leaky integrators to represent population dynamics BUT…..

3. 3 Beyond the systems level Do more realistic models support the selection hypothesis? Constraints provided by: Specific neuronal properties Physiological phenomena displayed by BG in toto…. If the price of a model performing selection is its failure exhibit these phenomena, the selection hypothesis is in question In particular, can models display oscillatory phenomena in BG? If so, then we can use the model to explore possible function of these oscillations Function or artifact?!

4. 4 Systems level – the model architecture cf, Hazrati and Parent, 1992, Mink and Thach 1993, Nambu et al 2000, Sato et al 2000 Assumes relatively diffuse projection from STN Emphasises STN’s role as input nucleus striatumSTN Cortex (‘salience’ input) output nuclei - + Striatum input STN output Diffuse projection 3 ‘channels’

5. 5 New functional architecture: selection and control pathways Interpret GP efferents as control signals for modulating selection pathway Gurney et al, 2001 Selection pathwayControl pathway Striatum (D2)Striatum (D1)STN EP/SNr GP Cortex/thalamus Diffuse projection

6. 6 Oscillations in basal ganglia: matching mechanisms to phenomena Basal ganglia display a wide range of oscillatory phenomena – from 100Hz These are probably associated with a correspondingly wide range of underlying mechanisms We focus on four BG features. Intrinsic nature of STN-GP coupling Dopaminergic modulation of this coupling Rebound bursting in STN Synaptic patterning

7. 7 Constructing the model

8. 8 Rebound bursting in STN Time Current IKIK Beurrier et al 1999 ILIL ITIT I K > I L burst ends inhibition

9. 9 Importance of synaptic patterning Inhibition at soma or proximal dendrites acts divisively (rather than ‘subtractively’) 70% of GPe input is proximal or somatic (Bevan et al 1997) cortexSTN GP Proximal dendrites soma Distal dendrites Captured phenomenlogically: use inhibition in proximal dendrites/soma to explicitly ‘gate’ more distal input

10. 10 Dopaminergic action in striatum Increased PSP decreased PSP W = W 0 (1 + λ) W = W 0 (1 - λ) λ < 1

11. 11 Dopaminergic action in STN D2 W = W 0 (1 – k 1 λ)W = W 0 (1 – k 2 λ) K 1, k 2 < 1 Similar story in GP…

12. 12 Dopamine: hypotheses Low levels of dopamine serve to couple STN and GP more tightly and to make STN more sensitive to its input Dopamine in striatum will make channel selection easier to achieve

13. 13 Model neurons: summary Leaky Integrate and Fire with AMPA NMDA, GABA, synaptic currents Shunting inhibition at proximal dendrites and soma Spontaneous currents Rebound bursting in STN, Dopamine in striatum, STN and GP. Inter-neuronal delays All of the above parametrised by best estimates from the literature

14. 14 Network Based on systems level model 3 discrete channels 64 neurons per channel, per nucleus Probabilistic connection scheme within channels (only 25% of all possible connections made)

15. 15 Constraining phenomena 1: Low frequency oscillations in STN-GP (Magill et al, Neuroscience,106, 2001) Low frequency oscillations (LFOs) in STN are driven by cortical slow wave under urethane anaesthesia. GP does not oscillate in control (normal DA) conditions. Only shows oscillation under dopamine depletion (6-OHDA lesion) Residual LFOs (with 6-OHDA lesion) in STN and GP under cortical ablation

16. 16 Data – STN control

17. 17 Model - STN control Pseudo-eeg

18. 18 Data – GP control

19. 19 Model - GP control (1)

20. 20 Model GP control (2)

21. 21 Data – STN DA-depleted

22. 22 Model STN DA-depleted

23. 23 Data – GP DA-depleted (in phase)

24. 24 Model - GP DA-depleted (in-phase)

25. 25 Data – GP DA-depleted (anti-phase)

26. 26 Model - GP DA-depleted (anti-phase)

27. 27 Data – cortical ablation and DA-depleted Most neurons do not show LFOs but residual LFO activity…

28. 28 Model - no cortex (DA-depleted) STN GP

29. 29 LFO counts In DA control conditions, no GP LFOs, STN driven by cortex LFO in GP promoted by DA depletion Residual LFO in STN & GP under cortical ablation Neuron is LFO if significant peak in power spectrum below 1.5Hz

30. 30 Mean firing rates

31. 31 LFO – mechanistic explanation Low frequency oscillations associated with rebound bursting will be ‘unmasked’ at low levels of dopamine…. GP more likely to generate pre-conditioning hyperpolarisation

32. 32 Constraining phenomena 2: gamma oscillations in STN (Brown et al., Exp Neuro. 177, 2002) There is gamma oscillation (40- 80Hz) in alert rats This is increased (86% mean) by systemic D2 agonist (quinpirole) Local field potential spectrum (control)

33. 33 Model simulated D2 agonist DA=0.2 control DA=0.8 ‘D2 agonist’ 128% power increase → Mean power spectra (192 neurons) Peaks in power spectrum

34. 34 Gamma oscillations: explanation Gamma oscillations are associated with the natural frequency of oscillation of the GP-STN circuit determined by circuit delays At control levels of dopamine, the presence of some LFO masks gamma Can’t be doing gamma during quiet phase of LFO period. At higher levels of dopamine, gamma is unmasked

35. 35 Selection experiments Cortical input (Mean firing rate) time ch1 ch2 1 2.5

36. 36 Selection and switching Ctx Ch1: 20 Hz Ctx Ch2: 40 Hz Time Mean firing rate SNr ch2ch1 ch3 ctx time ch1 ch2 1 2.5 Firing rate

37. 37 DA depletion prevents selection Ctx Ch1: 12 Hz Ctx Ch2: 20 Hz Time Mean firing rate SNr Firing rate ctx time ch1 ch2 1 2.5 ch2ch1 ch3 LFOs?

38. 38 Effects of DA depletion overcome by highly salient action Time Mean firing rate SNr Ctx Ch1: 20 Hz Ctx Ch2: 40 Hz Firing rate ctx time ch1 ch2 1 2.5 ch2ch1 ch3

39. 39 DA increase results in simultaneous selection Ctx Ch1: 20 Hz Ctx Ch2: 40 Hz Time Mean firing rate SNr Firing rate ctx time ch1 ch2 1 2.5 ch2ch1 ch3

40. 40 Summary A spiking model of BG constrained by known physiology is able to account for a range oscillatory phenomena Oscillations are modulated under Dopaminergic control of STN and GP The same model displays selection and switching properties, thereby supporting the selection hypothesis for BG function Currently exploring computational role of LFOs Perturb BG to selection in otherwise unresolved selection competition?

41. 41 The adaptive behaviour research group Peter Redgrave Paul Overton Kevin Gurney Tony Prescott Mark Humphries Ben Mitchinson Rob Stewart Ric Wood Jonathan Chambers Tom Stafford Ψ

42. 42 Simulation – basic selection salience EP/SNr output Selection threshold Compare with data from monkey SNr in behavioural task Schultz 1986 Results support selection hypothesis

43. 43 GP-STN coherency