Unconventional forms of plasticity: beyond the synaptic Hebbian paradigm

1. Unconventional forms of plasticity: beyond the synaptic Hebbian paradigm

2. Origin of synaptic plasticity: Donald Hebb • 1949, Hebb's PhD Thesis: " When an axon of cell A [...] persistently takes part in firing cell B, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased" • Major points: • long-lasting changes (trace, memory) • associativity (A & B must both fire) • locality (depends only on A & B) • Major issue: can only increase

3. Evidence for long-term potentiation (LTP) • Came out in the early 1970's (Bliss & Lomo J Physiol 1973) • High Frequency stimulations (HFS) in the hippocampus From Fino et al. J Neurosci 2005 A stimulation B maintains for hours-days ≈200 pulses ≈100 Hz

4. Evidence for long-term depression (LTD) • Came out in the early 1990's (Dudek & Bear PNAS 1992) • Low Frequency stimulations (LFS) in the hippocampus From Fino et al. J Neurosci 2005 A stimulation B maintains for hours-days ≈1000 pulses ≈1 Hz

5. pre pre Transition to spikes and spike-timings post post • Initiated in late 1990's (Markram et al. Science 1997; Bi & Poo J Neurosci 1998) • LTP or LTD depending on the timing between pairs of post- and pre-synaptic spikes Bi & Poo J Neurosci 1998 A (pre) 1 s B (post) pre post Δt = tpost-tpre < 0 Δt = tpost-tpre > 0 stimulation Δt maintains for hours ≈100 paired pulses@ ≈1 Hz

6. Molecular basis (@ glutamate synapses) VB @ soma (mV)  LTP Vthr Vrest time from Citri & Malenka Neuropsychopharmacology 2008 tA tA tA tB  LTD

7. Is that all our brain does? • Most computational models with plastic / learning synapses use rules derived from Hebbian LTP/LTD or STDP. • Yet there is more than this going on in our brains: • Anti-Hebbian plasticity

8. Is that all our brain does? • Most computational models with plastic / learning synapses use rules derived from Hebbian LTP/LTD or STDP. • Yet there is more than this going on in our brains: • Anti-Hebbian plasticity • Relaxed locality • Neuromodulation (e.g. dopamine, serotonin) • Glia-neuron interactions

9. Is that all our brain does? • Most computational models with plastic / learning synapses use rules derived from Hebbian LTP/LTD or STDP. • Yet there is more than this going on in our brains: • Anti-Hebbian plasticity • Relaxed locality • Neuromodulation (e.g. dopamine, serotonin) • Glia-neuron interactions • Nonassociative rules : • synaptic scaling • short-term facilitation

10. Is that all our brain does? • Most computational models with plastic / learning synapses use rules derived from Hebbian LTP/LTD or STDP. • Yet there is more than this going on in our brains: • Anti-Hebbian plasticity • Relaxed locality • Neuromodulation (e.g. dopamine, serotonin) • Glia-neuron interactions • Nonassociative rules : • synaptic scaling • short-term facilitation • Intrinsic plasticity

11. Outline • STDP: evolution with the number of paired stimulations • Intrinsic long-term plasticity of the threshold or gain • mechanical origins • competition with synaptic Hebbian learning in chaotic recurrent networks • Short term plasticity • modulation by glial cells: the tripartite synapse

12. STDP : EVOLUTIOn with the number of paired stimulationswith B. Delord and L. Venance LABS

13. STDP in the basal ganglia • BG = motor control, procedural memory, goal oriented tasks. • The striatum is the major input nucleus for cortical input cortico-striatal synapses from Fino & Venance Front Synaptic Neurosci 2010

14. Bi & Poo J Neurosci 1998 pre pre STDP in the basal ganglia @ 100 stims post post • STDP in at cortico-striatal synapses is Anti-Hebbian Fino et al. J Neurosci 2005 stimulation synaptic weight 1 s pre post Δt Δt = tpost-tpre < 0 Δt = tpost-tpre > 0 ≈100 paired pulses, ≈1 Hz

15. Decreasing the paired stimulation count Cui et al., submitted

16. LTP re-emerges at low stim. counts: model signaling by AMPAR and NMDAR Retrograde signaling by endocannabinoids Cui et al., submitted

17. Low-stims LTP is endocannabinoid (eCB) Full Model eCB-KO Model Experimental confirmation Cui et al., submitted

18. eCB-Low stims LTP also exists in the cortex Cui et al., submitted

19. A novel form of LTP • "Classical" NMDAR-mediated LTP and LTD disappear with fewer stimulations • But an endocannabinoid-mediated LTP emerges for 5-20 pairings • First evidence for endocannabinoid-mediated potentiation • Ongoing work: • variations of the frequency and stochasticity of Δt • effects in a network (# stims, time scales) • A possible cellular support for rapid learning: • Associative memories and behavioral rules can be learned within a few or even a single trial (Pasupathy et al. Nature 2005; Tse et al. Science 2007). • This would represent as few as 2-50 spikes • eCB-low stims LTP may code such rapid learnings

20. Intrinsic plasticity of the threshold or gainwith B. Delord LAB

21. Plasticity of the f-Icurve in the cortex firing frequency f Injected current I "Transfert function" Paz et al. J Physiol 2009

22. Plasticity of the f-Icurve in the cortex firing frequency f Injected current I "Transfert function" stimulation Paz et al. J Physiol 2009

23. Plasticity of the f-Icurve in the cortex • No change in synaptic weights : INTRINSIC PLASTICITY Paz et al. J Physiol 2009

24. Modulations of voltage-gated channels • What happense if other voltage-dependent ionic channels (ie not synaptic AMPAR and NMDAR) are also modulated by neural activity? Synapticplasticity Intrinsicplasticity INaT, INaP INMDAIAMPA ICa pyr pyr Ih IK ICa IKCa ???????? ICaT INaP INaT INa

25. A generic Hodgkin-Huxley model leak spike injected current (soma) generic voltage-gated ion channel X gX = total quantity of X: ?? Firing frequency of the neuron f IX properties 1/ 0 injected current Iinj Naudé et al., submitted

26. Increasing gX changes the f-I • IP expected with modulation of stiff-activating channels • Channels activating before spike: plasticity of the f-I threshold θ • Channels activating at or after spike: plasticity of the f-I inverse slope ε Naudé et al., submitted

27. Homeostatic IP of the threshold • A single neuron with Homeostatic IP of the threshold - - - firing frequency Desaturation of neuron i firing frequency fi Input from the reservoir 0 Naudé et al., submitted Input of neuron i

28. H-IP of the threshold in mixed networks • Recurrent neural networks (firing rate) with initial chaotic dynamics + Hebbian synaptic learning (Siri et al. J Physiol 2007; Neural Computation 2008) • +/- H-IP of the threshold ? Network dynamics @ 103 learning steps SP+IP chaotic Largest Lyapunov exponent Network-averaged firing SP (no IP) non chaotic Learning steps Naudé et al., submitted

29. H-IP of the threshold in mixed networks • Functional property: sensitivity to the input : Δ[x] = [network dynamics w/ input - network dynamics w/o input] Δ[x] SP+IP SP (no IP) Network-averaged firing Learning steps Naudé et al., submitted

30. H-IP of the threshold in mixed networks • Functional property: sensitivity to the input : Δ[x] = [network dynamics w/ input - network dynamics w/o input] Δ[x] SP+IP SP+IP SP (no IP) Network-averaged firing Learning steps Naudé et al., submitted

31. Conclusions • Intrinsic plasticity • is to be expected when the number of stiff-activating voltage-dependent ionic channels is regulated by the neuron activity • nature of changes (θ or ε) depends on if the channel opens before or during the spike. • H-IP may prevents some of the runaway effects of synaptic plasticity (neuronal saturation, simplification of dynamics) • Ongoing work • Non-homeostatic IP ? + - -

32. Modulation of short-term plasticity by glial cellswith E. Ben JACOB LAB

33. Short-term plasticity (STP) Paired-pulse plasticity • Recycling glutamate takes time: τrec≈ 0.5-1 sec • Restoring basal Ca levels after a presyn spike takes time: τf ≈ 1-2 sec • Maintains short-term only (0.1 – few sec) • STP crucially conditions information transfert from pre to post (Tsodyks & Markram PNAS 1997; Abbott et al Science 1997) glutamate + Ca2+ synaptic weight (% control) 100 facilitation depression presyn spike frequency

34. Glial cells modulate STP • Glial cells (astrocytes) ≈ 50 % of human brain • Not only mechanical/feeding support to neurons: • communicate with each other over long distances through slow calcium waves (Goldberg et al. PLoS Comp Biol 2010; De Pittà et al. J Biol Phys 2009) • associate with synapses into tripartite synapses • bidirectional comunication between presynaptic, postynaptic and astrocytes • Modulation of STP by glial cells has been evidenced but confusing: facilitation, depression, or both (Robitaille Neuron 1998; Jourdain et al., Nature Neurosci 2007)? Haydon Nature Rev Neurosci 2001

35. A model of glia-modulated STP De Pitta et al. PLoS Comp Biol 2011

36. Mean-field theory of the system potentiation syn. weight depression syn. weight De Pitta et al. PLoS Comp Biol 2011

37. Confirmation by simulations = = syn. weight syn. weight syn. weight syn. weight De Pitta et al. PLoS Comp Biol 2011

38. Conclusion ≈10 m/s neuron • Astrocytes dynamically switch synapses between depressing and potentiating modes • Astrocytes can decrease global synaptic weight while increasing paired-pulse potentiation (and vice-versa) (cf Jourdain et al., Nature Neurosci 2007) • The plasticity characteristics of a synapse may not be fixed but could be modulated by associated astrocytes. • Ongoing work: • effect on the postsynaptic terminal • modulation of long-term plasticity (STDP) • Perspectives: assembly of a mixed glia/neuron network glia ≈10 µm/s ≈10 m/s

39. SUMMARY

40. Unconventional plasticity • The "Unconventional computation" CS community: • "enrich or go beyond the standard models, such as the von-Neumann computer architecture and the Turing machine, which have dominated computer science for more than half a century" • Unconventional plasticity: even though the synaptic Hebbian framework prevails, outside-the-box plasticity forms do exist and it may be worth looking at them for learning & memory. • Expands the (limited?) framework of Hebbian synaptic plasticity: • time-scale-dependent plasticity ("quick learning" LTP) • homeostatic maintenance of dynamic regimes (intrinsic plasticity) • dynamic modulation of the synaptic operating regime (depressing/potentiating)

41. Thanks!!! for computer ressources • Funding : • Contributions: • Sch. Physics & Astronomy, Tel Aviv Univ, Israel • M. Goldberg • M. De Pittà • E. Ben Jacob • ISIR, Univ P&M Curie, Paris France • J. Naudé • B. Delord • S. Genet • Salk Institute, San Diego, USA • V. Volman • INSERM U1050 • Collège de France, Paris • Y. Cui • E. Fino • L. Venance • Project-Team Beagle, INRIA, Lyon, France • J. Lallouette • J.M. Gomès • H. Berry

42. More information • 2011 Meeting of the Society for Neuroscience (Washington, Nov 12-16) • "Sub-second induction unveils a switch from NMDA- to endocannabinoid-LTP", abstract # 348.04 • "Astrocyte regulation of presynaptic plasticity", abstract # 663.10 • Published paper: • De Pittà, Volman, Berry & Ben Jacob (2011) A tale of two stories: astrocyte regulation of synaptic depression and facilitation, PLoS Comput Biol (in press). • http://www.inrialpes.fr/Berry/ • Questions?

44. Is that all our brain does? • Most computational models with plastic / learning synapses use rules derived from Hebbian LTP/LTD or STDP. • Yet there is more than this going on in our brains: • Anti-Hebbian plasticity: • Relaxed locality • Neuromodulation (e.g. dopamine, serotonin) • Glia-neuron interactions • Nonassociative rules : • synaptic scaling • short-term facilitation A1+A2=3 A1/A2=1/3 A1+A2=6 A1/A2=2 A1+A2=3 A1/A2=2 A1 A2 B LTP Scaling

45. Short-term plasticity (STP) • Recycling glutamate takes time: τrec≈ 0.5-1 sec • Restoring basal Ca levels after a presyn spike takes time: τf ≈ 1-2 sec • Maintains short-term only (0.1 – few sec) presyn spikes glutamate + Ca2+ presyn Ca τf postsyn potential time

46. Short-term plasticity (STP) • Recycling glutamate takes time: τrec≈ 0.5-1 sec • Restoring basal Ca levels after a presyn spike takes time: τf ≈ 1-2 sec • Maintains short-term only (0.1 – few sec) presyn spikes glutamate + Ca2+ presyn Ca τf potentiation (facilitation) postsyn potential time

47. Short-term plasticity (STP) • Recycling glutamate takes time: τrec≈ 0.5-1 sec • Restoring basal Ca levels after a presyn spike takes time: τf ≈ 1-2 sec • Maintains short-term only (0.1 – few sec) presyn spikes glutamate + Ca2+ presyn Ca τrec depression postsyn potential time

48. Short-term plasticity (STP) • STP crucially conditions information transfert from pre to post (Tsodyks & Markram PNAS 1997; Abbott et al Science 1997) glutamate + Ca2+ synaptic weight (% control) 100 facilitation depression presyn spike frequency