1. How facilitation influences an attractor model of decision making Larissa Albantakis

2. Outline 1.Facilitation and depression in cortical neurons How does it work and what is it good for? 2. Facilitation and working memory 3. Facilitation and decision making Sequential decision making Parallel decision making 4. Outlook Lunch

3. Neurophysiological mechanism Facilitation: APs arriving in the presynaptic terminal cause the accumulation of calcium ions.  increase of released vesicles/AP From: Wang Y et al. 2006 Idea: - docked pool of vesicles containing neurotransmitter - each released vesicle is replaced with a time constant τ d. - intracellular calcium responsible for release probability of vesicles Synaptic depression: The neuron fires more rapidly than vesicles can be replaced.  Vesicle depletion

4. Possible benefits of facilitation and depression  Synaptic Filter (low-, high- or band-pass)  Decorrelation  Burst detection  Stabilize against noise (Gain Control) Persistent activity: Depression: Persistent activity can be maintained with relatively low firing rates (e.g. 15 Hz). Facilitation: May increase the strength of connections above a critical threshold necessary for the stability of persistent firing.  Brief periods of synaptic activity may transiently shift connected cortical neurons into a state in which recurrent excitation is sufficiently strong to support persistent activity.

5. Mostly depression in the cortex? Special properties of PFC: Heterogeneity of synaptic dynamics (40-50% facilitation dominant). Facilitation is often masked by high initial probability of release. Augmentation with time constants of several seconds. Short-term synaptic depression between pyramidal neurons has been observed in all cortical areas that have been examined (mainly V1, sensory areas). From: Wang Y et al. 2006

6. Why in PFC? – Working memory!

7. Synaptic Theory of Working Memory Mongillo et al. (2008) Science 319, 1543  Memory is maintained by short-term synaptic facilitation. (mediated by increased residual calcium levels at the presynaptic terminals) x: available resources (vesicles) u: fraction of resources used by each spike (residual calcium level) τ F = 1.5 s >> τ D = 0.2 s …Object Working memory dominated by recurrent inhibition Brunel and Wang (2001) J Comp Neurosci 11, 63-85  Working memory based on persistent activity maintained through recurrent NMDA synapses with long time constants. (Synapses are permanently enhanced by previous long-term potentiation)

8. F-D spiking network integrate-and-fire neurons (inhibitory and excitatory) structured network (p selective populations each encoding one memory item) E-E connections weights are multiplied by u(t)·x(t) (0 > ux > 1) From: Mongillo et al. 2008  Different regimes of working memory depending on background input

9. F-D spiking network From: Mongillo et al. 2008 Memory maintained synaptically Memory maintained by persistent firing

10. Temporary vs. permanent synaptic enhancement Advantages of strengthening synapses temporarily: metabolically more efficient (conserve synaptic resources during periods of baseline activity) greater flexibility and control (synaptic facilitation is activity dependent and can vary in a graded fashion) regulation of persistent activity by altering presynaptic release probability through neuromodulators (e.g. dopamine) Quiescent memory  decoupling from other brain areas

11. So many advantages… why not try in decision making?

12. Facilitation and sequential decision making Task: Comparison of two vibrotactile stimuli applied sequentially Experimental findings: “partially differential neurons” in VPC respond to f 1, show no persistent activity, but ramp up at the end of the delay period and are influenced by f 2 during the decision period Problem: Model of Wang 2002 (attractor network of two competing populations) does not work in sequential decision making Deco et al. in preparation f 1 > f 2 f 1 < f 2

13. Facilitation and sequential decision making Deco et al. in preparation f1f1 f2f2 delay Specific input Global attention signal

14. Facilitation and sequential decision making During application of f 2, the level of activity will depend on the synaptic history (u·f 1 ) and on f 2. f 2 f 1 f 2 f 1 network modelexperiment spikes/s Deco et al. in preparation Together with the neurons that encode just f 2, a decision of f 1 f 2 can be reached with a standard attractor model of decision making. f 1 > f 2 f 1 < f 2

15. Facilitation and parallel decision making Albantakis & Deco 2009 Task: Gather evidence (dot motion) and decide between multiple targets  Facilitation: Network performance and decision dynamics are mainly preserved. But: greater flexibility in weights!

16. Outlook Idea: Use facilitation (or combination of facilitation and depression) for experimental findings that are currently not completely accounted for by the multiple choice model without facilitation:  Log Odds (coherence-dependent difference between in and out choice)  Extend the multiple choice model to more than 4 choices  …

17. Thanks… … to my supervisor Prof.Gustavo Deco … Consolider and … to everybody for listening!

18. Facilitation and parallel decision making