1. Information Transfer at Dynamic Synapses: Effects of Short-Term Plasticity Patrick Scott 1 Anna Cowan 1 Andrew Walker 1 Christian Stricker 1,2 1 Division of Neuroscience, John Curtin School of Medical Research, ANU, Canberra, ACT. 2 ANU Medical School

2. Background Probability of neurotransmitter release changes according to previous activity Four major short-term effects: – Release-dependent depression (depletion; RDD) – Release-independent depression (RID) – Facilitation – Frequency-dependent recovery (FDR) How do they affect information transfer? Nobody knows… (yet)

3. Modelling Extended mathematical model of short-term plasticity Phenomenological response to AP success/failure to release changes in probability of subsequent release no channels, Ca 2+, etc. Previously RDD+F, deterministic (Fuhrmann et al 2002, J Neurophysiol 87:140) RDD+RID+FDR, quasi-stochastic (Fuhrmann et al 2004, J Physiol 557:415) Now RDD+RID+FDR+F, fully stochastic 4 coupled 1 st -order ordinary differential equations, with an explicit (iterative) solution

4. Parameter Estimation Fitted models to EPSCs from paired recordings in Layers IV/V of rat somatosensory cortex (N = 11) Simultaneous fits to different stimuli, EPSCs/variances Defined typical ‘facilitating’ and ‘depressing’ connection parameters Reduced  2 values all < 1 (i.e. good fits)

5. Information Measurement Generated 5.4 hours of synthetic data for each parameter combination, as postsynaptic APs with an integrate-and- fire model Measured information transfer using information theory; entropy (Strong et al 1998, Phys Rev Lett 80:197) Includes extrapolations to infinite data size and window length For single vesicle and network configurations => spike timing and rate- coding dominated.

6. Results – RDD & RID  rec = recovery timescale from RDD U 1R = strength of RID RDD, spike timingRID, spike timing RDD, rate codingRID, rate coding

7. Results – Facilitation & FDR U 1F = strength of facilitation  1 = strength of FDR Facilitation, spike timingFDR, spike timing Facilitation, rate codingFDR, rate coding

8. Results - Example RDD-dominated, no FDRRID-dominated, with FDR U 0 0.4 U 1R 0 U 1F 0  0 1 s  1 0.2  fac 0 s  FDR 2 s  rec 500 ms U 0 0.4 U 1R 0.2 U 1F 0  0 1 s  1 0.2  fac 0 s  FDR 2 s  rec 5 ms Spike Timing:11.49 bits/s Rate Coding:1.84 bits/s Spike Timing:27.31 bits/s Rate Coding:1.86 bits/s

9. Outcomes Information transfer by spike timing goes with release probability, not so for rate-coded information. – RDD: spike timing ↓, rate unaffected – RID: spike timing ↓, rate ↓ – Facilitation: spike timing ↑, rate ↓ – FDR: spike timing ↑, rate ↑ or ↓ with other parameters

10. Speculation Shows how brain can use alternative coding schemes and different dynamic processes to achieve varying goals at different network levels. Possible applications to neural prosthetics, neural electronics and artificial neural networks.