Information Transfer at Dynamic Synapses: Effects of Short-Term Plasticity

1. Patrick Scott1 Anna Cowan1 Andrew Walker1 Christian Stricker1,2 1Division of Neuroscience, John Curtin School of Medical Research, ANU, Canberra, ACT. 2 ANU Medical School Information Transfer at Dynamic Synapses: Effects of Short-Term Plasticity

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. ModellingExtended mathematical model of short-term plasticity • Phenomenological response to AP • success/failure to release • changes in probability of subsequent release • no channels, Ca2+, 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 1st-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 RDD, spike timing RID, spike timing RDD, rate coding RID, rate coding rec = recovery timescale from RDD U1R = strength of RID

7. Results – Facilitation & FDR Facilitation, spike timing FDR, spike timing Facilitation, rate coding FDR, rate coding U1F = strength of facilitation 1 = strength of FDR

8. Results - Example RDD-dominated, no FDR RID-dominated, with FDR U0 0.4 U1R 0 U1F 0 0 1 s 1 0.2 fac 0 s FDR 2 s rec 500 ms U0 0.4 U1R 0.2 U1F 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.