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Spike timing-dependent plasticity: Rules and use of synaptic adaptation

R étroaction lors de l‘ I ntégration V isuelle: vers une A rchitecture GE nérique. Spike timing-dependent plasticity: Rules and use of synaptic adaptation. Rudy Guyonneau Rufin van Rullen and Simon J. Thorpe. Overview. What is STDP anyway ? - Biological evidence

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Spike timing-dependent plasticity: Rules and use of synaptic adaptation

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  1. R étroaction lors de l‘ I ntégration V isuelle: vers une A rchitecture GE nérique Spike timing-dependent plasticity: Rules and use of synaptic adaptation Rudy Guyonneau Rufin van Rullen and Simon J. Thorpe

  2. Overview • What is STDP anyway? • - Biological evidence • - Theoretical studies • - Synthesis • Learning with STDP and asynchronously spiking neurons • - The « controlled » learning paradigm • (theory and biologically plausible implications) • - The « autonomous » learning paradigm • (growing filters and connectivity design)

  3. I. Biological evidence of synaptic adaptation (taken from Markram 1997) (taken from Bi & Poo 1998) « When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency as one of the cell firing B, is increased » [Hebb, 1949] Which fires together wires together 

  4. I. Experimental to functional STDP… … regulatory mechanism for rate and variability of post-synaptic firing … coincidence detector … introduces competition between synapses to control the firing rate (correlation factor) … suppresses strong recurrent excitatory loops « In general, STDP greatly expands the capability of Hebbian learning to address temporally sensitive computational tasks. » (taken from Abbott & Nelson, NatureN, 2000)

  5. I. Modelling stuff (Taken from Gerstner & Kistler, BioCyb, 2002) (Taken from Song et al., NatureN, 2000) STDP can act as a learning mechanism for generating neuronal responses selective to input timing, order and sequence. sequence learning and prediction [Abbott & Blum, 96] spatial path learning in navigation [Blum & Abbott, 96; Mehta, Neuron, 00] direction selectivity in visual responses [Mehta, Neuron, 00] STDP also as « temporal difference learning » [Rao & Sejnowski, NeuralComp, 01]

  6. I. Spike timing-dependent plasticity So what have we got? The neuron’s activity, its spikes history, gives rise to modifications of the efficacy of its excitatory synapses that in turn affects its own spiking behaviour. That means we have synaptic adaptation in the autopoeitic sense.

  7. Spikes reproducibility The retina provides evidence for highly reproducible firing events... [Berry et al., 97] ... And individual spikes with high temporal precision have been reported throughout the ventral stream. [from the retina (Sestokas, 91) to IT (Nakamura, 98); MT (Bair and Koch, 96)] Importantly, the latency of the first spike in the spike train is the most reliable. [Mainen & Sejnowski, 95]  stimulus-locked spike timings are temporally (quite) precise! What does that mean when we consider STDP in the context of reproducible spike trains?

  8. II. What happens when a neuron repeatedly receives the same stimulus? INPUT 1/3 + 1/3 + 1/3 = 1  t3 2/3 + 3/3 = 5/3  t2 1/3 + 2/3 = 1  t2 3/3 = 1  t1 3/3 = 1  t1 3/3 = 1  t1 3/3 = 1  t1 Weight INPUT INPUT INPUT INPUT INPUT INPUT 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 3/3 INPUT INPUT INPUT INPUT INPUT INPUT INPUT Spike time 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 2/3 Weight Weight Weight Weight Weight Weight OUTPUT OUTPUT OUTPUT OUTPUT OUTPUT OUTPUT OUTPUT 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 Weight Weight Weight Weight Weight Weight Weight 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 0/3 Step 2 t0 t0 t0 t0 t0 t0 t0 t0 t0 t0 t0 t0 t0 t0 t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 t3 t3 t3 t3 t3 t3 t3 t3 t3 t3 t3 t3 t3 t3 Spike time Spike time Spike time Spike time Spike time Spike time STDP STDP STDP STDP STDP STDP Step 7 Step 4 Step 3 Step 6 Step 5 Synapses get modified at t1 Synapses get modified at t2 Synapses get modified at t1 Synapses get modified at t3 Synapses get modified at t1 Synapses get modified at t2 Step 2 Step 3 Step 5 Step 4 Step 6 Step 1 Step 1 Spike time Spike time Spike time Spike time Spike time Spike time Spike time • Step n • a wave of spikes elicits a post-synaptic response and triggers STDP-like learning rule • synapses carrying spikes just preceding the post-synaptic one are potentiated. • Step n+1 • Re-propagating the input spike-wave, the latency of the post-synaptic spike is slightly decreased. • synapses carrying even earlier spikes are potentiated and later ones depressed. Reiterate By iterating the processus on and on from an(y) initial state allowing a post-synaptic response, it follows that the earliest synapses get maximally potentiated and later ones are depressed. • Main assumption • Reproducibility of the spike wave (saccades, oscillations).

  9. II. Results Stimulus is composed of … 20Hz reproducible spike trains (Poisson process) by 1000 input neurons … to which a 5ms jitter is added 5Hz spontaneous activity Typical input spike trains Dynamics of the simulation

  10. II. Latency versus… … firing rate … synchronicity (Gerstner, 97) no spontaneous activity – 5ms jitter (Abeles) 5 Hz spontaneous activity - no jitter

  11. II. Selectivity 50000 distractor spike-trains

  12. II. Neuron population case Retina to V1 (preliminary results) « Autonomous learning » Given the reliability and form of retinal input, what happens when a reductive, simili, but still biologically plausible, visual system « experiments » a simili natural world? V1 to V2? (preliminary results) Extension It may be possible that through STDP, areas build their selectivities from correlated afferents.

  13. The end. (taken from vanRullen & Thorpe, 02) (taken from Felleman & vanEssen, 91) • General conclusions • STDP is accountable for adaptation at the cellular level. • Definition of a learning paradigm for asynchronously spiking neuron networks. • Connectivity…

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