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Thanks to: Alex Loebel, Omri Barak, Asher Uziel, Henry Markram

Information encoding and processing via spatio-temporal spike patterns in cortical networks Misha Tsodyks , Dept of Neurobiology, Weizmann Institute, Rehovot, Israel. Thanks to: Alex Loebel, Omri Barak, Asher Uziel, Henry Markram. Rate coding (V1). Y. Prut, …, M. Abeles 1998.

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Thanks to: Alex Loebel, Omri Barak, Asher Uziel, Henry Markram

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  1. Information encoding and processing via spatio-temporal spike patterns in cortical networksMisha Tsodyks, Dept of Neurobiology, Weizmann Institute, Rehovot, Israel Thanks to: Alex Loebel, Omri Barak, Asher Uziel, Henry Markram

  2. Rate coding (V1)

  3. Y. Prut, …, M. Abeles 1998

  4. W. Bair & C. Koch 1996

  5. DeWeese, …, Zador 2003

  6. Open questions: How do precise spike patterns emerge in the cortex? How can they be robust in the presence of random firing of surrounding neurons? What is the relation between the spike patterns and the stimuli that they are coding for? How can the information carried by spike patterns be processed?

  7. Open questions: How do precise spike patterns emerge in the cortex? How can they be robust in the presence of random firing of surrounding neurons? (Synfire chains? – I don’t like it!) What is the relation between the spike patterns and the stimuli that they are coding for? How can the information carried by spike patterns be processed?

  8. Recurrent networks with dynamic synapses (unstructured) Tsodyks et al 2000

  9. Wang Yun et al 1998

  10. Modeling Time-Dependent Release 4 Synaptic Parameters • Absolute strength • Probability of release • Depression time constant • Facilitation time constant

  11. Population spikes

  12. Population spikes

  13. Origin of Population Bursts

  14. Temporal Correlations

  15. Network response to stimulation

  16. Simplified model (no inhibition, uniform connections, rate equations) i J J

  17. The rate equations • Two sets of equations representing the excitatory units firing rate, E, and their depression factor, R : Loebel & Tsodyks 2002

  18. Population spikes in the simplified model

  19. Adiabatic approximation (except during the population spike)

  20. Adiabatic approximation (except during the population spike) Population spike:

  21. Adiabatic approximation Population spike: Higher spontaneous activity – lower propensity for population spikes.

  22. Response to excitatory pulses Inputs: Response: Population spike No population spike

  23. Inputs: Response: Population spike No population spike

  24. Inputs: Response: Population spike No population spike

  25. Response to tonic stimuli The tonic stimuli is represented by a constant shift of the {e}’s, that, when large enough, causes the network to burst and reach a new steady state

  26. Interaction between stimuli

  27. Open questions: How do precise spike patterns emerge in the cortex? (Synfire chains?) How can they be robust in the presence of random spontaneous and evoked firing of surrounding neurons? What is the relation between the spike patterns and the stimuli that they are coding for? How can the information carried by spike patterns be processed?

  28. Extended model Loebel & Tsodyks 2006

  29. The model response to a ‘pure tone’

  30. Constraining the propagation of the PS along the map

  31. Forward suppression Rotman et al, 2001

  32. Network response to complex stimuli

  33. Network response to complex stimuli

  34. Open questions: How do precise spike patterns emerge in the cortex? (Synfire chains?) How can they be robust in the presence of random spontaneous and evoked firing of surrounding neurons? What is the relation between the spike patterns and the stimuli that they are coding for? How can the information carried by spike patterns be processed?

  35. Processing spike patterns: Tempotron (Guetig and Sompolinsky, 2006) Learned patterns vs background patterns Barak & Tsodyks, 2006

  36. Variance-based learning where

  37. Cost function for learning

  38. Learning rules for spatio-temporal patterns Gradient descent: Correlation-based:

  39. Convergence of learning

  40. Performance of the tempotron

  41. Measuring the tempotron performance

  42. Robustness to time warps

  43. Conclusions 1. Networks with synaptic depression can encode spatio-temporal inputs by precise spike patterns. 2. Random spontaneous activity could play a crucial role in setting the sensitivity of the network to sensory inputs (top-down control, attention, expectations, …?) 3. Coding by spike patterns is highly nonlinear. 4. Effective learning rules for recognition of spike patterns in tempotron-like networks can be derived.

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