Temporal Code: Dynamics in neuronal networks

1. Temporal Code: Dynamics in neuronal networks Alexa Riehle Institut de Neurosciences Cognitives de la Méditerrannée INCM - CNRS Marseille ariehle@lnf.cnrs-mrs.fr

2. It is commonly accepted that perceptual and motor functions are based on distributed processes where neurons do not act in isolation, but organize in functional groups. It is less clear, though, how these networks organize dynamically in space and time to cope with momentary computational demands

3. Neural coding: “rate vs temporal code” Although our knowledge about the morphology and the physiology of thecerebralcortexdrastically increased since the last 30 years, its basic operational modeis still highly controversial debated. rate code temporal code What is a code ? the neuronal representation of information. 1) What ? 2) How ? 3) Which precision ? How do all known components interact to form an efficient working system? How are complex functions such as perception and action realized within neuronal networks? The development of a new paradigm : the concept of "cell assemblies" it is not a matter of an alternative, but a complementary mode

4. Cooperativity in cortical networks “Let us .. assume as the basis of all our subsequent reasoning this law: When two elementary brain processes have been active together or in immediate succession, one of them, on re-occurring, tends to propagate its excitement into the other.” William James (1890) Psychology(Briefer Course)

5. Cooperativity in cortical networks “The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become associated, so that activity in one facilitates activity in the other… This is then the cell assembly. ” Donald O. Hebb (1949) The Organization of Behavior RaphaelLorente de Nó (1937)

6. Cooperativity in cortical networks “Each set (of neurons) excites synchronous firing in the next set, which in turn excites synchronously the next set of neurons, etc. We shall call this arrangement … the synfire chain. .. although the neuron operates as an integrator it is especially sensitive to coincident firing of a few presynaptic sources. ..It is evident that activity within a network of neurons would tend to organize itself in chains of synchronously firing groups..” Moshe Abeles (1982) Local Cortical Circuits

7. Cooperativity in cortical networks “.. (cell assemblies) are dynamic entities, defined … by the ever-changing level of correlation among the activities of their member neurons. … there is no need for the synaptic contacts to be particularly strong: the corresponding connections become effective through synchronous activity with other neurons” Ad Aertsen et al. (1991) Z. Hirnforsch. 6: 735-743

8. Cell assemblies Functional and dynamic units Defined by the synchronization or another precise temporal structure of the discharge Flexibility : each neuron can participate, successively, at differents functional groups

9. Two modes of fonctioning of pyramidal neurons in the cerebral cortex Temporalintegration Coincidence detection integration time long : 5 to 50 msshort : 1 to 5 ms mean interval between spikes shortlong i.e., discharge rate highlow who contributes to the generation of output ? all spikes only synchronous spikes temporal precision irrelevant relevant i.e. relevant information mean discharge rate temporal structure of spikes how many spikes are necessary for producing a spike at the output ? ~300 ~30

10. Synchronization of neuronal activity 1 There are two explanations for synchronous activity : * common inputor * functionalinteraction by means of relatively small neuronal networks A common input modulates simultaneously the discharge patterns of the two neurons; there is, thus, no direct interaction between the two neurons. A functionalinteraction involves a mechanism by which the discharge of one neuron influences the discharge probability of the other.

11. Synchronization of neuronal activity 2 We have to discriminate between * a structuralconnectivity (or an anatomic one) and * a functionalconnectivity (or an efficient one). The first might be described as * stationaryand fixed, whereas the second as * dynamichaving a time constant of modulation in the range of tens to hundreds of milliseconds.

12. Are neurons able to produce action potentiels with a temporal precision in the range of milliseconds ?

13. The temporal precision of neuronal discharge Cat auditory cortex the cat listens during 20 minutes to natural noise Aertsen et al., Biol. Cybernetics 32: 175-185 (1979)

14. The temporal precision of neuronal discharge …. the same tape is repeated Aertsen et al., Biol. Cybernetics 32: 175-185 (1979)

15. The temporal precision of neuronal discharge …. the two records are then cross-correlated Aertsen et al., Biol. Cybernetics 32: 175-185 (1979)

16. The temporal precision of neuronal discharge … a constant electrical stimulus is repeatedly applied, the precision vanishes

17. The temporal precision of neuronal discharge …. a noisy stimulus is applied, discharge is very precise and repetitive

18. Two, three, many electrodes in the brain 20 m

19. Asynchronous action potentials Neuron 1 Neuron 2 Synchronous action potentials Neuron 1 Neuron 2

20. The most basic technique of data analysis The cross-correlogram neuron 1 neuron 2 # coincidences -3 -2 -1 0 1 2 3 time units

21. Cross-correlation and shift predictor Temporal precision : 1-2 ms # of spikes / bin lead / lag (ms)

22. The binding problem The understanding of how neurons co-operate in order to form perceptual or motor representations is one of the major objectives in neurobiology How is individual neuronal activity integrated to form functionally efficient spatio-temporally patterns within networks?

23. Binding by synchrony From: Engel et al, Cerebral Cortex 7: 571-582 (1997)

24. Temporal coding in the visual cortex Freiwald, Kreiter & Singer, NeuroReport 6: 2348-2352 (1995)

25. Temporal coding in the somatosensory cortex Roy & Alloway, J Neurophysiol 81: 999-1013 (1999), Fig. 3

26. Temporal coding in the somatosensory cortex Width of the peak as a function of the stimulus Roy & Alloway, J Neurophysiol 81: 999-1013 (1999), Fig. 9

27. Raw Joint Peri-Stimulus-Time-Histogram (Joint PSTH) Aertsen, Gerstein, Habib, Palm (1989) Dynamics of neuronal firing correlation modulation of "effective connectivity". J. Neurophysiol. 61: 900-917 Aertsen & Gerstein In: Krüger J (ed) Neuronal cooperativity. pp 52-67 (1991)

28. Normalized Joint Peri-Stimulus-Time-Histogram Aertsen & Gerstein In: Krüger J (ed) Neuronal cooperativity. pp 52-67 (1991)

29. Joint Peri-Stimulus-Time-Histogram (Joint PSTH) Aertsen & Gerstein In: Krüger J (ed) Neuronal cooperativity pp 52-67 (1991)

30. Normalized Joint PSTH In two different behavioral conditions recorded in the frontal cortex of the monkey Vaadia et al., Nature 373: 515-518 (1995)

31. Correlation in frontal eye field of the monkey Vaadia et al., Nature 373: 515-518 (1995)

32. (1) Detection of precise spike coincidences : Activity of two simultaneously recorded neurons Riehle et al., Science 278: 1950-1953 (1997)

33. (2) Detection of precise spike coincidences : Synchronous spikes (precision : 2 ms) Riehle et al., Science 278: 1950-1953 (1997)

34. (3) Detection of precise spike coincidences : Measured (red) and expected (black) coincidence rates Riehle et al., Science 278: 1950-1953 (1997)

35. (2) Detection of precise spike coincidences : Synchronous spikes (precision : 2 ms) Riehle et al., Science 278: 1950-1953 (1997)

36. (4) Detection of precise spike coincidences : Statistically significant coincidences ("Unitary Events") Riehle et al., Science 278: 1950-1953 (1997)

37. Multiple single-neuron recordings using 7 independently movable micro-electrodes (Reitböck system, Thomas Recording, Germany)

38. Simple Reaction Time Task One Movement Direction Uncertainty about Signal Occurrence (“Conditional Probability”) Four possible Delay Durations presented at random with equal probability: 600 - 900 - 1200 - 1500 ms Riehle, Grün, Diesmann, Aertsen. Science 278: 1950-1953 (1997)

39. start switch PS 1.RS 2.RS 3.RS 4.RS - 500ms 0 600 900 1200 1500 ms time (ms) Conditional Probability 0.25 0.33 0.5 1

40. Behavioral results Conditional probability: 0.25 0.33 0.5 1 Riehle, Grün, Diesmann, Aertsen. Science 278: 1950-1953 (1997)

41. Spike synchronization in relation to signal expectancy Riehle et al., Science 278: 1950-1953 (1997)

42. Event expectancy with increasing probability Riehle et al., Science 278: 1950-1953 (1997)

43. Synchronization and discharge rate are used in a complementary way by motor cortex no rate modulation rate modulation % of pairs of neurons Internal event External event After Riehle et al., Science 278: 1950-1953 (1997)

44. Multi-directional Pointing Movement: Simple Reaction Time Task No Uncertainty: Prior Information about Direction Fixed Delay: 1000 ms Bastian, Riehle, Erlhagen, Schöner. NeuroReport 9: 315-319 (1998) Grammont, Riehle. Exp. Brain Res. 128: 118-122 (1999) Erlhagen, Bastian, Jancke, Riehle, Schöner. J. Neurosci. Meth. 94: 53-66 (1999) Riehle, Grammont, Diesmann, Grün, J. Physiol (Paris) 94: 569-582 (2000) Bastian, Schöner, Riehle, Eur. J. Neurosci. (2003, in press)

45. Schematic representation of the three main types of neurons recorded in the preparation paradigm Riehle & Requin, 1987 - 1995

46. Assembly formation Discharge rates of two simultaneously recorded neurons The one is preparation-related (purple) and the other rather execution-related (green) Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)

47. Assembly formation Coincidences are detected with a temporal precision of 1 ms Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)

48. Assembly formation Raw (blue) and expected (black) coincidence rates Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)

49. Assembly formation statistical significance : Joint-surprise value Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)

50. Assembly formation Binding by synchrony or "shake-hand neurons": Neurons form an assembly to strengthen the transition from preparation to action Unitary Events Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)