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Computational neuroethology: linking neurons, networks and behavior Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign.

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Presentation on theme: "Computational neuroethology: linking neurons, networks and behavior Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign."— Presentation transcript:

1 Computational neuroethology: linking neurons, networks and behavior Mark E. Nelson Beckman Institute Univ. of Illinois, Urbana-Champaign

2 TALK OUTLINE Multiscale modeling in computational neuroethology Model system - weakly electric fish Modeling strategies Level I:Behavior Level II:Sensory physics Level III:Single neurons Level IV:Local networks Summary

3 Multiscale Organization of the Nervous System Organism Brain/CNS Networks Neurons Synapses Molecules Brain maps 1 m 10 cm 1 mm 100  m 1  m 1 Å 1 cm Churchland & Sejnowski 1988Delcomyn 1998

4 Neuroethology: Neural Basis of Behavior Environment Delcomyn 1998 SensorsEffectors Organism Sensory Processing Motor Control Neural Integration Brain Body

5 Neuroethology of Electrolocation Big picture: What are the neural mechanisms and computational principles of active sensing? Small picture: How do weakly electric fish capture prey? What computations take place in the CNS during prey capture behavior?

6 BACKGROUND Weakly Electric Fish

7 Distribution of Electric Fish

8 Black ghost knifefish ( Apteronotus albifrons )

9 mechano MacIver, from Carr et al., 1982 Electroreceptors ~15,000 tuberous electroreceptor organs 1 nerve fiber per electroreceptor organ up to 1000 spikes/s per nerve fiber

10 Ecology & Ethology of A. albifrons inhabits tropical freshwater rivers and streams in South America nocturnal; hunts at night for aquatic insect larvae and small crustaceans in turbid water uses electric sense for prey detection, navigation, social interactions ribbon fin propulsion – forward/reverse/hover

11 Self-generated Electric Field

12 Principle of active electrolocation

13 Prey-capture Behavior Daphnia magna (water flea) 1 mm

14 BEHAVIOR Electrosensory-mediated Prey capture behavior

15 Prey-capture video analysis

16 Prey capture behavior

17 Fish Body Model

18 Motion capture software

19 MOVIE: prey capture behavior

20 Rapid reversal marks putative time-of-detection Velocity Profile (N=116) Acceleration Profile (N=116) Zero-crossing in acceleration is used as detection time

21 Distribution of detection points Front viewSide view

22 Active motor strategies: Dorsal roll toward prey

23 Neuroethology: Neural Basis of Behavior Environment Delcomyn 1998 SensorsEffectors Organism Sensory Processing Motor Control Neural Integration Brain Body

24 PHYSICS of electrosensory image formation

25 Electrosensory Image Reconstruction

26 Voltage perturbation at skin  : Estimating Daphnia signal strength electrical contrast prey volume fish E-field at prey distance from prey to receptor THIS FORMULA CAN BE USED TO COMPUTE THE SIGNAL AT EVERY POINT ON THE BODY SURFACE

27

28 Reconstructed Electrosensory Image (  )

29 Electrosensory Images

30 ELECTROPHYSIOLOGY of primary sensory afferents

31 mechano MacIver, from Carr et al., 1982 Electroreceptors ~15,000 tuberous electroreceptor organs 1 nerve fiber per electroreceptor organ

32 Neural coding in electrosensory afferent fibers

33 Probability coding (P-type) afferent spike trains 00010101100101010011001010000101001010 P head = 0.333  P head  = 0.337 P head = 0.333

34 Model of primary afferents Brandman & Nelson Neural Comp. 14, 1575-1597 (2002)

35 ELECTROPHYSIOLOGY of CNS electrosensory neurons

36 ELL Circuitry

37 ELL histology

38 Compartmental Modeling

39 Hodgkin-Huxley Model for voltage-dependent conductances

40 Compartmental Modeling Hodgkin-Huxley Model for voltage-dependent conductances

41 ELL pyramidal cell

42 ELECTROPHYSIOLOGY of electrosensory networks

43 Central Processing in the ELL

44 Spatiotemporal processing in 3 parallel ELL maps Primary Electrosensory Afferents Centromedial map Space: small RFs Time: low-pass Centrolateral map Space: med. RFs Time: band-pass Lateral map Space: large RFs Time: high-pass temporal integration both spatial integration

45 Multiresolution filtering in the CNS

46 Neuroethology: Neural Basis of Behavior Environment Delcomyn 1998 SensorsEffectors Organism Sensory Processing Motor Control Neural Integration Brain Body

47 Acknowledgements Malcolm MacIver Noura Sharabash Relly Brandman Jozien Goense Rama Ratnam Rüdiger Krahe Ling Chen Kevin Christie Jonathan House NIMH and NSF


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