1. competition degeneracy modularity feedback Elements of robustness:

2. Feedback

3. Controller ~100 ms retinal inputs Goal Feedforward Controller Eyeball + eye movement Sensed Variable feedback A classic example of feedback in neural circuits: error correction during smooth pursuit

4. Degeneracy

5. A classic example of degeneracy in biology: the genetic code

6. Swensen & Bean, J. Neurosci. 2005 cell 1cell 2 Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells acutely dissociated Purkinje somata

7. Swensen & Bean, J. Neurosci. 2005 cell 1 cell 2 cell 3 cell 4 cell 5 cell 6 Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells

8. Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells Swensen & Bean, J. Neurosci. 2005

9. Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells Swensen & Bean, J. Neurosci. 2005 An acute decrease in Na + conductance produces a compensatory increase in voltage- dependent and Ca 2+ –dependent K + conductances.

10. Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells Swensen & Bean, J. Neurosci. 2005

11. Neuron-level degeneracy: robustness of bursting in cerebellar Purkinje cells Swensen & Bean, J. Neurosci. 2005 A chronic decrease in Na + conductance produces a compensatory increase in Ca 2+ conductance.

12. Degeneracy and feedback input output system variables set point homeostat

13. input output system variables set point homeostat Degeneracy and feedback

14. Goldman, Golowasch, Marder, & Abbott, J. Neurosci. 2001 Mapping the state space of neuron-level degeneracy: robustness of bursting in stomatogastric ganglion neurons model stomatogastric ganglion neuron

15. Goldman, Golowasch, Marder, & Abbott, J. Neurosci. 2001 Mapping the state space of neuron-level degeneracy: robustness of bursting in stomatogastric ganglion neurons model stomatogastric ganglion neuron

16. Evolvability - the capacity to adapt by natural selection Evolution - adaptation by natural selection Degeneracy can increase evolvability by distributing system outcomes near phenotypic transition boundaries.

17. Prinz et al. Nature 2004 Circuit-level degeneracy: robustness of patterns in the stomastogastric ganglion data

18. Prinz et al. Nature Neuroscience 2004 Circuit-level degeneracy: robustness of patterns in the stomastogastric ganglion model

19. Competition

20. A classic example of competition in neural circuits: the developing neuromuscular junction Luo & O’Leary, Ann. Rev. Neurosci. 2005

21. Another classic example of competition in neural circuits: developing ocular dominance columns Luo & O’Leary, Ann. Rev. Neurosci. 2005

22. Competitive synaptic interactions: spike-timing dependent plasticity Song & Abbott, Nat. Neurosci. 1999 Abbott, Zoology 2003 pre leads postpre lags post

23. presynaptic rate = 10 Hzpresynaptic rate = 13 Hz Competitive synaptic interactions: spike-timing dependent plasticity Song & Abbott, Nat. Neurosci. 1999 Abbott, Zoology 2003 Homeostatic control of total excitatory drive over a range of presynaptic firing rates.

24. Modularity

25. A classic example of modularity in biology: the domain structure of genes and proteins “Exon shuffling” was recognized early in molecular biology as a potential mechanism to generate diverse novel proteins based on existing functional building-blocks.

26. Bell, Han, & Sawtell, Annu. Rev. Neurosci. 2008 Oertel & Young, Trends Neurosci. 2004 Roberts & Portfors, Biol. Cybern. 2008 Modularity in neural circuits a putative example: “cerebellar-like” circuits

27. Bell, Han, & Sawtell, Annu. Rev. Neurosci. 2008 Oertel & Young, Trends Neurosci. 2004 Roberts & Portfors, Biol. Cybern. 2008 Modularity in neural circuits mammalian cerebellummammalian dorsal cochlear nucleusteleost cerebellum teleost medial octavolateral nucleusmormyrid electrosensory lobegymnotid electrosensory lobe “cerebellar-like” circuits in vertebrates

28. Bell, Han, & Sawtell, Annu. Rev. Neurosci. 2008 Oertel & Young, Trends Neurosci. 2004 Roberts & Portfors, Biol. Cybern. 2008 Modularity in neural circuits common anatomical features of cerebellar-like circuits: large principal cells (often GABAergic) having large spiny dendrites principal cells receive excitatory input from a very large population of granule cells forming parallel axon bundles that target the spiny dendrites of principal cells principal cells also receive excitatory ascending input from sensory regions targeting the perisomatic/proximal region of principal cells common functional features of cerebellar-like circuits: parallel fibers carry “higher-level” information (higher-level sensory signals, corollary discharges, proprioceptive info) ascending inputs by contrast carry lower-level information (pertaining to the same sensory modality or sensorimotor task) parallel fiber signals can in principle “predict” the lower-level signals “prediction” is learned by pairing parallel fiber input with ascending sensory input pairing produces a depression of parallel fiber inputs (anti-Hebbian plasticity)

29. Modularity can permit an organism to process a new input without evolving an entirely novel circuit from scratch—in effect, building diverse objects using existing building-blocks. What “modules” (if any) might be the circuit-level equivalent of protein domains at the molecular level? Sharma, Angelucci, & Sur, Nature 2001 von Melchner, Pallas, & Sur, Nature 2001 Modularity in neural circuits re-routing experiments show that auditory cortex can process visual inputs

30. shorten summary (to ~400 words) add an assessment (probably >300 words) identify major problems, if any identify unusual strengths, if any for each major point, state the implications clearly for each major problem, indicate appropriate solutions