[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling

By Thomas J. Anastasio

University of Illinois at Urbana-Champaign

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Abstract

In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks

14.1 Neuroinformatics and Molecular Networks

14.2 Enhanced Learning in Neural Networks with Smart Synapses

14.3 Combining Complementary Network Paradigms for Memory Formation

14.4 Smart Synapses and Complementary Rules in Cerebellar Learning

14.5 A Final Word

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Researchers should cite this work as follows:

  • Thomas J. Anastasio (2013), "[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling," https://nanohub.org/resources/18948.

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University of Illinois at Urbana-Champaign, Urbana, IL

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NanoBio Node

University of Illinois at Urbana-Champaign

Tags

  1. NanoBio Node at Illinois
  2. neural networks
  3. Neural Systems Modeling
  4. neuroinformatics
  5. neuroscience
  6. synapses
[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling
  • Tutorial on Neural Systems Modeling 1. Tutorial on Neural Systems Mod… 0
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  • Figure 6.1 A generic, two-layered feedforward neural network 2. Figure 6.1 A generic, two-laye… 22.312282587177236
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  • Figure 6.4 A generic, three-layered feedforward neural network 3. Figure 6.4 A generic, three-la… 67.281574731059735
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  • Figure 14.1 Some of the cellular signaling mechanisms involved in long-term potentiation (LTP) of synapses onto hippocampal pyramidal neurons 4. Figure 14.1 Some of the cellul… 852.99125658045318
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  • Figure 14.1 Some of the cellular signaling mechanisms involved in long-term potentiation (LTP) of synapses onto hippocampal pyramidal neurons 5. Figure 14.1 Some of the cellul… 903.75339894712749
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  • Figure 14.1 Some of the cellular signaling mechanisms involved in long-term potentiation (LTP) of synapses onto hippocampal pyramidal neurons 6. Figure 14.1 Some of the cellul… 907.69933623254758
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  • Figure 14.2 Analyzing the connectivity in the molecular signaling network of a hippocampal pyramidal neuron (Part 1) 7. Figure 14.2 Analyzing the conn… 943.346532387274
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  • Figure 14.2 Analyzing the connectivity in the molecular signaling network of a hippocampal pyramidal neuron (Part 2) 8. Figure 14.2 Analyzing the conn… 1121.3149919890134
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  • Figure 14.2 Analyzing the connectivity in the molecular signaling network of a hippocampal pyramidal neuron 9. Figure 14.2 Analyzing the conn… 1197.023483634699
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  • Figure 14.3 The number of feedback and feedforward loops that are progressively engaged following ligand binding in the hippocampal cell signaling network 10. Figure 14.3 The number of feed… 1209.1956969558253
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  • Figure 14.4 Comparing the number of actual and expected feedback loops engaged following ligand binding in the hippocampal cell signaling network 11. Figure 14.4 Comparing the numb… 1286.2417944609751
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  • Figure 3.19 Simulating the selection of a saccade target as it might occur in the superior colliculus using a winners-take-all recurrent, laterally inhibitory neural network 12. Figure 3.19 Simulating the sel… 1418.2634927901122
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  • Figure 14.5 The number of islands (isolated clusters of connected nodes) decreases as the hippocampal cell signaling model is reconstituted by sequentially adding back nodes of increasing connectivity 13. Figure 14.5 The number of isla… 1505.8097962920576
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  • Figure 14.6 Types and abundance of motifs connecting nodes in the hippocampal cell signaling network (Part 1) 14. Figure 14.6 Types and abundanc… 1612.9520256351568
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  • Figure 14.6 Types and abundance of motifs connecting nodes in the hippocampal cell signaling network (Part 2) 15. Figure 14.6 Types and abundanc… 1677.2908674753946
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  • Figure 14.6 Types and abundance of motifs connecting nodes in the hippocampal cell signaling network (Part 3) 16. Figure 14.6 Types and abundanc… 1708.7246051728084
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  • Figure 14.6 Types and abundance of motifs connecting nodes in the hippocampal cell signaling network 17. Figure 14.6 Types and abundanc… 1742.8335545891507
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  • Figure 14.7 A three-layered network that can learn the exclusive-OR using local learning rules, reinforcement, and smart synapses 18. Figure 14.7 A three-layered ne… 1812.7903410391393
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  • Table 14.1 19. Table 14.1 1870.0398718242161
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  • Figure 14.8 Learning curves for the network that learns the exclusive-OR using local rules, reinforcement, and smart synapses 20. Figure 14.8 Learning curves fo… 2250.6102848500127
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  • Figure 14.9 Illustrating the complementary learning systems model 21. Figure 14.9 Illustrating the c… 2478.2853394555596
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  • Figure 14.10 Performance of the complementary learning systems model with and without interleaved learning 22. Figure 14.10 Performance of th… 2695.12349406685
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  • Figure 14.10 Performance of the complementary learning systems model with and without interleaved learning (Part 1) 23. Figure 14.10 Performance of th… 2737.1429729326196
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  • Figure 14.10 Performance of the complementary learning systems model with and without interleaved learning (Part 2) 24. Figure 14.10 Performance of th… 2789.2303829559028
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  • Figure 14.11 Simulating retrograde amnesia using the complementary network model of memory consolidation 25. Figure 14.11 Simulating retrog… 2962.9428503521885
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  • Figure 14.12 A portrait of David Marr by Nicholas Wade 26. Figure 14.12 A portrait of Dav… 3199.2049617435659
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  • Figure 14.13 Schematic diagram of the Purkinje cell and its connections with neurons of other types in the cerebellum 27. Figure 14.13 Schematic diagram… 3242.5861798450628
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  • Figure 14.13 Schematic diagram of the Purkinje cell and its connections with neurons of other types in the cerebellum (Part 1) 28. Figure 14.13 Schematic diagram… 3270.116157855547
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  • Figure 14.13 Schematic diagram of the Purkinje cell and its connections with neurons of other types in the cerebellum (Part 2) 29. Figure 14.13 Schematic diagram… 3307.5649151130092
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  • Figure 14.14 Diagram illustrating the architecture of the input minimization (InMin) model of cerebellar learning 30. Figure 14.14 Diagram illustrat… 3695.721511378274
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  • Figure 14.15 Pseudo-code for the input minimization (InMin) algorithm 31. Figure 14.15 Pseudo-code for t… 4065.5249459004567
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  • Figure 14.16 Using InMin to simulate cerebellar adaptation of vestibulo-ocular reflex (VOR) amplitude to normal, low, or high levels 32. Figure 14.16 Using InMin to si… 4107.4307055100517
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  • Figure 14.17 Polar plots showing the amplitude and phase of model Purkinje-cell responses before and after VOR adaptation 33. Figure 14.17 Polar plots showi… 4142.3945022288262
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  • Figure 14.18 Polar plots showing the amplitude and phase of real Purkinje-cell responses before and after VOR adaptation 34. Figure 14.18 Polar plots showi… 4177.5927844475591
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  • Figure 14.16 Using InMin to simulate cerebellar adaptation of vestibulo-ocular reflex (VOR) amplitude to normal, low, or high levels 35. Figure 14.16 Using InMin to si… 4217.9708324526709
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  • Figure 14.19 The cell signaling pathways thought to underlie long-term depression (LTD) in the cerebellum 36. Figure 14.19 The cell signalin… 4320.4862567299515
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