Simbrain 3.0: A flexible, visually-oriented neural network simulator

Neural Networks

Volumn 83, Issue , 2016, Pages 1-10

  Tosi, Zachary ^a   Yoshimi, Jeffrey ^b  

^a INDIANA UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Education; Network visualization; Neural networks; Simulator

Indexed keywords

EDUCATION; EDUCATION COMPUTING; NEURAL NETWORKS; SIMULATORS; STUDENTS;

CUSTOMIZABLE; INTUITIVE INTERFACES; NETWORK VISUALIZATION; NEURAL NETWORK DESIGNS; NEURAL NETWORK SIMULATORS;

COMPLEX NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ANALYSIS; COMPUTER INTERFACE; COMPUTER PROGRAM; PRIORITY JOURNAL; SIMULATION; SIMULATOR; SYNAPSE; SYNAPTIC TRANSMISSION; EDUCATION; HUMAN; INFORMATION SCIENCE; SOFTWARE;

HUMANS; INFORMATICS; NEURAL NETWORKS (COMPUTER); SOFTWARE;

EID: 84982168458     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2016.07.005     Document Type: Article

References (48)

1. Adl-Tabatabai, Ali-Reza, Cierniak, Michał, Lueh, Guei-Yuan, Parikh, Vishesh~M., Stichnoth, James~M., Fast, effective code generation in a just-in-time java compiler. ACM SIGPlAN notices, vol. 33, 1998, ACM, 280–290.
2. Aisa, Brad, Mingus, Brian, O'Reilly, Randy, The emergent neural modeling system. Neural networks 21:8 (2008), 1146–1152.
3. Alvarez, Pablo, Squire, Larry~R., Memory consolidation and the medial temporal lobe: a simple network model. Proceedings of the National Academy of Sciences 91:15 (1994), 7041–7045.
4. Bekolay, Trevor, Bergstra, James, Hunsberger, Eric, DeWolf, Travis, Stewart, Terrence~C., Rasmussen, Daniel, Choo, Xuan, Voelker, Aaron~Russell, Eliasmith, Chris, Nengo: a python tool for building large-scale functional brain models. Frontiers in neuroinformatics, 7, 2013.
5. Braitenberg, Valentino, Vehicles: Experiments in synthetic psychology. 1986, MIT press.
6. Brette, Romain, Gerstner, Wulfram, Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of neurophysiology 94:5 (2005), 3637–3642.
7. Brette, Romain, Goodman, Dan~F.M., Simulating spiking neural networks on gpu. Network: Computation in Neural Systems, 2012.
8. Executorservice. (java platform se 8) - oracle help center (2016).
9. Elman, Jeffrey~L., Finding structure in time. Cognitive science 14:2 (1990), 179–211.
10. FitzHugh, Richard, Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal, 1(6), 1961, 445.
11. Gewaltig, Marc-Oliver, Diesmann, Markus, NEST (neural simulation tool). Scholarpedia, 2(4), 2007, 1430.
12. Gilson, Matthieu, Fukai, Tomoki, Stability versus neuronal specialization for STDP: long-tail weight distributions solve the dilemma. PLoS One 6:10 (2011), 1–18.
13. Gleeson, Padraig, Steuber, Volker, Silver, R. Angus, Crook, Sharon, NeuroML. Computational systems neurobiology, 2012, Springer, 489–517.
14. Goetz, Brian, Peierls, Tim, Java concurrency in practice. 2006, Pearson Education.
15. Goodman, Dan F.M., Brette, Romain, The brian simulator. Frontiers in neuroscience, 3, 2008, 26.
16. Hebb, Donald~Olding, The organization of behavior: A neuropsychological theory. 1949, Wiley, New York.
17. Hodgkin, Allan~L., Huxley, Andrew~F., Currents carried by sodium and potassium ions through the membrane of the giant axon of loligo. The Journal of physiology 116:4 (1952), 449–472.
18. Hopfield, John~J., Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences 79:8 (1982), 2554–2558.
19. Hotton, Scott, Yoshimi, Jeff, Extending dynamical systems theory to model embodied cognition. Cognitive Science 35:3 (2011), 444–479.
20. Ito, Shinya, Hansen, Michael~E., Heiland, Randy, Lumsdaine, Andrew, Litke, Alan~M., Beggs, John~M., Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PloS one, 6(11), 2011, e27431.
21. Izhikevich, Eugene~M., et al. Simple model of spiking neurons. IEEE Transactions on neural networks 14:6 (2003), 1569–1572.
22. Jaeger, Herbert (2001). The echo state approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 148:34.
23. Jaeger, Herbert, Maass, Wolfgang, Principe, Jose, Special issue on echo state networks and liquid state machines. Neural Networks 20:3 (2007), 287–289.
24. Java bindings for opencl. (2015). http://www.jocl.org/. Accessed 05.10.15.
25. Kohonen, Teuvo, The self-organizing map. Neurocomputing 21:1 (1998), 1–6.
26. Krenek, Jiri, Kuca, Kamil, Krejcar, Ondrej, Maresova, Petra, Sobeslav, V., Blazek, Pavel, Artificial neural network tools for computerised data modeling and processing. Computational intelligence and informatics (CINTI), 2014 IEEE 15th international symposium on, 2014, IEEE, 255–260.
27. Lazar, Andreea, Pipa, Gordon, Triesch, Jochen, SORN: a self-organizing recurrent neural network. Frontiers in computational neuroscience, 3, 2009.
28. Lewis, J.P., Neumann, Ulrich, Performance of java versus c++. 2003, University of southern California.
29. Maass, Wolfgang, Natschläger, Thomas, Markram, Henry, Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation 14:11 (2002), 2531–2560.
30. Markram, Henry, Wang, Yun, Tsodyks, Misha, Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences 95:9 (1998), 5323–5328.
31. Miller, Kenneth~D., MacKay, David J.C., The role of constraints in hebbian learning. Neural Computation 6:1 (1994), 100–126.
32. Morris, Catherine, Lecar, Harold, Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 1981, 193.
33. Naka, K.I., Rushton, W.A.H., S-potentials from colour units in the retina of fish (cyprinidae). The Journal of Physiology 185:3 (1966), 536–555.
34. Norton, David, Ventura, Dan, Improving liquid state machines through iterative refinement of the reservoir. Neurocomputing 73:16 (2010), 2893–2904.
35. Oancea, Bogdan, Rosca, Ion~G.H., Andrei, Tudorel, Iluzia Iacob, Andreea, Evaluating java performance for linear algebra numerical computations. Procedia Computer Science 3 (2011), 474–478.
36. Oja, Erkki, Simplified neuron model as a principal component analyzer. J. Math. Biol. 15:3 (1982), 267–273.
37. Paleczny, Michael, Vick, Christopher, Click, Cliff, The java hotspot tm server compiler. Proceedings of the 2001 symposium on Java TM virtual machine research and technology symposium-volume 1, 2001, USENIX Association pp. 1–1.
38. Peirce, Jonathan~ W., The potential importance of saturating and supersaturating contrast response functions in visual cortex. Journal of vision, 7(6), 2007, 13.
39. Rinzel, John, Ermentrout, G. Bart, Analysis of neural excitability and oscillations. Methods in neuronal modeling 2 (1998), 251–292.
40. Roth, A., & van Rossum, M.C.W. (2009). Computational modeling methods for neuroscientists.
41. Rumelhart, David E., Hinton, Geoffrey E., & Williams, Ronald J. (1985). Learning Internal Representations by Error Propagation. Technical Report, DTIC Document.
42. Rumelhart, David~ E., McClelland, James~ L., et al., PDP~Research Group. Parallel distributed processing, Vol. 1, 1988, IEEE.
43. Schreiber, Thomas, Measuring information transfer. Physical Review Letters, 85(2), 2000, 461.
44. Sun Developer Network. (2007). The java hotspot performance engine architecture. Sun Microsystem.
45. Sjöström, J., Gerstner, W., Spike-timing dependent plasticity. Scholarpedia, 5(2), 2010, 1362 revision 142314.
46. Taboada, Guillermo L., Ramos, Sabela, Expósito, Roberto R., Touriño, Juan, Doallo, Ramón, Java in the high performance computing arena: research, practice and experience. Science of Computer Programming 78:5 (2013), 425–444.
47. Yoshimi, Jeff, Simbrain: A visual framework for neural network analysis and education. Brains, Minds, and Media, 3, 2008.
48. Yoshimi, Jeff (2014). Narrowing the explanatory gap with bridge metaphors.