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A modular latching chain

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Abstract

Many cognitive tasks involve transitions between distinct mental processes, which may range from discrete states to complex strategies. The ability of cortical networks to combine discrete jumps with continuous glides along ever changing trajectories, dubbed latching dynamics, may be essential for the emergence of the unique cognitive capacities of modern humans. Novel trajectories have to be followed in the multidimensional space of cortical activity for novel behaviours to be produced; yet, not everything changes: several lines of evidence point at recurring patterns in the sequence of activation of cortical areas in a variety of behaviours. To extend a mathematical model of latching dynamics beyond the simple unstructured auto-associative Potts network previously analysed, we introduce delayed structured connectivity and hetero-associative connection weights, and we explore their effects on the dynamics. A modular model in the small-world regime is considered, with modules arranged on a ring. The synaptic weights include a standard auto-associative component, stabilizing distinct patterns of activity, and a hetero-associative component, favoring transitions from one pattern, expressed in one module, to the next, in the next module. We then study, through simulations, how structural parameters, like those regulating rewiring probability, noise and feedback connections, determine sequential association dynamics.

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References

  • Abeles M (1982) Local cortical circuits: an electrophysiological study. Springer, Berlin

    Book  Google Scholar 

  • Amati D, Shallice T (2007) On the emergence of modern humans. Cognition 103:358–385

    Article  PubMed  Google Scholar 

  • Amit DJ (1989) Modeling brain function: the world of attractor neural networks. Cambridge University Press, New York

    Book  Google Scholar 

  • Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 6:512–523

    Article  Google Scholar 

  • Braitenberg V (1978) Cortical architectonics: general and areal. In: Bazier PH MAB (ed) Architectonics of the cerebral cortex. Raven press, New York, pp 443–465

  • Fulvi-Mari CC, Treves A (1998) Modeling neocortical areas with a modular neural network. Biosystems 48:47–55

    Article  CAS  PubMed  Google Scholar 

  • Gerth S, beim Graben P (2009) Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser. Cognitive Neurodynamics 3(4):297–316

    Article  PubMed Central  PubMed  Google Scholar 

  • Guo D, Li C (2010) Self-sustained irregular activity in 2-d small-world networks of excitatory and inhibitory neurons. IEEE Trans Neural Netw 21:895–905

    Article  CAS  PubMed  Google Scholar 

  • Hauser M, Chomsky N, Fitch WT (2002) The language faculty: what is it, who has it, and how did it evolve? Science 298:1569–1579

    Article  CAS  PubMed  Google Scholar 

  • James W (1892) The stream of consciousness. Psychology

  • Kanter I (1988) Potts-glass models of neural networks. Phys Rev A 37:2739–2742

    Article  PubMed  Google Scholar 

  • Koechlin E, Summerfield C (2007) An information theoretical approach to prefrontal executive function. Trends Cogn Sci 11:229–235

    Article  PubMed  Google Scholar 

  • Kropff E, Treves A (2005) The storage capacity of potts models for semantic memory retrieval. J Stat Mech Theory Exp 8:P08010

    Google Scholar 

  • Kropff E, Treves A (2007) The complexity of latching transitions in large scale cortical networks. Nat Comput 2:169–185

    Article  Google Scholar 

  • Lansner A, Fransén E, Sandberg A (2003) Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory. Theory Biosci 122(1):19–36

    Google Scholar 

  • Li Y, Tsuda I (2013) Novelty-induced memory transmission between two nonequilibrium neural networks. Cogn Neurodyn 7(3):225–236

    Article  Google Scholar 

  • Mountcastle VB (1998) Perceptual neuroscience: the cerebral cortex. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Pulvermüller F, Knoblauch A (2009) Discrete combinatorial circuits emerging in neural networks: a mechanism for rules of grammar in the human brain? Neural Netw 22:161–172

    Article  PubMed  Google Scholar 

  • Roxin A, Riecke H, Solla SA (2004) Self-sustained activity in a small world network of excitable neurons. Phys Rev Lett 92(19)198101:1–4

    Google Scholar 

  • Russo E, Namboodiri VM, Treves A, Kropff E (2008) Free association transitions in models of cortical latching dynamics. New J Phy 10(015008):1–19

    Google Scholar 

  • Russo E, Pirmoradian S, Treves A (2010) Associative latching dynamics vs syntax. Adv Cognit Neurodyn(II) 111–115

  • Russo, E, Treves, A (2012) Cortical free association dynamics: distinct phases of a latching network. Phys Rev E 85(5)051920:1–18

    Google Scholar 

  • Seyed-Allaei S, Amati D, Shallice T(2010) Internally driven strategy change. Think Reason 16(4):308–331

    Article  Google Scholar 

  • Treves A (2005) Frontal latching networks: a possible neural basis for infinite recursion. Cogn Neuropsychol 22(3-4):276–291

    Article  PubMed  Google Scholar 

  • Tsodyks M, Feigelman M (1998) The enhanced storage capacity in neural networks with low activity level. Europhys Lett 6(2):101–105

    Article  Google Scholar 

  • Watts D, Strogatz S (1998) Collective dynamics of ’small-world’ networks. Science 286:509–512

    Google Scholar 

  • Wennekers T, Gunther P (2009) Syntactic sequencing in Hebbian cell assemblies. Cogn Neurodyn 3:429–441

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

S.S. thanks Eleonora Russo and Emilio Kropff for earnest and warmhearted assistance in sharing the idea of latching dynamics. S.S. and H.Y. are grateful for the support of the National Natural Science Foundation of China (61071180).

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Correspondence to Alessandro Treves.

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Song, S., Yao, H. & Treves, A. A modular latching chain. Cogn Neurodyn 8, 37–46 (2014). https://doi.org/10.1007/s11571-013-9261-1

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  • DOI: https://doi.org/10.1007/s11571-013-9261-1

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