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Probabilistically segregated neural circuits and subcritical linguistics

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Abstract

Early studies of cortical information codes and memory capacity have assumed large neural networks, which, subject to evenly probable binary (on/off) activity, were found to be endowed with large storage and retrieval capacities under the Hebbian paradigm. Here, we show that such networks are plagued with exceedingly high cross-network connectivity, yielding long code words, which are linguistically non-realistic and difficult to memorize and comprehend. Noting that the neural circuit activity code is jointly governed by somatic and synaptic activity states, termed neural circuit polarities, we show that, subject to subcritical polarity probability, random-graph-theoretic considerations imply small neural circuit segregation. Such circuits are shown to represent linguistically plausible cortical code words which, in turn, facilitate storage and retrieval of both circuit connectivity and firing-rate dynamics.

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References

  • Amari S (1972) Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Trans Comput 21:1197–1206

    Article  Google Scholar 

  • Amit DJ, Gutfreund H, Sompolinsky H (1987) Statistical mechanics of neural networks near saturation. Ann Phys 173:30–67

    Article  Google Scholar 

  • Atwood HL, Wojtowicz JM (1999) Silent synapses in neural plasticity: current evidence. Learn Mem 6:542–571

    Article  CAS  Google Scholar 

  • Baram Y (2013) Global attractor alphabet of neural firing modes. J Neurophys 110:907–915

    Article  Google Scholar 

  • Baram Y (2017a) Developmental metaplasticity in neural circuit codes of firing and structure. Neural Netw 85:182–196

    Article  Google Scholar 

  • Baram Y (2017b) Asynchronous segregation of cortical circuits and their function: a life-long role for synaptic death. AIMS Neurosci 4(2):87–101

    Article  Google Scholar 

  • Baram Y (2018) Circuit polarity effect of cortical connectivity, activity, and memory. Neural Comput 30(11):3037–3071

    Article  Google Scholar 

  • Baram Y, Sal’ee D (1992) Lower bounds on the capacities of binary and ternary networks storing sparse random vectors. IEEE Trans Inf Theory 38(6):1633–1647

    Article  Google Scholar 

  • Bienenstock EL, Cooper LN, Munro PW (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci 2:32–48

    Article  CAS  Google Scholar 

  • Bollobás B (1984) The evolution of random graphs. Trans Am Math Soc 286(1):257–274

    Article  Google Scholar 

  • Bonzon P (2017) Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications. Cogn Neurodyn 11(4):327–353

    Article  Google Scholar 

  • Carandini M, Ferster D (2000) Membrane potential and firing rate in cat primary visual cortex. J Neurosci 20:470–484

    Article  CAS  Google Scholar 

  • Carlsson A, Lindquist MA (1963) Effect of chlorpromazine and haloperidol on formation of 3-methoxytyramine and normetanephrine in mouse brain. Acta Pharmacol Toxicol 20:140–144

    Article  CAS  Google Scholar 

  • Cooper L, Intrator N, Blais BS, Shouval HZ (2004) Theory of cortical plasticity. World Scientific, Hackensack

    Book  Google Scholar 

  • Drachman DA (2005) Do we have brain to spare? Neurology 64(12):2004–2005

    Article  Google Scholar 

  • Erdős P, Rény A (1959) On random graphs I (PDF). Publ Math 6:290–297

    Google Scholar 

  • Erdős P, Rény A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61

    Google Scholar 

  • Gerstner W (1995) Time structure of the activity in neural network models. Phys Rev E 51:738–758

    Article  CAS  Google Scholar 

  • Groves PM, Wilson CJ, Young SJ, Rebec GV (1975) Self-inhibition by dopaminergic neurons: an alternative to the “neuronal feedback loop” hypothesis for the mode of action of certain psychotropic drugs. Science 190:522–528

    Article  CAS  Google Scholar 

  • Han Y, Kebschull JM, Campbell RAA, Cowan D, Imhof F, Zador AM, Mrsic-Flogel TD (2018) The logic of single-cell projections from visual cortex. Nature 556:51–56

    Article  CAS  Google Scholar 

  • Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York

    Google Scholar 

  • Hodgkin A, Huxley AA (1952) Quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544

    Article  CAS  Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 79:2554–2558

    Article  CAS  Google Scholar 

  • Hu Y, Trousdale J, Josić K, Shea-Brown E (2013) Motif statistics and spike correlations in neuronal networks. J Stat Mech: Theory Exp P03012; BMC Neurosci 2012, 13(Suppl 1):P43

  • Lapicque L (1907) Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarisation. J Physiol Pathol Gen 9:620–635

    Google Scholar 

  • McCulloch WS, Pitts WA (1943) Logical calculus of the idea immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  Google Scholar 

  • McEliece RJ, Posner EC, Rodemich ER, Venkatesh S (1987) The capacity of the hopfield associative memory. IEEE Trans Inf Theory 33(4):461–482

    Article  Google Scholar 

  • Melnick IV (1994 Rus, 2010 Eng) Electrically silent neurons in the substantia gelatinosa of the rat spinal cord. Fiziol Zh 56(5):34–39

  • Mizraji E, Lin J (2017) The feeling of understanding: an exploration with neural models. Cogn Neurodyn 11(2):135–146

    Article  Google Scholar 

  • Rao AR (2018) An oscillatory neural network model that demonstrates the benefits of multisensory learning. Cogn Neurodyn 12(5):481–499

    Article  Google Scholar 

  • Smith TC, Jahr CE (2002) Self-inhibition of olfactory bulb neurons. Nat Neurosci 5:760–766

    Article  CAS  Google Scholar 

  • Stefanescu RA, Jirsa VK (2008) A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons. PLoS Comput Biol 4(11):e1000219. https://doi.org/10.1371/journal.pcbi.1000219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stratton P, Wiles J (2015) Global segregation of cortical activity and metastable dynamics. Front Syst Neurosci 25(9):119

    Google Scholar 

  • Tong J, Kong C, Wang X, Liu H, Li B, He Y (2019) Transcranial direct current stimulation influences bilingual language control mechanism: evidence from cross-frequency coupling. Cogn Neurodyn. https://doi.org/10.1007/s11571-019-09561-w:1-12

    Article  PubMed  Google Scholar 

  • Tsodyks M, Feigel’man MV (1988) The enhanced storage capacity in neural networks with low activity level. EPL Europhys Lett 6(2):101

    Article  Google Scholar 

  • Vincente CJP, Amit DA (1989) Optimised network for sparsely coded patterns. J Phys A 22:559–569

    Article  Google Scholar 

  • von Bartheld CS, Bahney J, Herculano-Houzel S (2016) The search for true numbers of neurons and glial cells in the human brain: a review of 150 years of cell counting. J Comp Neurol 524:3865–3895

    Article  Google Scholar 

  • Wei H, Dai D, Bu Y (2017) A plausible neural circuit for decision making and its formation based on reinforcement learning. Cogn Neurodyn 11(3):259–281

    Article  Google Scholar 

Download references

Acknowledgement

The author thanks Yuval Filmus for a very helpful introduction to random graph theory.

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  1. Computer Science Department, Technion- Israel Institute of Technology, 32000, Haifa, Israel

    Yoram Baram

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Correspondence to Yoram Baram.

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Baram, Y. Probabilistically segregated neural circuits and subcritical linguistics. Cogn Neurodyn 14, 837–848 (2020). https://doi.org/10.1007/s11571-020-09602-9

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