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Gating multiple signals through detailed balance of excitation and inhibition in spiking networks

Abstract

Recent theoretical work has provided a basic understanding of signal propagation in networks of spiking neurons, but mechanisms for gating and controlling these signals have not been investigated previously. Here we introduce an idea for the gating of multiple signals in cortical networks that combines principles of signal propagation with aspects of balanced networks. Specifically, we studied networks in which incoming excitatory signals are normally cancelled by locally evoked inhibition, leaving the targeted layer unresponsive. Transmission can be gated 'on' by modulating excitatory and inhibitory gains to upset this detailed balance. We illustrate gating through detailed balance in large networks of integrate-and-fire neurons. We show successful gating of multiple signals and study failure modes that produce effects reminiscent of clinically observed pathologies. Provided that the individual signals are detectable, detailed balance has a large capacity for gating multiple signals.

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Figure 1: Network connectivity and properties.
Figure 2: Detailed balance in a network.
Figure 3: Response analysis.
Figure 4: Gain properties.
Figure 5: Network pathologies.
Figure 6: Gating two signals in a network.
Figure 7: Multiple signals into a single cell.

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References

  1. Shu, Y., Hasenstaub, A. & McCormick, D.A. Turning on and off recurrent balanced cortical activity. Nature 423, 288–293 (2003).

    Article  CAS  Google Scholar 

  2. Haider, B., Duque, A., Hasenstaub, A.R. & McCormick, D.A. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci. 26, 4535–4545 (2006).

    Article  CAS  Google Scholar 

  3. Shadlen, M.N. & Newsome, W.T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994).

    Article  CAS  Google Scholar 

  4. Troyer, T.W. & Miller, K.D. Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Comput. 9, 971–983 (1997).

    Article  CAS  Google Scholar 

  5. Amit, D.J. & Brunel, N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7, 237–252 (1997).

    Article  CAS  Google Scholar 

  6. van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996).

    Article  CAS  Google Scholar 

  7. Brunel, N. Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons. J. Physiol. (Paris) 94, 445–463 (2000).

    Article  CAS  Google Scholar 

  8. Kumar, A., Schrader, S., Aertsen, A. & Rotter, S. The high-conductance state of cortical networks. Neural Comput. 20, 1–43 (2008).

    Article  Google Scholar 

  9. Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge University Press, Cambridge, UK, 1991).

    Book  Google Scholar 

  10. Aertsen, A., Diesmann, M. & Gewaltig, M.O. Propagation of synchronous spiking activity in feedforward neural networks. J. Physiol. (Paris) 90, 243–247 (1996).

    Article  CAS  Google Scholar 

  11. Diesmann, M., Gewaltig, M.O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).

    Article  CAS  Google Scholar 

  12. van Rossum, M.C., Turrigiano, G.G. & Nelson, S.B. Fast propagation of firing rates through layered networks of noisy neurons. J. Neurosci. 22, 1956–1966 (2002).

    Article  CAS  Google Scholar 

  13. Vogels, T.P., Rajan, K. & Abbott, L.F. Neural networks dynamics. Annu. Rev. Neurosci. 28, 357–376 (2005).

    Article  CAS  Google Scholar 

  14. Vogels, T.P. & Abbott, L.F. Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci. 25, 10786–10795 (2005).

    Article  CAS  Google Scholar 

  15. Destexhe, A. & Contreras, D. Neuronal computations with stochastic network states. Science 314, 85–90 (2006).

    Article  CAS  Google Scholar 

  16. Kumar, A., Rotter, S. & Aertsen, A. Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J. Neurosci. 28, 5268–5280 (2008).

    Article  CAS  Google Scholar 

  17. Posner, M.I. ed. Cognitive Neuroscience of Attention (Guilford Press, New York, 2004).

    Google Scholar 

  18. Germuska, M., Saha, S., Fiala, J. & Barbas, H. Synaptic distinction of laminar-specific prefrontal-temporal pathways in primates. Cereb. Cortex 16, 865–875 (2006).

    Article  Google Scholar 

  19. Salinas, E. Context-dependent selection of visuomotor maps. BMC Neurosci. 5, 47–68 (2004).

    Article  Google Scholar 

  20. Disney, A.A., Aoki, C. & Hawken, M.J. Gain modulation by nicotine in macaque v1. Neuron 56, 701–713 (2007).

    Article  CAS  Google Scholar 

  21. Disney, A.A. & Aoki, C. Muscarinic acetylcholine receptors in macaque V1 are most frequently expressed by parvalbumin-immunoreactive neurons. J. Comp. Neurol. 507, 1748–1762 (2008).

    Article  Google Scholar 

  22. Disney, A.A., Domakonda, K.V. & Aoki, C. Differential expression of muscarinic acetylcholine receptors across excitatory and inhibitory cells in visual cortical areas V1 and V2 of the macaque monkey. J. Comp. Neurol. 499, 49–63 (2006).

    Article  CAS  Google Scholar 

  23. Xiang, Z., Huguenard, J.R. & Prince, D.A. Cholinergic switching within neocortical inhibitory networks. Science 281, 985–988 (1998).

    Article  CAS  Google Scholar 

  24. Gil, Z., Connors, B.W. & Amitai, Y. Differential regulation of neocortical synapses by neuromodulators and activity. Neuron 19, 679–686 (1997).

    Article  CAS  Google Scholar 

  25. Mitchell, J.F., Sundberg, K.A. & Reynolds, J.H. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55, 131–141 (2007).

    Article  CAS  Google Scholar 

  26. Binzegger, T., Douglas, R.J. & Martin, K.A. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci. 24, 8441–8453 (2004).

    Article  CAS  Google Scholar 

  27. Abbott, L.F. Theoretical neuroscience rising. Neuron 60, 489–495 (2008).

    Article  CAS  Google Scholar 

  28. Anderson, C.H. & Van Essen, D.C. Shifter circuits: a computational strategy for dynamic aspects of visual processing. Proc. Natl. Acad. Sci. USA 84, 6297–6301 (1987).

    Article  CAS  Google Scholar 

  29. Olshausen, B.A., Anderson, C.H. & Van Essen, D.C. A neurobiological model of visual attention and invariant pattern recognition based on dynamical routing of information. J. Neurosci. 13, 4700–4719 (1993).

    Article  CAS  Google Scholar 

  30. Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).

    Article  CAS  Google Scholar 

  31. Okun, M. & Lampl, I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat. Neurosci. 11, 535–537 (2008).

    Article  CAS  Google Scholar 

  32. Baca, S.M., Marin-Burgin, A., Wagenaar, D.A. & Kristan, W.B. Jr. Widespread inhibition proportional to excitation controls the gain of a leech behavioral circuit. Neuron 57, 276–289 (2008).

    Article  CAS  Google Scholar 

  33. Song, S., Miller, K.D. & Abbott, L.F. Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000).

    Article  CAS  Google Scholar 

  34. Morrison, A., Aertsen, A. & Diesmann, M. Spike-timing-dependent plasticity in balanced random networks. Neural Comput. 19, 1437–1467 (2007).

    Article  Google Scholar 

  35. Lewis, D.A., Hashimoto, T. & Volk, D.W. Cortical inhibitory neurons and schizophrenia. Nat. Rev. Neurosci. 6, 312–324 (2005).

    Article  CAS  Google Scholar 

  36. Seeman, P. Dopamine receptors and the dopamine hypothesis of schizophrenia. Synapse 1, 133–152 (1987).

    Article  CAS  Google Scholar 

  37. Moore, H., West, A.R. & Grace, A.A. The regulation of forebrain dopamine transmission: relevance to the pathophysiology and psychopathology of schizophrenia. Biol. Psychiatry 46, 40–55 (1999).

    Article  CAS  Google Scholar 

  38. Tamminga, C.A. Schizophrenia and glutamatergic transmission. Crit. Rev. Neurobiol. 12, 21–36 (1998).

    Article  CAS  Google Scholar 

  39. Jackson, M.E., Homayoun, H. & Moghaddam, B. NMDA receptor hypofunction produces concomitant firing rate potentiation and burst activity reduction in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 101, 8467–8472 (2004).

    Article  CAS  Google Scholar 

  40. Rubenstein, J.L. & Merzenich, M.M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2, 255–267 (2003).

    Article  CAS  Google Scholar 

  41. Tabuchi, K. et al. A neuroligin-3 mutation implicated in autism increases inhibitory synaptic transmission in mice. Science 318, 71–76 (2007).

    Article  CAS  Google Scholar 

  42. Aviel, Y., Mehring, C., Abeles, M. & Horn, D. On embedding synfire chains in a balanced network. Neural Comput. 15, 1321–1340 (2003).

    Article  CAS  Google Scholar 

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Acknowledgements

The idea of detailed balance was originally suggested to us by G. Turrigiano. Research supported by the US National Science Foundation (IBN-0235463), the Swartz Foundation, the Patterson Trust Fellowship Program in Brain Circuitry and a US National Institutes of Health (NIH) Director's Pioneer Award, part of the NIH Roadmap for Medical Research, through grant number 5-DP1-OD114-02. Thanks to J. Peelle, M. Schiff, P. Jercog and R. Yuste for suggestions.

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Correspondence to Tim P Vogels.

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Vogels, T., Abbott, L. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat Neurosci 12, 483–491 (2009). https://doi.org/10.1038/nn.2276

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