Abstract
Several studies posit energy as a constraint on the coding and processing of information in the brain due to the high cost of resting and evoked cortical activity. This suggestion has been addressed theoretically with models of a single neuron and two coupled neurons. Neural mass models (NMMs) address mean-field based modeling of the activity and interactions between populations of neurons rather than a few neurons. NMMs have been widely employed for studying the generation of EEG rhythms, and more recently as frameworks for integrated models of neurophysiology and functional MRI (fMRI) responses. To date, the consequences of energy constraints on the activity and interactions of ensembles of neurons have not been addressed. Here we aim to study the impact of constraining energy consumption during the resting-state on NMM parameters. To this end, we first linearized the model, then used stochastic control theory by introducing a quadratic cost function, which transforms the NMM into a stochastic linear quadratic regulator (LQR). Solving the LQR problem introduces a regime in which the NMM parameters, specifically the effective connectivities between neuronal populations, must vary with time. This is in contrast to current NMMs, which assume a constant parameter set for a given condition or task. We further simulated energy-constrained stochastic control of a specific NMM, the Wilson and Cowan model of two coupled neuronal populations, one of which is excitatory and the other inhibitory. These simulations demonstrate that with varying weights of the energy-cost function, the NMM parameters show different time-varying behavior. We conclude that constraining NMMs according to energy consumption may create more realistic models. We further propose to employ linear NMMs with time-varying parameters as an alternative to traditional nonlinear NMMs with constant parameters.
Similar content being viewed by others
References
Aiello, L. C., & Wheeler, P. (1995). The expensive-tissue hypothesis: the brain and the digestive system in human and primate evolution. Current Anthropology, 36, 199–221.
Alle, H., Roth, A., & Geiger, J. R. (2009). Energy-efficient action potentials in hippocampal mossy fibbers. Science, 325, 1405–1408.
Ames, A. (2000). CNS energy metabolism as related to function. Brain Research Reviews, 34, 42–68.
Attwell, D., & Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends in Neurosciences, 25, 621–625.
Babajani, A., & Soltanian-Zadeh, H. (2006). Integrated MEG/EEG and fMRI model based on neural masses. IEEE Transactions on Biomedical Engineering, 53, 1794–1801.
Balasubramanian, V., Kimber, D., & Berry, M. J., II. (2001). Metabolically efficient information processing. Neural Computation, 13, 799–815.
Bennet, M. R., & Kearns, J. L. (2000). Statistics of synaptic release at nerve terminals. Progress in Neurobiology, 60, 545–606.
Blomquist, P., Devor, A., Indahl, U. G., Ulbert, I., Einevoll, G. T., & Dale, A. M. (2009). Estimation of thalamocortical and intracortical network models from joint thalamic single-eletrode and cortical laminar-electrode recordings in the rat barrel system. PLoS Computational Biology, 5, e1000328.
Brock, O., & Kavraki, L. (2001). Decomposition based motion planning: a framework for real time motion planning in high-dimensional configuration spaces. In IEEE International Conference on Robotics and Automation, 2, 1469–1474.
Chen, S., Li, X., & Zhou, X. Y. (1998). Stochastic linear quadratic regulators with indefinite control weight costs. SIAM Journal on Control and Optimization, 36, 1685–1702.
Chen, X., & Zhou, Y. (2004). Stochastic linear-quadratic control with conic control constraints on an infinite time horizon. SIAM Journal on Control and Optimization, 43, 1120–1150.
Chklovskii, D. B., & Koukalov, A. A. (2004). Maps in the brain: what can we learn from them? Annual Review of Neuroscience, 27, 369–392.
Çimen, T. (2008). State-dependent Riccati equation (SDRE) control: A survey. In: Proc. of the 17th IFAC World Congress, Seoul, South Korea.
Clarke, D. D., & Sokoloff, L. (1999) Circulation and energy metabolism of the brain. In Agranoff, B. W., & Siegel, G. J. (eds), Basic neurochemistry. Molecular, cellular and medical aspects (6th edn) (pp. 637–670). Lippincott-Raven.
David, O., & Friston, K. J. (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage, 20, 1743–1755.
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008). The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 4, e1000092.
Faugeras, O., Veltz, R., & Grimbert, F. (2009). Persistent neural states: stationary localized activity patterns in nonlinear continuous n-population, q-dimensional neural networks. Neural Computation, 21, 147–187.
Feng, J., & Tuckwell, H. C. (2003). Optimal control of neuronal activity. Physical Review Letters, 91, 018101.
Freeman, W. J. (1975). Mass action in the nervous system. New York: Academic.
Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11, 127–138.
Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology, Paris, 100, 70–87.
Gertsner, W., Kreiter, A. K., Markram, H., & Hertz, A. V. M. (1997). Neural codes: firing rates and beyond. PNAS, 94, 12740–12741.
Granger, C. W. J. (2008). Non-linear models: where do we go next-time varying parameters models? Studies in Nonlinear Dynamics & Econometrics, 12, 1–9.
Grimbert, F., & Faugeras, O. (2006). Bifurcation analysis of Jansen’s neural mass model. Neural Computation, 18, 3052–3068.
Guigon, E., Baraduc, P., & Desmurget, M. (2008). Optimality, stochasticity, and variability in motor behaviour. Journal of Computational Neuroscience, 24, 57–68.
Hasenstaub, A., Otte, S., Callaway, E., & Sejnowski, T. J. (2010). Metabolic cost as a unifying principle governing neuronal biophysics. PNAS, 107, 12329–12334.
Heemels, W. P. M. H., Van Eijndhoven, S. J. L., & Stoorvogel, A. A. (1998). Linear quadratic regulator with positive controls. International Journal of Control, 70, 551–578.
Highman, N. J. (2008). Function of matrices: theory and computation. Philadelphia: Siam. Sociaty for Industrial and Applied Mathematics.
Hu, Y., & Zhou, X. Y. (2005). Constrained stochastic LQ control with random coefficients, and application to portfolio selection. SIAM Journal on Control and Optimization, 44, 444–466.
Jansen, B. H., & Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics, 73, 357–366.
Johnson, C. D. (1987). Limits of propriety for linear-quadratic regulator problems. International Journal of Control, 35, 1835–1846.
Kalman, R. E. (1960). Contributions to the theory of optimal control. Bol Soc Math Mexicana, 5, 102–119.
Karbowski, J. (2003). How does connectivity between cortical areas depend on brain size? Implications for efficient computations. Journal of Computational Neuroscience, 15, 347–356.
Katz, B., & Miledi, R. (1970). Membrane noise produced by acetylcholine. Nature, 226, 962–963.
Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244–247.
Körding, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10, 319–326.
Laughlin, S. B. (2001). Energy as a constrain on the coding and processing of sensory information. Current Opinion in Neurobiology, 11, 475–480.
Laughlin, S. B., & Sejnowski, T. J. (2003). Communication in neuronal networks. Science, 301, 1870–1874.
Lennie, P. (2003). The cost of cortical computation. Current Biology, 13, 493–497.
Levy, W. B., & Baxter, R. A. (1996). Energy efficient neural codes. Neural Computation, 8, 531–543.
Lopes da Silva, F. H., Hoeks, A., Smits, H., & Zetterberg, L. H. (1974). Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik, 15, 27–37.
Maloney, L. T., & Mamassian, P. (2009). Bayesian decision theory as a model of human visual perception: testing Bayesian transfer. Visual Neuroscience, 26, 147–155.
Maloney, L. T., & Zhang, H. (2010). Decision-theoretic models of visual perception and action. Vision Research, 50, 2362–2374.
Martin, R. D. (1996). Scaling of the mammalian brain: the maternal energy hypothesis. News in Physiological Sciences, 11, 149–156.
Model, P. G., Highstein, S. M., & Bennett, M. V. L. (1975). Depletion of vesicles and fatigue of transmission at a vertebrate central synapse. Brain Research, 98, 209–228.
Montgomery, J. M., & Madison, D. V. (2004). Discrete synaptic states define a major mechanism of synaptic plasticity. TRENDS in Neuroscience, 27, 744–750.
Moran, R. J., Stephan, K. E., Kiebel, S. J., Rombach, N., O’Connor, W. T., Murphy, K. J., et al. (2008). Bayesian estimation of synaptic physiology from the spectral responses of neural masses. NeuroImage, 42, 272–284.
Niven, J. E. (2007). Brains, islands and evolution: breaking all the rules. Trends in Ecology & Evolution, 22, 57–59.
Niven, J. E., & Laughlin, S. B. (2008). Energy limitation as a selective pressure on the evolution of sensory systems. The Journal of Experimental Biology, 211, 1792–1804.
Niven, J. E., Vähäsöyrinki, M., & Juusola, M. (2003). Shaker K+ -channels are predicted to reduce the metabolic cost of neural information in Drosophila photoreceptors. Proceedings of the Royal Society of London - Series B: Biological Sciences, 270, S58–S61.
Oksendal, B. (2003). Stochastic Differential Equations. Springer-verlag.
Pons, A. J., Cantero, J. L., Atienza, M., & Garcia-Ojalvo, J. (2010). Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling. NeuroImage, 52, 848–861.
Raichle, M. E. (2010). Two views of brain function. Trends in Cognitive Sciences, 14, 180–190.
Sotero, R. C., Bortel, A., Martínez-Cancino, R., Neupane, S., O’Connor, P., & Shmuel, A. (2010). Anatomically-constrained effective connectivity within and among layers in a cortical column modeled and estimated from local field potentials. Journal of Integrative Neuroscience, 9, 355–379.
Sotero, R. C., & Martínez-Cancino, R. (2010). Dynamical mean field model of a neural-glial mass. Neural Computation, 22, 969–997.
Sotero, R. C., & Trujillo-Barreto, N. J. (2007). Modelling the role of excitatory and inhibitory neuronal activity in the generation of the BOLD signal. NeuroImage, 35, 149–165.
Sotero, R. C., & Trujillo-Barreto, N. J. (2008). Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism. NeuroImage, 39, 290–309.
Sotero, R. C., Trujillo-Barreto, N. J., Iturria-Medina, Y., Carbonell, F., & Jiménez, J. C. (2007). Realistically coupled neural mass models can generate EEG rhythms. Neural Computation, 19, 478–512.
Spiegler, A., Kiebel, S. J., Atay, F. M., & Knösche, T. R. (2010). Bifurcation analysis of neural mass models: impact of extrinsic inputs and dendritic time constants. NeuroImage, 52, 1041–1058.
Todorov, E. (2005). Stochastic optimal control and estimation methods adapted to the noise characteristics of the sensorimotor system. Neural Computation, 17, 1084–1108.
Todorov, E., Li, W., & Pan, W. (2005). From task parameters to motor synergies: a hierarchical framework for approximately optimal feedback control of redundant manipulators. Journal of Robotic Systems, 22, 669–710.
Tomás-Rodríguez, M., & Banks, S. P. (2010). Linear, Time-varying Approximations to Nonlinear Dynamical Systems with Applications in Control and Optimization. Lecture Notes in Control and Information Sciences. New York: Springer.
Torrealdea, F. J., Sarasola, C., & d’Anjou, A. (2009a). Energy consumption and information transmission in model neurons. Chaos, Solitons and Fractals, 40, 60–68.
Torrealdea, F. J., Sarasola, C., d’Anjou, A., Moujahid, A., & Velez de Mendizabal, N. (2009b). Energy efficiency of information transmission by electrically coupled neurons. Bio Systems, 97, 60–71.
Turelli, M. (1977). Random environments and stochastic calculus. Theoretical Population Biology, 12, 140–178.
Valdés-Sosa, P. A., Sanchez-Bornot, J. M., Sotero, R. C., Iturria-Medina, Y., Bosch-Bayard, J., Carbonell, F., et al. (2009). Model driven EEG/fMRI fusion of brain oscillations. Human Brain Mapping, 30, 2701–2721.
Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1–24.
Wonham, W. M. (1968). On a matrix Riccati equation of stochastic control. SIAM Journal on Control and Optimization, 6, 312–326.
Zetterberg, L. H., Kristiansson, L., & Mossberg, K. (1978). Performance of a model for a local neuron population. Biological Cybernetics, 31, 15–26.
Acknowledgements
We thank Debra Dawson and Laura Betcherman for their comments on an earlier version of the manuscript and for English editing. Supported by Industry Canada / MNI Center of excellence in commercialization and research postdoctoral fellowship and grant awarded to RCS and AS respectively, and by CIHR grant MOP-102599 and Human Frontier Science grant RGY0080/2008 awarded to AS.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Action Editor: Gaute T Einevoll
Appendix A. Solution of the Hamilton-Jacobi-bellman equation for the LQR problem
Appendix A. Solution of the Hamilton-Jacobi-bellman equation for the LQR problem
Given the system:
The Hamilton-Jacobi-Bellman (HJB) equation for \( {\mathbf{\psi }}\left( {s,{\mathbf{y}}} \right) = \mathop{{\inf }}\limits_{{\mathbf{u}}} {{\mathbf{J}}^{{\mathbf{u}}}}\left( {s,{\mathbf{y}}} \right) \)is (Oksendal 2003):
where:
and \( {{\mathbf{F}}^{{\mathbf{u}}}}\left( {t,{\mathbf{y}}} \right) = {\mathbf{u}}'(t){\mathbf{Ru}}(t) + {\mathbf{y}}'(t){\mathbf{Qy}}(t) \). Using (9) we get:
where we used: \( {\mathbf{b}}\left( {{\mathbf{y}},{\mathbf{u}}} \right) = {\mathbf{u}}(t) + {\mathbf{\mu }} \). The minimum is obtained when \( \frac{\partial }{{\partial {u_i}}}\left( {{F^u}\left( {t,y} \right) + {L^u}\psi } \right) = 0 \), i.e. when: \( 2{\mathbf{Ru}} + 2{\mathbf{My}} = 0 \), or:
We substitute this value in (56) and obtain the right side equal to 0 if we choose \( {\mathbf{M}}(t) \) such that:
and \( {\mathbf{\gamma }}(t) \) such that:
Rights and permissions
About this article
Cite this article
Sotero, R.C., Shmuel, A. Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state. J Comput Neurosci 32, 563–576 (2012). https://doi.org/10.1007/s10827-011-0370-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10827-011-0370-8