Behavior of a Model for Feedback-Controlled Reverberating Circuit and Immediate Memory Function

V. F. Rodrigues, R. S. Wedemann, M. C. S. de Castro, D. Silva, C. M. Cortez

Abstract


 In this work we implement a mathematical model of synaptic transmission connecting neurons in a circuit of reverberating discharges in order to investigate its behavior in front of parametric variations. Using a program developed in C language, we verified if this model would behave as short-term memory circuit. In the simulation, we used neural parametric values from experimental measures in animal. Our model was able to reproduce polysynaptic activity of a neuronal group of rat brain (looping time of about 102 ms). On the other hand, we verified that the inhibitory feedback synapses in circuit with weight varying with presynaptic firing time and frequency is capable to change the circuit characteristic to reproduce the typical behavior of neural circuits. The results suggest that, differently from some recent considerations, the reverberating circuit model has great potential to reproduce the typical behavior of neural circuits and could be seen as a possible model for immediate memory.


Keywords


Modeling, synaptic transmission, short-term memory, computer simulation.

Full Text:

PDF

References


Billock, V. A. Very short-term visual memory via reverberation: a role for the cortico-thalamic excitatory circuit in temporal filling-in during blinks and saccades? Vision Res 37 (1997), 949-953.

Branco, T. & Häusser, M. Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron. 69(5) (2011), 885-892.

Cardoso, F. R. G., Cruz, F. A. O., Silva, D., & Cortez C. M. Computational modeling of synchronization process of the circadian timing system of mammals. Biol Cybern 100 (2009), 385-393.

Cowan, N. What are the differences between long-term, short-term, and working memory? Prog Brain Res 169 (2008), 323–338.

Cruz, F. A. O., Silva D, & Cortez C. M. Simulation of a spinal reflex circuit model controlled by a central pattern generator. Far East J Appl Math 33 (2008), 307-336.

Cruz, F. A. O., & Cortez C. MComputer simulation of a central pattern generator via Kuramoto model. Physica A 353. (2005), 258-270.

Dalcin, B., Cruz, F. A. O., Cortez C. M., & Passos EL Computer modeling of a spinal reflex circuit. Braz. J Phys 35 (2005), 987-994.

Davelaar, E. J., Goshen-Gottstein, Y., Ashkenazi, A., Haarmann, H. J., & Usher, M. The demise of short-term memory revisited: empirical and computational investigations of recency effects. Psychol Rev 112(1) (2005), 3-42.

Dongare, A.D., Kharde, R.R., & Kachare, A. D. Introduction to Artificial Neural Network. Int J Eng Innov Tech 2 (2012), 189-194.

Foss, J., & Milton, J. Multistability in recurrent neural loops arising from delay. J Neurophysiol 84 (2000), 975-985.

Friesen, W. O., & Block, G. D. What is a biological oscillator? Am J Physiol 246R (1984), 847.

Gardner, D. Synaptic Transmission. In: Conn PM, editor. Neuroscience in Medicine. Philadelphia: J. B. Lippincott Company. (1995), pp. 75-114.

Hebb, D.O. Organization of Behavior: A Neuropsychological Theory. New York, John Wiley and Sons (1939).

Johnson, L.R., Hou, M., Ponce-Alvarez, A., Gribelyuk, L. M., Alphs, H. H., Albert, L., Brown, B. L., Ledoux, J.E., & Doyère, V. A recurrent network in the lateral amygdala: a mechanism for coincidence detection. Front Neural Circuits 2 (2008), 3.

Johnson, L. R., Ledoux, J. E., & Doyère, V. Hebbian reverberations in emotional memory micro circuits. Front Neurosci 3 (2009), 198–205.

Jonides, J., Lewis, R. L., Nee, D. E., Lustig, C. A., Berman, M.G., & Moore, K.S. The mind and brain of short-term memory. Ann Rev Psychol 59 (2008), 193–224.

Mongillo, G, Barak O, & Tsodyks M Synaptic theory of working memory. Science 319 (2008), 1543–1546.

Négyessy, L., Nepusz, T., Zalányi, L., & Bazsó, F. Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas. Proc. R. Soc. B 275 (2008), 2403–2410.

Niktarash, A.H. Discussion on the reverberatory model of short-term memory: a computational approach. Brain Cogn 53 (2003),1-8.

Pinto, T., Wedemann, R. S., & Cortez C. M. Modeling the electric potential across neuronal membranes: the effect of fixed charges on spinal ganglion neurons and neuroblastoma cells. PlosOne 9(5) (2011), e96194.

Rankin, J., Sussman, E., & Rinzel, J. Neuromechanistic Model of Auditory Bistability. PLoS Comput Biol 11(2015), e1004555.

Rao, R. P., & Sejnowski, T. J. Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Comput 13 (2001), 2221-2237.

Safaai, H., Neves, R., Eschenko, O., Logothetis, N.K., & Panzeri, S. Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing. Proc Natl Acad Sci USA 112 (2016), 12834-1289.

Sejnowski, T. J. The book of Hebb. Neuron 24 (1999), 773–776.

Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn Sci 8 (2004), 418–425.

Stopfer M, Laurent G Short-term memory in olfactory network dynamics. Nature 402 (1999), 664-68.

Takeuchi, T., Duszkiewicz, A. J.,& Morris R.G.M.) the synaptic plasticity and memory hypothesis: encoding, storage and persistence. Philos Trans R Soc Lond B Biol Sci 369 (2014, 20130288.

Tononi, G., Edelman, G. M., & Sporns, O. Complexity and coherency: integrating information in the brain. Trends Cogn Sci 2 (1998), 474-484.




DOI: https://doi.org/10.5540/tema.2020.021.03.601

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia