Dynamic branching in a neural network model for probabilistic prediction of sequences
Tóm tắt
An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.
Tài liệu tham khảo
Aguilar, C., Chossat, P., Krupa, M., et al. (2017). Latching dynamics in neural networks with synaptic depression. PLoS ONE, 12(8), e0183710.
Albrengues, C., Lavigne, F., Aguilar, C., et al. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE, 14(7), e0219666.
Amari, S. (1972). Characteristics of random nets of analog neuron-like elements. IEEE Transactions on Systems, Man, and Cybernetics, 5, 643–57.
Amit, D. J., & Brunel, N. (1997). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cerebral Cortex, 7(3), 237–52.
Amit, D. J., Brunel, N., & Tsodyks, M. V. (1994). Correlations of cortical hebbian reverberations: Theory versus experiment. Journal of Neuroscience, 14, 6435–45.
Amit, D. J., Bernacchia, A., & Yakovlev, V. (2003). Multiple-object working memory-a model for behavioral performance. Cerebral Cortex, 13(5), 435–43.
Bak, P., & Chialvo, D. R. (2001). Adaptive learning by extrernal dynamics and negative feedback. Physical Review E, 63(3), 031912.
Bak, P., & Paczuski, M. (1995). Complexity, contingency and criticality. Proceedings of the National Academy of Sciences, 92(15), 6669–96.
Bastos, A. M., Lundqvist, M., Waite, A. S., et al. (2020). Layer and rhythm specificity for predictive routing. Proceedings of the National Academy of Sciences, 117(49), 31459–69.
Bell, A. H., Summerfield, C., Morin, E. L., et al. (2016). Encoding of stimulus probability in macaque inferior temporal cortex. Current Biology, 26(17), 2280–90.
Bienenstock, E., & Lehmann, D. (1998). Regulated criticality in the brain? Advances in Complex Systems, 1(4), 361–84.
Bliss, T. V., & Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361, 31–39.
Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology, 232, 331–56.
Brunel, N. (1996). Hebbian learning of context in recurrent neural networks. Neural Computation, 15(8), 1677–710.
Brunel, N., & Lavigne, F. (2009). Semantic priming in a cortical network model. Journal of Cognitive Neuroscience, 21(2300-19).
Brunel, N., & Wang, X. J. (2001). Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. Journal of Computational Neuroscience, 11(1), 63–85.
Bunge, S. A., Kahn, I., Wallis, J. D., et al. (2003). Neural circuits subserving the retrieval and maintenance of abstract rules. Journal of Neuophysiology, 90(5), 3419–28.
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432.
Chen, B., & Miller, P. (2020). Attractor-state itinerancy in neural circuits with synaptic depression. Journal of Mathematical Neuroscience, 10(1), 1–19.
Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6(10), 744–50.
Cocchi, L., Gollo, L. L., Zalesky, A., et al. (2017). Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158, 132–52.
Dasgupta, I., Schulz, E., Tenenbaum, J. B., et al. (2020). A theory of learning to infer. Psychological Review, 127(3), 412.
Dehghani, N., Peyrache, A., Telenczuk, B., et al. (2016). Dynamic balance of excitation and inhibition in human and monkey neocortex. Scientific Reports, 6(1), 23176.
Delaney-Busch, N., Morgan, E., Lau, E. F., et al. (2017). Comprehenders rationally adapt semantic predictions to the statistics of the local environment: A Bayesian model of trial-by-trial N400 amplitudes. In 39th Annual Conference of the Cognitive Science Society. London, England.
DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117–21.
Ding, N., Melloni, L., Zhang, H., et al. (2015). ortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1), 158–64.
Erickson, C. A., & Desimone, R. (1999). Responses of macaque perirhinal neurons during and after visual stimulus association learning. Journal of Neuroscience, 19(10404-16).
FitzGerald, T., Dolan, R. J., & Friston, K. J. (2015). Dopamine, reward learning, and active inference. Frontiers in Computational Neuroscience, 9, 136.
Friston, K. J., Shiner, T., FitzGerald, T., et al. (2012). Dopamine, affordance and active inference. PLoS Computational Biology, 8(1), e1002327.
Fujimichi, R., Naya, Y., Koyano, K. W., et al. (2010). Unitized representation of paired objects in area 35 of the macaque perirhinal cortex. Europen Journal of Neuroscience, 32(4), 659–67.
Fuster, J. M., & Alexander, G. E. (1971). Neuron activity related to short-term memory. Science, 173(3997), 652–4.
Gershman, S. J. (2019). How to never be wrong. Psychonomic Bulletin and Review, 26(1), 13–28.
Gershman, S. J., & Uchida, N. (2019). Believing in dopamine. Nature Review. Neuroscience, 20(11), 703–14.
Gochin, P. M., Colombo, M., Dorfman, G. A., et al. (1994). Neural ensemble coding in inferior temporal cortex. Journal of Neuophysiology, 71, 2325–37.
Hahnloser, R. H., Kozhevnikov, A. A., & Fee, M. S. (2002). An ultra-sparse code underliesthe generation of neural sequences in a songbird. Nature, 419(6902), 65–70.
Harnal, H., & Giraud, A. L. (2012). Cortical oscillations and sensory predictions. Trends in Cognitive Science, 16, 390–8.
Harvey, C. D., Coen, P., & Tank, D. W. (2012). Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature, 484(7392), 62–68.
Hebb, D. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley and Sons.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–58.
Hung, C., Kreiman, G., Poggio, J., & DiCarlo, T. (2005). Fast read-out of object information in inferior temporal cortex. Science, 310, 863–6.
Hutchison, K., Heap, S., Neely, J., et al. (2014). Attentional control and asymmetric associative priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(3), 844–56.
Ison, M. J., Quian, Q. R., & Fried, I. (2015). Rapid encoding of new memories by individual neurons in the human brain. Neuron, 87(1), 220–30.
Kamiński, J., Sullivan, S., Chung, J. M., et al. (2017). Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nature neuroscience, 20(4), 590–601.
Kang, C. J., & Treves, A. (2019). The challenge of taming a latching network near criticality. The Functional Role of Critical Dynamics in Neural Systems (vol. 11, p. 81–94). Springer Series on Bio- and Neurosystems.
Kirkwood, A., & Bear, M. F. (1994). Homosynaptic long-term depression in the visual cortex. Neuroscience, 14, 3404–12.
Köksal Ersöz, E., Aguilar, C., Chossat, P., et al. (2020). Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability. PLoS ONE, 15(4), e0231165.
Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427(6971), 244–7.
Kornblith, S., Quian Quiroga, R., Koch, C., et al. (2017). Persistent single-neuron activity during working memory in the human medial temporal lobe. Current Biology, 27(7), 1026–1032. https://doi.org/10.1016/j.cub.2017.02.013
Kreiman, G., Hung, C. P., Kraskov, A., et al. (2006). Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron, 49, 433–45.
Kutas, M., DeLong, K. A., & Smith, N. J. (2011). A look around at what lies ahead: Prediction and predictability in language processing. In Predictions in the Brain: Using Our Past to Generate a Future (pp. 190–207). Oxford University Press.
Lam, N., Borduqui, T., Hallak, J., et al. (2021). Effects of altered excitation-inhibition balance on decision making in a cortical circuit model. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.1371-20.2021
Lau, E. F., Holcomb, P. J., & Kuperberg, G. R. (2013). Dissociating N400 effects of prediction from association in single-word contexts. Journal of Cognitive Neuroscience, 25(3), 484–502.
Lavigne, F. (2004). Aim networks: autoincursive memory networks for anticipation toward learned goals. International Journal of Computing Anticipatory Systems, 14, 196–214.
Lavigne, F., & Darmon, N. (2008). Dopaminergic neuromodulation of semantic priming in a cortical network model. Neuropsychologia, 46, 3074–87.
Lavigne, F., & Denis, S. (2002). Neural network modeling of learning of contextual constraints on adaptive anticipations. International Journal of Computing Anticipatory Systems, 12, 253–68.
Lavigne, F., Vitu, F., & d’Ydewalle, G. (2000). The influence of semantic context on initial eye landing sites in words. Acta Psychologica, 104(2), 191–214.
Lavigne, F., Dumercy, L., & Darmon, N. (2011). Determinants of multiple semantic priming: A meta-analysis and spike frequency adaptive model of a cortical network. Journal of Cognitive Neuroscience, 23(6), 1447–74.
Lavigne, F., Dumercy, L., Chanquoy, L., et al. (2012). Dynamics of the semantic priming shift: Behavioral experiments and cortical network model. Cogitive Neurodynamics, 6(6), 467–83.
Lavigne, F., Chanquoy, L., Dumercy, L., et al. (2013). Early dynamics of the semantic priming shift. Advances in Cognitive Psychology, 9(1), 1–14.
Lavigne, F., Avnaïm, F., & Dumercy, L. (2014). Inter-synaptic learning of combination rules in a cortical network model. Frontiers in Psychology, 5, 842.
Lazartigues, L., Mathy, F., & Lavigne, F. (2021). Statistical learning of unbalanced exclusive-or temporal sequences in humans. PLoS ONE, 16(2), e0246826.
Lerner, I., & Shriki, O. (2014). Internally and externally driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation. Frontiers in Psychology, 5(314), 00314.
Lerner, I., Bentin, S., & Shriki, O. (2012). Spreading activation in an attractor network with latching dynamics: automatic semantic priming revisited. Cognitive Science, 36, 1339–82.
Levina, A., & Herrmann, M. (2006). Dynamical synapses give rise to a power-law distribution of neuronal avalanches. In Advances in Neural Information Processing Systems (pp 771–78. 18). MIT Press, Cambridge, MA, USA.
Luka, B. J., & Van Petten, C. (2014). Prospective and retrospective semantic processing: Prediction, time, and relationship strength in event-related potentials. Brain and Language, 135, 115–29.
Magnasco, M. O., Piro, O., & Cecchi, G. A. (2009). Self-tuned critical anti-Hebbian networks. Physical Review Letters, 102(25), 258102.
Messinger, A., Squire, L., Zola, S. M., et al (2001). Neuronal representations of stimulus associations develop in the temporal lobe during learning. Proceedings of the National Academy of Sciences, 98(12239-44).
Miller, E. K. (1999). The prefrontal cortex: complex neural properties for complex behavior. Neuron, 22, 15–17.
Miller, K. D., & Fumarola, F. (2012). Mathematical equivalence of two common forms of firing rate models of neural networks. Neural Computation, 24(1), 25–31.
Minier, L., Fagot, J., & Rey, A. (2016). The temporal dynamics of regularity extraction in non-human primates. Cognitive Science, 40(4), 1019–30.
Miyashita, Y. (1988). Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature, 335.
Miyashita, Y., & Chang, H. S. (1988). Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature, 331, 68–70.
Mongillo, G., Amit, D. J., & Brunel, N. (2003). Retrospective and prospective persistent activity induced by hebbian learning in a recurrent cortical network. Europen Journal of Neuroscience, 18(7), 2011–24.
Mongillo, G., Rumpel, S., & Loewenstein, Y. (2018). Inhibitory connectivity defines the realm of excitatory plasticity. Nature Neuroscience, 21(10), 1463–70.
Muhammad, R., Wallis, J. D., & Miller, E. K. (2006). A comparison of abstract rules in the prefrontal cortex, premotor cortex, inferior temporal cortex and striatum. Journal of Cognitive Neuroscience, 18(6), 974–89.
Naya, Y., Yoshida, M., & Miyashita, Y. (2001). Backward spreading of memory-retrieval signal in the primate temporal cortex. Science, 291(5504), 661–64.
Naya, Y., Yoshida, M., Takeda, M., et al. (2003). Delay-period activities in two subdivisions of monkey inferotemporal cortex during pair association memory task. The European Journal of Neuroscience, 18, 2915–8.
Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In Basic Processes in Reading: Visual Word Recognition. Lawrence Erlbaum Associates, Inc. (pp. 264–336).
Pereira, U., & Brunel, N. (2020). Unsupervised learning of persistent and sequential activity. Frontiers in computational neuroscience, 13, 97.
Quian, Q. R. (2012). Concept cells: the building blocks of declarative memory functions. Nature Review Neuroscience, 13, 587–97.
Quian, Q. R. (2016). Neuronal codes for visual perception and memory. Neuropsychologia, 83, 227–41.
Quian, Q. R., & Kreiman, G. (2010). Measuring sparseness in the brain: comment on Bowers. Psychological Review, 117, 291–99.
Rainer, G., Rao, S. C., & Miller, E. K. (1999). Prospective coding for objects in primate prefrontal cortex. Journal of Neuroscience, 19, 5493–5505.
Reddy, L., Poncet, M., Self, M. W., et al. (2015). Learning of anticipatory responses in single neurons of the human medial temporal lobe. Nature communications, 6(1), 1–8.
Rolls, E. T., & Tovee, M. J. (1995). Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Neuophysiology, 73(2), 713–26.
Rolls, E. T., Loh, M., Deco, G., et al. (2008). Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nature Review Neuroscience, 9, 696.
Sakai, K., & Miyashita, Y. (1991). Neural organization for the long-term memory of paired associates. Nature, 354, 152–55.
Schaal, S., Mohajerian, P., & Ijspeert, A. (2007). Dynamics systems vs. optimal control-a unifying view. Progress in Brain Research, 165, 425–445.
Tamura, H., & Tanaka, K. (2001). Visual response properties of cells in the ventral and dorsal parts of the macaque inferotemporal cortex. Cerebral Cortex, 11, 384–99.
Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19, 109–39.
Tanaka, K. (2003). Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities. Cereb Cortex, 13, 90–99.
Thurley, K., Senn, W., & Luscher, H. R. (2008). Dopamine increases the gain of the input-output response of rat prefrontal pyramidal neurons. Journal of Neurophysiology, 99(6), 2985–97.
Tremblay, R., Lee, S., & Rudy, B. (2016). GABAergic interneurons in the neocortex: From cellular properties to circuits. Neuron, 91, 260–92.
Tsao, D. Y., Freiwald, W. A., Tootell, R. B., et al. (2006). A cortical region consisting entirely of face-selective cells. Science, 311(5761), 670–4.
Tsodyks, M. V. (1990). Hierarchical associative memory in neural networks with low activity level. Modern Physics Letters B, 4, 259–65.
Tsodyks, M. V., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences, 94, 719–23.
Van Petten, C. (2014). Examining the N400 semantic context effect item-by-item: Relationship to corpus-based measures of word co-occurrence. International Journal of Psychophysiology, 94, 407–19.
Vander Weele, C. M., Siciliano, C. A., Matthews, G. A., et al. (2018). Dopamine enhances signal-to-noise ratio in cortical-brainstem encoding of aversive stimuli. Nature, 563, 397–401.
Varela, J. A., Sen, K., Gibson, J., et al. (1997). A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience, 17(20), 7926–40.
Volman, V., Behrens, M. M., & Sejnowski, T. J. (2011). Downregulation of parvalbumin at cortical GABA synapses reduces network gamma oscillatory activity. Journal of Neuroscience, 31(49), 18137–48.
Wallis, J. D., & Miller, E. K. (2003). From rule to response: neuronal processes in the premotor and prefrontal cortex. Journal of Neuophysiology, 90(3), 1790–806.
Wallis, J. D., Anderson, K. C., & Miller, E. K. (2001). Single neurons in prefrontal cortex encode abstract rules. Nature, 411(6840), 953–6.
Wang, X. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36, 955–68.
Weinberger, N. M. (1998). Physiological memory in primary auditory cortex: characteristics and mechanisms. Neurobiology of Learning and Memory, 70(1–2), 226–51.
Willems, R. M., Frank, S. L., Nijhof, A. D., et al. (2015). Prediction during natural language comprehension. Cerebral Cortex, 26(6), 2506–16.
Wilson, H. R., & Cowan, J. D. (2012). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysics Journal, 12(1), 1–24.
Wirth, S., Yanike, M., Frank, L. M., et al. (2003). Single neurons in the monkey hippocampus and learning of new associations. Science, 300(5625), 1578–81.
Yakovlev, V., Fusi, S., E., B., et al. (1998). nter-trial neuronal activity in inferior temporal cortex: A putative vehicle to generate long-term visual associations. Nature Neuroscience, 1(4), 310–17.
Yizhar, O., Fenno, L., Prigge, M., et al. (2011). Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature, 477(7363), 171–8.
Yoshida, M., Naya, Y., & Miyashita, Y. (2003). Anatomical organization of forward fiber projections from area TE to perirhinal neurons representing visual long-term memory in monkeys. Proceedings of the National Academy of Sciences, 100(4257–62).
Young, M., & Yamane, S. (1992). Sparse population coding of faces in the inferotemporal cortex. Science, 256(5061), 1327–31.