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Chapter 14 - Attractor Network Dynamics, Transmitters, and Memory and Cognitive Changes in Aging

Published online by Cambridge University Press:  30 November 2019

Kenneth M. Heilman
University of Florida
Stephen E. Nadeau
University of Florida
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An attractor network is used in computational neuroscience to model the neuronal processes important for cognitive functions such as memory as well as motor behaviors. These networks are composed of neurons with excitatory interconnections that can settle into a stable pattern of firing. This chapter describes how attractor networks in the cerebral cortex are important for short- and long-term memory, attention, and decision-making. It then discusses how the random firing of neurons can influence the stability of these networks by introducing stochastic noise, and how these effects are involved in probabilistic decision-making and are implicated in some disorders of cortical function, such as poor short-term memory, attention, and alterations of cognitive functions with aging. Further, this chapter describes how alterations in transmitters that occur with aging, including acetylcholine, dopamine, and norepinephrine, can impair the stability of these memory networks, resulting in poor memory and attention. This computational neuroscience approach has implications for treatment.

Publisher: Cambridge University Press
Print publication year: 2019

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Rolls, E.T., Cerebral Cortex: Principles of Operation. Oxford: Oxford University Press, 2016.CrossRefGoogle Scholar
Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 1982. 79: 25542558.CrossRefGoogle ScholarPubMed
Amit, D.J., Modeling Brain Function. Cambridge: Cambridge University Press, 1989.CrossRefGoogle Scholar
Hertz, J., Krogh, A., and Palmer, R.G., An Introduction to the Theory of Neural Computation. Wokingham: Addison-Wesley, 1991.CrossRefGoogle Scholar
Rolls, E.T., and Deco, G., Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging. Neurobiology of Learning and Memory, 2015. 118: 150161.CrossRefGoogle Scholar
Kohonen, T., Oja, E., and Lehtio, P., Storage and processing of information in distributed memory systems, in Parallel Models of Associative Memory, Hinton, G.E. and Anderson, J.A., editors. Hillsdale, NJ: Lawrence Erlbaum, 1981, pp. 129167.Google Scholar
Rolls, E.T., and Treves, A., Neural Networks and Brain Function. Oxford: Oxford University Press, 1998.Google Scholar
Treves, A., and Rolls, E.T., What determines the capacity of autoassociative memories in the brain? Network, 1991. 2: 371397.CrossRefGoogle Scholar
Hebb, D.O., The Organization of Behavior: A Neuropsychological Theory. New York: Wiley, 1949.Google Scholar
Rolls, E.T., and Treves, A., The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain. Network, 1990. 1: 407421.CrossRefGoogle Scholar
Treves, A., Graded-response neurons and information encodings in autoassociative memories. Physical Review A, 1990. 42: 24182430.CrossRefGoogle ScholarPubMed
Treves, A., and Rolls, E.T., Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus, 1992. 2: 189199.CrossRefGoogle ScholarPubMed
Rolls, E.T., et al., Simulation studies of the CA3 hippocampal subfield modelled as an attractor neural network. Neural Networks, 1997. 10: 15591569.CrossRefGoogle Scholar
Amaral, D.G., Ishizuka, N., and Claiborne, B., Neurons, numbers and the hippocampal network. Progress in Brain Research, 1990. 83: 111.CrossRefGoogle ScholarPubMed
Treves, A., and Rolls, E.T., A computational analysis of the role of the hippocampus in memory. Hippocampus, 1994. 4: 374391.CrossRefGoogle ScholarPubMed
Kesner, R.P., and Rolls, E.T., A computational theory of hippocampal function, and tests of the theory: new developments. Neuroscience and Biobehavioral Reviews, 2015. 48: 92147.CrossRefGoogle ScholarPubMed
Rolls, E.T., Advantages of dilution in the connectivity of attractor networks in the brain. Biologically Inspired Cognitive Architectures, 2012. 1: 4454.CrossRefGoogle Scholar
Rolls, E.T., Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas. Philosophical Transactions of the Royal Society of London B, 1992. 335: 1121.Google ScholarPubMed
Rolls, E.T., Consciousness absent and present: a neurophysiological exploration. Progress in Brain Research, 2003. 144: 95106.CrossRefGoogle Scholar
Panzeri, S., et al., Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons. Network, 2001. 12(4): 423440.CrossRefGoogle ScholarPubMed
Treves, A., Mean-field analysis of neuronal spike dynamics. Network, 1993. 4: 259284.CrossRefGoogle Scholar
Battaglia, F.P., and Treves, A., Stable and rapid recurrent processing in realistic auto-associative memories. Neural Computation, 1998. 10: 431450.CrossRefGoogle Scholar
Braitenberg, V., and Schütz, A., Anatomy of the Cortex. Berlin: Springer-Verlag, 1991.CrossRefGoogle Scholar
Abeles, M., Corticonics: Neural Circuits of the Cerebral Cortex. New York: Cambridge University Press, 1991.CrossRefGoogle Scholar
Rolls, E.T., and Mills, W.P.C., Computations in the deep vs superficial layers of the cerebral cortex. Neurobiology of Learning and Memory, 2017. 145: 205221.CrossRefGoogle ScholarPubMed
Goldman-Rakic, P.S., Cellular basis of working memory. Neuron, 1995. 14: 477485.CrossRefGoogle ScholarPubMed
Goldman-Rakic, P.S., The prefrontal landscape: implications of functional architecture for understanding human mentation and the central executive. Philosophical Transactions of the Royal Society B, 1996. 351: 14451453.Google ScholarPubMed
Fuster, J.M., Executive frontal functions. Experimental Brain Research, 2000. 133(1): 6670.CrossRefGoogle ScholarPubMed
Fuster, J.M., and Alexander, G.E., Neuron activity related to short-term memory. Science, 1971. 173: 652654.CrossRefGoogle ScholarPubMed
Kubota, K., and Niki, H., Prefrontal cortical unit activity and delayed alternation performance in monkeys. Journal of Neurophysiology, 1971. 34(3): 337347.CrossRefGoogle ScholarPubMed
Funahashi, S., Bruce, C.J., and Goldman-Rakic, P.S., Mnemonic coding of visual space in monkey dorsolateral prefrontal cortex. Journal of Neurophysiology, 1989. 61: 331349.CrossRefGoogle Scholar
Fuster, J.M., The Prefrontal Cortex. 4th ed. London: Academic Press, 2008.CrossRefGoogle Scholar
Renart, A., Parga, N., and Rolls, E.T., A recurrent model of the interaction between the prefrontal cortex and inferior temporal cortex in delay memory tasks, in Advances in Neural Information Processing Systems, Solla, S.A., Leen, T.K., and Mueller, K.-R., editors. Cambridge, MA: MIT Press, 2000, pp. 171177.Google Scholar
Renart, A., et al., A model of the IT-PF network in object working memory which includes balanced persistent activity and tuned inhibition. Neurocomputing, 2001. 38 –40: 15251531.CrossRefGoogle Scholar
Goldman-Rakic, P.S., and Leung, H.-C., Functional architecture of the dorsolateral prefrontal cortex in monkeys and humans, in Principles of Frontal Lobe Function, Stuss, D.T. and Knight, R.T., editors. New York: Oxford University Press, 2002, pp. 8595.CrossRefGoogle Scholar
Tuckwell, H., Introduction to Theoretical Neurobiology. Cambridge: Cambridge University Press, 1988.Google Scholar
Jackson, B.S., Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons. Neural Computation, 2004. 16(10): 21252195.CrossRefGoogle ScholarPubMed
Deco, G., Rolls, E.T., and Romo, R., Stochastic dynamics as a principle of brain function. Progress in Neurobiology, 2009. 88: 116.CrossRefGoogle ScholarPubMed
Rolls, E.T. and Deco, G., The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function. Oxford: Oxford University Press, 2010.CrossRefGoogle Scholar
Brunel, N., and Wang, X.J., Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. Journal of Computational Neuroscience, 2001. 11: 6385.CrossRefGoogle Scholar
Durstewitz, D., Seamans, J.K., and Sejnowski, T.J., Neurocomputational models of working memory. Nature Neuroscience, 2000. 3 Suppl: 11841191.CrossRefGoogle ScholarPubMed
Loh, M., Rolls, E.T., and Deco, G., A dynamical systems hypothesis of schizophrenia. PLoS Computational Biology, 2007. 3(11): e228. doi:10.1371/journal.pcbi.0030228.CrossRefGoogle ScholarPubMed
Rolls, E.T., et al., Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nature Reviews Neuroscience, 2008. 9: 696709.CrossRefGoogle ScholarPubMed
Rolls, E.T., Loh, M., and Deco, G., An attractor hypothesis of obsessive-compulsive disorder. European Journal of Neuroscience, 2008. 28: 782793.CrossRefGoogle ScholarPubMed
Brunel, N., and Hakim, V., Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, 1999. 11(7): 16211671.CrossRefGoogle ScholarPubMed
Mattia, M., and Del Giudice, P., Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. Physical Review E, 2002. 66: 5191751919.CrossRefGoogle Scholar
Mattia, M., and Del Giudice, P., Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2004. 70(5 Pt 1): 052903.CrossRefGoogle ScholarPubMed
Deco, G., and Rolls, E.T., Decision-making and Weber’s Law: a neurophysiological model. European Journal of Neuroscience, 2006. 24: 901916.CrossRefGoogle ScholarPubMed
Faisal, A.A., Selen, L.P., and Wolpert, D.M., Noise in the nervous system. Nature Reviews Neuroscience, 2008. 9(4): 292303.CrossRefGoogle Scholar
Lisman, J.E., Fellous, J.M., and Wang, X.J., A role for NMDA-receptor channels in working memory. Nature Neuroscience, 1998. 1(4): 273275.CrossRefGoogle ScholarPubMed
Wang, X.-J., Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. Journal of Neuroscience, 1999. 19(21): 95879603.CrossRefGoogle ScholarPubMed
Compte, A., et al., Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb Cortex, 2000. 10(9): 910923.CrossRefGoogle Scholar
Wang, X.J., Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences, 2001. 24(8): 455463.CrossRefGoogle ScholarPubMed
Tegner, J., Compte, A., and Wang, X.J., The dynamical stability of reverberatory neural circuits. Biological Cybernetics, 2002. 87(5–6): 471481.CrossRefGoogle ScholarPubMed
Rolls, E.T., Emotion Explained. Oxford: Oxford University Press, 2005.CrossRefGoogle Scholar
Coyle, J.T., Tsai, G., and Goff, D., Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Annals of the New York Academy of Sciences, 2003. 1003: 318327.CrossRefGoogle ScholarPubMed
Coyle, J.T., Glutamate and schizophrenia: beyond the dopamine hypothesis. Cellular and Molecular Neurobiology, 2006. 26(4–6): 365384.CrossRefGoogle ScholarPubMed
Seamans, J.K., and Yang, C.R., The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 2004. 74(1): 158.CrossRefGoogle ScholarPubMed
Durstewitz, D., A few important points about dopamine’s role in neural network dynamics. Pharmacopsychiatry, 2006. 39(Suppl 1): S72S75.CrossRefGoogle ScholarPubMed
Durstewitz, D., Dopaminergic modulation of prefrontal cortex network dynamics, in Monoaminergic Modulation of Cortical Excitability, Tseng, K.-Y. and Atzori, M., editors. New York: Springer, 2007, pp. 217234.CrossRefGoogle Scholar
Winterer, G., and Weinberger, D.R., Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends in Neurosciences, 2004. 27(11): 683690.CrossRefGoogle Scholar
Rolls, E.T., The Brain, Emotion, and Depression. Oxford: Oxford University Press, 2018.Google Scholar
Rolls, E.T., The orbitofrontal cortex and emotion in health and disease, including depression. Neuropsychologia, 2019. 128:1443 doi: 10.1016/j.neuropsychologia.2017.09.021.Google Scholar
Rolls, E.T., The roles of the orbitofrontal cortex via the habenula in non-reward and depression, and in the responses of serotonin and dopamine neurons. Neuroscience and Biobehavioral Reviews, 2017. 75: 331334.CrossRefGoogle ScholarPubMed
Rolls, E.T., A non-reward attractor theory of depression. Neuroscience and Biobehavioral Reviews, 2016. 68: 4758.CrossRefGoogle Scholar
Cheng, W., et al., Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain, 2016. 139(Pt 12): 32963309.CrossRefGoogle ScholarPubMed
Wang, X.J., Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 2002. 36: 955968.CrossRefGoogle ScholarPubMed
Brody, C.D., Romo, R., and Kepecs, A., Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Current Opinion in Neurobiology, 2003. 13: 204211.CrossRefGoogle ScholarPubMed
Machens, C.K., Romo, R., and Brody, C.D., Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science, 2005. 307: 11211124.CrossRefGoogle ScholarPubMed
Wong, K.F., and Wang, X.J., A recurrent network mechanism of time integration in perceptual decisions. Journal of Neuroscience, 2006. 26(4): 13141328.CrossRefGoogle ScholarPubMed
Rolls, E.T., Emotion and Decision-Making Explained. Oxford: Oxford University Press, 2014.Google ScholarPubMed
O’Kane, D., and Treves, A., Why the simplest notion of neocortex as an autoassociative memory would not work. Network, 1992. 3: 379384.CrossRefGoogle Scholar
Rolls, E.T., Memory, Attention, and Decision-making: A Unifying Computational Neuroscience Approach. Oxford: Oxford University Press, 2008.Google Scholar
Rolls, E.T., Information representation, processing and storage in the brain: analysis at the single neuron level, in The Neural and Molecular Bases of Learning, Changeux, J.-P. and Konishi, M., editors. Chichester: Wiley, 1987, pp. 503540.Google Scholar
Rolls, E.T., Functions of neuronal networks in the hippocampus and neocortex in memory, in Neural Models of Plasticity: Experimental and Theoretical Approaches, Byrne, J.H. and Berry, W.O., editors. San Diego: Academic Press, 1989, pp. 240265.CrossRefGoogle Scholar
Rolls, E.T., The representation and storage of information in neuronal networks in the primate cerebral cortex and hippocampus, in The Computing Neuron, Durbin, R., Miall, C., and Mitchison, G., editors. Wokingham: Addison-Wesley, 1989, pp. 125159.Google Scholar
Rolls, E.T., Functions of neuronal networks in the hippocampus and cerebral cortex in memory, in Models of Brain Function, Cotterill, R.M.J., editor. Cambridge: Cambridge University Press, 1989, pp. 1533.Google Scholar
Rolls, E.T., Theoretical and neurophysiological analysis of the functions of the primate hippocampus in memory. Cold Spring Harbor Symposia in Quantitative Biology, 1990. 55: 9951006.CrossRefGoogle ScholarPubMed
Rolls, E.T., Functions of the primate hippocampus in spatial processing and memory, in Neurobiology of Comparative Cognition, Olton, D.S. and Kesner, R.P., editors. Hillsdale, NJ: Lawrence Erlbaum, 1990, pp. 339362.Google Scholar
Rolls, E.T., Functions of the primate hippocampus in spatial and non-spatial memory. Hippocampus, 1991. 1: 258261.CrossRefGoogle Scholar
Rolls, E.T., and Kesner, R.P., A computational theory of hippocampal function, and empirical tests of the theory. Progress in Neurobiology, 2006. 79: 148.CrossRefGoogle Scholar
Rolls, E.T., An attractor network in the hippocampus: theory and neurophysiology. Learning and Memory, 2007. 14: 714731.CrossRefGoogle ScholarPubMed
Rolls, E.T., The storage and recall of memories in the hippocampo-cortical system. Cell and Tissue Research, 2018. 373:577604. doi: 10.1007/s00441-017-2744-3.CrossRef
Marr, D., Simple memory: a theory for archicortex. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 1971. 262: 2381.CrossRefGoogle ScholarPubMed
McNaughton, B.L., and Morris, R.G.M., Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends in Neurosciences, 1987. 10(10): 408415.CrossRefGoogle Scholar
Levy, W.B., A computational approach to hippocampal function, in Computational Models of Learning in Simple Neural Systems, Hawkins, R.D. and Bower, G.H., editors. San Diego: Academic Press, 1989, pp. 243305.CrossRefGoogle Scholar
McNaughton, B.L., Associative pattern completion in hippocampal circuits: new evidence and new questions. Brain Research Reviews, 1991. 16: 193220.Google Scholar
McClelland, J.L., McNaughton, B.L., and O’Reilly, R.C., Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 1995. 102: 419457.CrossRefGoogle ScholarPubMed
Ishizuka, N., Weber, J., and Amaral, D.G., Organization of intrahippocampal projections originating from CA3 pyramidal cells in the rat. Journal of Comparative Neurology, 1990. 295: 580623.CrossRefGoogle ScholarPubMed
Kondo, H., Lavenex, P., and Amaral, D.G., Intrinsic connections of the macaque monkey hippocampal formation: II. CA3 connections. Journal of Comparative Neurology, 2009. 515(3): 349377.Google ScholarPubMed
Rolls, E.T., The primate hippocampus and episodic memory, in Handbook of Episodic Memory, Dere, E. et al., editors. Amsterdam: Elsevier, 2008, pp. 417438.CrossRefGoogle Scholar
Rolls, E.T., and Xiang, J.-Z., Spatial view cells in the primate hippocampus, and memory recall. Reviews in the Neurosciences, 2006. 17(1–2): 175200.CrossRefGoogle ScholarPubMed
Grady, C.L., Cognitive neuroscience of aging. Annals of the New York Academy of Science, 2008. 1124: 127144.CrossRefGoogle Scholar
Miller, E.K., The “working” of working memory. Dialogues in Clinical Neuroscience, 2013. 15(4): 411418.Google ScholarPubMed
Wang, M., et al., NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron, 2013. 77(4): 736749.CrossRefGoogle ScholarPubMed
Samson, R.D., and Barnes, C.A., Impact of aging brain circuits on cognition. European Journal of Neuroscience, 2013. 37(12): 19031915.CrossRefGoogle ScholarPubMed
Schliebs, R., and Arendt, T., The cholinergic system in aging and neuronal degeneration. Behavioural Brain Research, 2011. 221(2): 555563.CrossRefGoogle ScholarPubMed
Kelly, K.M., et al., The neurobiology of aging. Epilepsy Research, 2006. 68(Suppl 1): S5S20.CrossRefGoogle ScholarPubMed
Arnsten, A.F., and Jin, L.E., Molecular influences on working memory circuits in dorsolateral prefrontal cortex. Progress in Molecular Biology and Translational Science, 2014. 122: 211231.CrossRefGoogle ScholarPubMed
Goldman-Rakic, P.S., Muly, E.C., 3rd, and Williams, G.V., D(1) receptors in prefrontal cells and circuits. Brain Research Reviews, 2000. 31(2–3): 295301.CrossRefGoogle ScholarPubMed
Castner, S.A., Williams, G.V., and Goldman-Rakic, P.S., Reversal of antipsychotic-induced working memory deficits by short-term dopamine D1 receptor stimulation. Science, 2000. 287(5460): 20202022.CrossRefGoogle ScholarPubMed
Sikstrom, S., Computational perspectives on neuromodulation of aging. Acta Neurochirurgica Suppl, 2007. 97(Pt 2): 513518.CrossRefGoogle Scholar
Diamond, A., Evidence for the importance of dopamine for prefrontal cortex functions early in life. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 1996. 351(1346): 14831493; discussion 1494.Google ScholarPubMed
Diamond, A., Consequences of variations in genes that affect dopamine in prefrontal cortex. Cerebral Cortex, 2007. 17(Suppl 1): i161i170.CrossRefGoogle ScholarPubMed
Wang, M., et al., Alpha2A-adrenoceptors strengthen working memory networks by inhibiting cAMP-HCN channel signaling in prefrontal cortex. Cell, 2007. 129(2): 397410.CrossRefGoogle ScholarPubMed
He, C., et al., Neurophysiology of HCN channels: from cellular functions to multiple regulations. Progress in Neurobiology, 2014. 112: 123.CrossRefGoogle ScholarPubMed
Grudzien, A., et al., Locus coeruleus neurofibrillary degeneration in aging, mild cognitive impairment and early Alzheimer’s disease. Neurobiology of Aging, 2007. 28(3): 327335.CrossRefGoogle ScholarPubMed
Moore, T.L., et al., Cognitive impairment in aged rhesus monkeys associated with monoamine receptors in the prefrontal cortex. Behavioural Brain Research, 2005. 160(2): 208221.CrossRefGoogle ScholarPubMed
Downs, J.L., et al., Orexin neuronal changes in the locus coeruleus of the aging rhesus macaque. Neurobiology of Aging, 2007. 28(8): 12861295.CrossRefGoogle ScholarPubMed
Wang, M., et al., Neuronal basis of age-related working memory decline. Nature, 2011. 476(7359): 210213.CrossRefGoogle ScholarPubMed
Carlyle, B.C., et al., cAMP-PKA phosphorylation of tau confers risk for degeneration in aging association cortex. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111(13): 50365041.CrossRefGoogle ScholarPubMed
Kesner, R.P., and Rolls, E.T., Role of long term synaptic modification in short term memory. Hippocampus, 2001. 11: 240250.CrossRefGoogle ScholarPubMed
Lauterborn, J.C., et al., Chronic ampakine treatments stimulate dendritic growth and promote learning in middle-aged rats. Journal of Neuroscience, 2016. 36(5): 16361646.CrossRefGoogle ScholarPubMed
Burke, S.N. and Barnes, C.A., Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 2006. 7(1): 3040.CrossRefGoogle ScholarPubMed
Mesulam, N.-M., Human brain cholinergic pathways. Progress in Brain Research, 1990. 84: 231241.CrossRefGoogle ScholarPubMed
Baxter, M.G., and Bucci, D.J., Selective immunotoxic lesions of basal forebrain cholinergic neurons: twenty years of research and new directions. Behavioral Neuroscience, 2013. 127(5): 611618.CrossRefGoogle ScholarPubMed
Hasselmo, M.E., and Sarter, M., Modes and models of forebrain cholinergic neuromodulation of cognition. Neuropsychopharmacology, 2011. 36(1): 5273.CrossRefGoogle ScholarPubMed
Bear, M.F., and Singer, W., Modulation of visual cortical plasticity by acetylcholine and noradrenaline. Nature, 1986. 320: 172176.CrossRefGoogle ScholarPubMed
Fuhrmann, G., Markram, H., and Tsodyks, M., Spike frequency adaptation and neocortical rhythms. Journal of Neurophysiology, 2002. 88(2): 761770.CrossRefGoogle ScholarPubMed
Abbott, L.F., et al., Synaptic depression and cortical gain control. Science, 1997. 275(5297): 220224.CrossRefGoogle ScholarPubMed
Rolls, E.T., Burton, M.J., and Mora, F., Hypothalamic neuronal responses associated with the sight of food. Brain Research, 1976. 111(1): 5366.CrossRefGoogle ScholarPubMed
Mora, F., Rolls, E.T., and Burton, M.J., Modulation during learning of the responses of neurones in the lateral hypothalamus to the sight of food. Experimental Neurology, 1976. 53: 508519.CrossRefGoogle Scholar
Burton, M.J., Rolls, E.T., and Mora, F., Effects of hunger on the responses of neurones in the lateral hypothalamus to the sight and taste of food. Experimental Neurology, 1976. 51: 668677.CrossRefGoogle Scholar
Wilson, F.A.W., and Rolls, E.T., Learning and memory are reflected in the responses of reinforcement-related neurons in the primate basal forebrain. Journal of Neuroscience, 1990. 10: 12541267.CrossRefGoogle ScholarPubMed
Wilson, F.A.W., and Rolls, E.T., Neuronal responses related to reinforcement in the primate basal forebrain. Brain Research, 1990. 509: 213231.CrossRefGoogle ScholarPubMed
Rolls, E.T., et al., Activity of neurones in different forebrain structures during visual discrimination learning in the monkey. Experimental Brain Research, 1979. 32: R39R40.Google Scholar
Wilson, F.A.W., and Rolls, E.T., Neuronal responses related to the novelty and familiarity of visual stimuli in the substantia innominata, diagonal band of Broca and periventricular region of the primate. Experimental Brain Research, 1990. 80: 104120.CrossRefGoogle ScholarPubMed
Rolls, E.T., et al., Neuronal responses related to visual recognition. Brain, 1982. 105: 611646.CrossRefGoogle ScholarPubMed
Amaral, D.G., et al., Anatomical organization of the primate amygdaloid complex, in The Amygdala, Aggleton, J.P., editor. New York: Wiley-Liss, 1992, pp. 166.Google Scholar
Giocomo, L.M., and Hasselmo, M.E., Neuromodulation by glutamate and acetylcholine can change circuit dynamics by regulating the relative influence of afferent input and excitatory feedback. Molecular Neurobiology, 2007. 36(2): 184200.CrossRefGoogle ScholarPubMed
Gil, Z., Connors, B.W., and Amitai, Y., Differential regulation of neocortical synapses by neuromodulators and activity. Neuron, 1997. 19(3): 679686.CrossRefGoogle ScholarPubMed
Disney, A.A., Domakonda, K.V., and Aoki, C., Differential expression of muscarinic acetylcholine receptors across excitatory and inhibitory cells in visual cortical areas V1 and V2 of the macaque monkey. Journal of Comparative Neurology, 2006. 499(1): 4963.CrossRefGoogle ScholarPubMed
Deco, G., and Thiele, A., Cholinergic control of cortical network interactions enables feedback-mediated attentional modulation. European Journal of Neuroscience, 2011. 34(1): 146157.CrossRefGoogle ScholarPubMed
Power, J.M., and Sah, P., Competition between calcium-activated K+ channels determines cholinergic action on firing properties of basolateral amygdala projection neurons. Journal of Neuroscience, 2008. 28(12): 32093220.CrossRefGoogle ScholarPubMed
Adelman, J.P., Maylie, J., and Sah, P., Small-conductance Ca2+-activated K+ channels: form and function. Annual Review of Physiology, 2012. 74: 245269.CrossRefGoogle ScholarPubMed
Sah, P., and Faber, E.S., Channels underlying neuronal calcium-activated potassium currents. Progress in Neurobiology, 2002. 66(5): 345353.CrossRefGoogle ScholarPubMed
Tovee, M.J., et al., Information encoding and the responses of single neurons in the primate temporal visual cortex. Journal of Neurophysiology, 1993. 70(2): 640654.CrossRefGoogle ScholarPubMed
Liu, Y.H., and Wang, X.J., Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 2001. 10(1): 2545.CrossRefGoogle ScholarPubMed
Lin, C.H., Lane, H.Y., and Tsai, G.E., Glutamate signaling in the pathophysiology and therapy of schizophrenia. Pharmacology Biochemistry and Behavior, 2012. 100(4): 665677.CrossRefGoogle ScholarPubMed
Levin, E.D., Complex relationships of nicotinic receptor actions and cognitive functions. Biochemical Pharmacology, 2013. 86(8): 11451152.CrossRefGoogle ScholarPubMed
Zurkovsky, L., Taylor, W.D., and Newhouse, P.A., Cognition as a therapeutic target in late-life depression: potential for nicotinic therapeutics. Biochemical Pharmacology, 2013. 86(8): 11331144.CrossRefGoogle ScholarPubMed
Hu, N.W., Ondrejcak, T., and Rowan, M.J., Glutamate receptors in preclinical research on Alzheimer’s disease: update on recent advances. Pharmacology Biochemistry Behavior, 2012. 100(4): 855862.CrossRefGoogle ScholarPubMed
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