Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-17T11:24:15.407Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  30 November 2019

By
Edmund T. Rolls
Edited by
Kenneth M. Heilman  and
Stephen E. Nadeau
Kenneth M. Heilman
Affiliation:
University of Florida
Stephen E. Nadeau
Affiliation:
University of Florida
Get access

Summary

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.

Keywords

short- and long-term memoryattentionagingcognitive functionepisodic memoryneural networkautoassociation networkcomputational neurosciencestochastic dynamics
Type
Chapter
Information
Cognitive Changes and the Aging Brain , pp. 203 - 225
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Rolls, E.T., Cerebral Cortex: Principles of Operation. Oxford: Oxford University Press, 2016.Google Scholar
Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 1982. 79: 2554–2558.Google Scholar
Amit, D.J., Modeling Brain Function. Cambridge: Cambridge University Press, 1989.Google Scholar
Hertz, J., Krogh, A., and Palmer, R.G., An Introduction to the Theory of Neural Computation. Wokingham: Addison-Wesley, 1991.Google 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: 150–161.Google 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. 129–167.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: 371–397.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: 407–421.Google Scholar
Treves, A., Graded-response neurons and information encodings in autoassociative memories. Physical Review A, 1990. 42: 2418–2430.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: 189–199.Google Scholar
Rolls, E.T., et al., Simulation studies of the CA3 hippocampal subfield modelled as an attractor neural network. Neural Networks, 1997. 10: 1559–1569.Google Scholar
Amaral, D.G., Ishizuka, N., and Claiborne, B., Neurons, numbers and the hippocampal network. Progress in Brain Research, 1990. 83: 1–11.Google Scholar
Treves, A., and Rolls, E.T., A computational analysis of the role of the hippocampus in memory. Hippocampus, 1994. 4: 374–391.Google Scholar
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: 92–147.Google Scholar
Rolls, E.T., Advantages of dilution in the connectivity of attractor networks in the brain. Biologically Inspired Cognitive Architectures, 2012. 1: 44–54.Google 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: 11–21.Google Scholar
Rolls, E.T., Consciousness absent and present: a neurophysiological exploration. Progress in Brain Research, 2003. 144: 95–106.Google Scholar
Panzeri, S., et al., Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons. Network, 2001. 12(4): 423–440.Google Scholar
Treves, A., Mean-field analysis of neuronal spike dynamics. Network, 1993. 4: 259–284.CrossRefGoogle Scholar
Battaglia, F.P., and Treves, A., Stable and rapid recurrent processing in realistic auto-associative memories. Neural Computation, 1998. 10: 431–450.Google Scholar
Braitenberg, V., and Schütz, A., Anatomy of the Cortex. Berlin: Springer-Verlag, 1991.Google Scholar
Abeles, M., Corticonics: Neural Circuits of the Cerebral Cortex. New York: Cambridge University Press, 1991.Google 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: 205–221.Google Scholar
Goldman-Rakic, P.S., Cellular basis of working memory. Neuron, 1995. 14: 477–485.Google Scholar
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: 1445–1453.Google Scholar
Fuster, J.M., Executive frontal functions. Experimental Brain Research, 2000. 133(1): 66–70.Google Scholar
Fuster, J.M., and Alexander, G.E., Neuron activity related to short-term memory. Science, 1971. 173: 652–654.Google Scholar
Kubota, K., and Niki, H., Prefrontal cortical unit activity and delayed alternation performance in monkeys. Journal of Neurophysiology, 1971. 34(3): 337–347.Google Scholar
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: 331–349.Google 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. 171–177.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: 1525–1531.Google 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. 85–95.Google 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): 2125–2195.CrossRefGoogle ScholarPubMed
Deco, G., Rolls, E.T., and Romo, R., Stochastic dynamics as a principle of brain function. Progress in Neurobiology, 2009. 88: 1–16.Google Scholar
Rolls, E.T. and Deco, G., The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function. Oxford: Oxford University Press, 2010.Google 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: 63–85.CrossRefGoogle Scholar
Durstewitz, D., Seamans, J.K., and Sejnowski, T.J., Neurocomputational models of working memory. Nature Neuroscience, 2000. 3 Suppl: 1184–1191.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: 696–709.Google Scholar
Rolls, E.T., Loh, M., and Deco, G., An attractor hypothesis of obsessive-compulsive disorder. European Journal of Neuroscience, 2008. 28: 782–793.Google Scholar
Brunel, N., and Hakim, V., Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, 1999. 11(7): 1621–1671.Google Scholar
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: 51917–51919.Google 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.Google Scholar
Deco, G., and Rolls, E.T., Decision-making and Weber’s Law: a neurophysiological model. European Journal of Neuroscience, 2006. 24: 901–916.Google Scholar
Faisal, A.A., Selen, L.P., and Wolpert, D.M., Noise in the nervous system. Nature Reviews Neuroscience, 2008. 9(4): 292–303.Google 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): 273–275.Google Scholar
Wang, X.-J., Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. Journal of Neuroscience, 1999. 19(21): 9587–9603.Google Scholar
Compte, A., et al., Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb Cortex, 2000. 10(9): 910–923.Google Scholar
Wang, X.J., Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences, 2001. 24(8): 455–463.CrossRefGoogle ScholarPubMed
Tegner, J., Compte, A., and Wang, X.J., The dynamical stability of reverberatory neural circuits. Biological Cybernetics, 2002. 87(5–6): 471–481.Google Scholar
Rolls, E.T., Emotion Explained. Oxford: Oxford University Press, 2005.Google 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: 318–327.Google Scholar
Coyle, J.T., Glutamate and schizophrenia: beyond the dopamine hypothesis. Cellular and Molecular Neurobiology, 2006. 26(4–6): 365–384.Google Scholar
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): 1–58.Google Scholar
Durstewitz, D., A few important points about dopamine’s role in neural network dynamics. Pharmacopsychiatry, 2006. 39(Suppl 1): S72–S75.Google Scholar
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. 217–234.Google Scholar
Winterer, G., and Weinberger, D.R., Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends in Neurosciences, 2004. 27(11): 683–690.Google 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. 128:14–43 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: 331–334.Google Scholar
Rolls, E.T., A non-reward attractor theory of depression. Neuroscience and Biobehavioral Reviews, 2016. 68: 47–58.CrossRefGoogle ScholarPubMed
Cheng, W., et al., Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain, 2016. 139(Pt 12): 3296–3309.Google Scholar
Wang, X.J., Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 2002. 36: 955–968.Google Scholar
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: 204–211.Google Scholar
Machens, C.K., Romo, R., and Brody, C.D., Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science, 2005. 307: 1121–1124.Google Scholar
Wong, K.F., and Wang, X.J., A recurrent network mechanism of time integration in perceptual decisions. Journal of Neuroscience, 2006. 26(4): 1314–1328.Google Scholar
Rolls, E.T., Emotion and Decision-Making Explained. Oxford: Oxford University Press, 2014.Google Scholar
O’Kane, D., and Treves, A., Why the simplest notion of neocortex as an autoassociative memory would not work. Network, 1992. 3: 379–384.Google 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. 503–540.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. 240–265.Google 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. 125–159.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. 15–33.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: 995–1006.Google Scholar
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. 339–362.Google Scholar
Rolls, E.T., Functions of the primate hippocampus in spatial and non-spatial memory. Hippocampus, 1991. 1: 258–261.Google 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: 1–48.Google Scholar
Rolls, E.T., An attractor network in the hippocampus: theory and neurophysiology. Learning and Memory, 2007. 14: 714–731.CrossRefGoogle ScholarPubMed
Rolls, E.T., The storage and recall of memories in the hippocampo-cortical system. Cell and Tissue Research, 2018. 373:577–604. doi: 10.1007/s00441-017-2744-3.Google Scholar
Marr, D., Simple memory: a theory for archicortex. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 1971. 262: 23–81.Google 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): 408–415.Google 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. 243–305.Google Scholar
McNaughton, B.L., Associative pattern completion in hippocampal circuits: new evidence and new questions. Brain Research Reviews, 1991. 16: 193–220.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: 419–457.Google Scholar
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: 580–623.Google Scholar
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): 349–377.Google Scholar
Rolls, E.T., The primate hippocampus and episodic memory, in Handbook of Episodic Memory, Dere, E. et al., editors. Amsterdam: Elsevier, 2008, pp. 417–438.Google 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): 175–200.Google Scholar
Grady, C.L., Cognitive neuroscience of aging. Annals of the New York Academy of Science, 2008. 1124: 127–144.Google Scholar
Miller, E.K., The “working” of working memory. Dialogues in Clinical Neuroscience, 2013. 15(4): 411–418.Google Scholar
Wang, M., et al., NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron, 2013. 77(4): 736–749.Google Scholar
Samson, R.D., and Barnes, C.A., Impact of aging brain circuits on cognition. European Journal of Neuroscience, 2013. 37(12): 1903–1915.Google Scholar
Schliebs, R., and Arendt, T., The cholinergic system in aging and neuronal degeneration. Behavioural Brain Research, 2011. 221(2): 555–563.Google Scholar
Kelly, K.M., et al., The neurobiology of aging. Epilepsy Research, 2006. 68(Suppl 1): S5–S20.Google Scholar
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: 211–231.Google Scholar
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): 295–301.Google Scholar
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): 2020–2022.Google Scholar
Sikstrom, S., Computational perspectives on neuromodulation of aging. Acta Neurochirurgica Suppl, 2007. 97(Pt 2): 513–518.Google 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): 1483–1493; discussion 1494.Google Scholar
Diamond, A., Consequences of variations in genes that affect dopamine in prefrontal cortex. Cerebral Cortex, 2007. 17(Suppl 1): i161–i170.Google Scholar
Wang, M., et al., Alpha2A-adrenoceptors strengthen working memory networks by inhibiting cAMP-HCN channel signaling in prefrontal cortex. Cell, 2007. 129(2): 397–410.Google Scholar
He, C., et al., Neurophysiology of HCN channels: from cellular functions to multiple regulations. Progress in Neurobiology, 2014. 112: 1–23.Google Scholar
Grudzien, A., et al., Locus coeruleus neurofibrillary degeneration in aging, mild cognitive impairment and early Alzheimer’s disease. Neurobiology of Aging, 2007. 28(3): 327–335.Google Scholar
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): 208–221.Google Scholar
Downs, J.L., et al., Orexin neuronal changes in the locus coeruleus of the aging rhesus macaque. Neurobiology of Aging, 2007. 28(8): 1286–1295.Google Scholar
Wang, M., et al., Neuronal basis of age-related working memory decline. Nature, 2011. 476(7359): 210–213.Google Scholar
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): 5036–5041.Google Scholar
Kesner, R.P., and Rolls, E.T., Role of long term synaptic modification in short term memory. Hippocampus, 2001. 11: 240–250.Google Scholar
Lauterborn, J.C., et al., Chronic ampakine treatments stimulate dendritic growth and promote learning in middle-aged rats. Journal of Neuroscience, 2016. 36(5): 1636–1646.Google Scholar
Burke, S.N. and Barnes, C.A., Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 2006. 7(1): 30–40.Google Scholar
Mesulam, N.-M., Human brain cholinergic pathways. Progress in Brain Research, 1990. 84: 231–241.Google Scholar
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): 611–618.Google Scholar
Hasselmo, M.E., and Sarter, M., Modes and models of forebrain cholinergic neuromodulation of cognition. Neuropsychopharmacology, 2011. 36(1): 52–73.Google Scholar
Bear, M.F., and Singer, W., Modulation of visual cortical plasticity by acetylcholine and noradrenaline. Nature, 1986. 320: 172–176.Google Scholar
Fuhrmann, G., Markram, H., and Tsodyks, M., Spike frequency adaptation and neocortical rhythms. Journal of Neurophysiology, 2002. 88(2): 761–770.CrossRefGoogle ScholarPubMed
Abbott, L.F., et al., Synaptic depression and cortical gain control. Science, 1997. 275(5297): 220–224.Google Scholar
Rolls, E.T., Burton, M.J., and Mora, F., Hypothalamic neuronal responses associated with the sight of food. Brain Research, 1976. 111(1): 53–66.Google Scholar
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: 508–519.Google 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: 668–677.Google 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: 1254–1267.Google Scholar
Wilson, F.A.W., and Rolls, E.T., Neuronal responses related to reinforcement in the primate basal forebrain. Brain Research, 1990. 509: 213–231.Google Scholar
Rolls, E.T., et al., Activity of neurones in different forebrain structures during visual discrimination learning in the monkey. Experimental Brain Research, 1979. 32: R39–R40.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: 104–120.Google Scholar
Rolls, E.T., et al., Neuronal responses related to visual recognition. Brain, 1982. 105: 611–646.Google Scholar
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. 1–66.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): 184–200.Google Scholar
Gil, Z., Connors, B.W., and Amitai, Y., Differential regulation of neocortical synapses by neuromodulators and activity. Neuron, 1997. 19(3): 679–686.Google Scholar
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): 49–63.Google Scholar
Deco, G., and Thiele, A., Cholinergic control of cortical network interactions enables feedback-mediated attentional modulation. European Journal of Neuroscience, 2011. 34(1): 146–157.Google Scholar
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): 3209–3220.Google Scholar
Adelman, J.P., Maylie, J., and Sah, P., Small-conductance Ca2+-activated K+ channels: form and function. Annual Review of Physiology, 2012. 74: 245–269.Google Scholar
Sah, P., and Faber, E.S., Channels underlying neuronal calcium-activated potassium currents. Progress in Neurobiology, 2002. 66(5): 345–353.Google Scholar
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): 640–654.Google Scholar
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): 25–45.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): 665–677.Google Scholar
Levin, E.D., Complex relationships of nicotinic receptor actions and cognitive functions. Biochemical Pharmacology, 2013. 86(8): 1145–1152.Google Scholar
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): 1133–1144.Google Scholar
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): 855–862.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×