Models of hippocampus

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Author: Dr. Michael E. Hasselmo, Center for Memory and Brain, Boston University, Boston, MA

This is article is a draft in preparation.

Models of hippocampus simulate physiological phenomena or behavioral functions of the hippocampus, using the techniques of computational neuroscience.

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Anatomical overview

The hippocampus is a three-layered cortical structure at the border of the neocortex that receives afferent input from cortical regions processing a range of sensory modalities. This input arrives via the entorhinal cortex and other parahippocampal regions. The output of the hippocampus projects back to parahippocampal cortices and to subcortical structures via the fornix. Models of hippocampus commonly include representations of neuron function within different subregions of the hippocampal formation, including the entorhinal cortex, the dentate gyrus, and cornu ammonis regions CA3 and CA1. There is less focus on region CA4 (consisting of cells in the hilus of the dentate gyrus) and CA2 (consisting of neurons receiving both Schaffer collateral and mossy fiber input).

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Types of models

Models of hippocampus range across many levels of abstractions. These include:

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Behavioral functions

Models of hippocampus address the role of hippocampus in encoding and retrieval of information for a range of different memory-guided behaviors in humans and other animals. Many models were motivated by the effect of damage to the hippocampus by ischemia or encephalitis, and by the bilateral removal of the anterior hippocampus in patient HM. Damage to the hippocampus in human subjects causes severe impairments for encoding of new Episodic memory. In humans, hippocampal lesions cause impairments of performance on memory tasks including free recall and cued recall of paired associates. Lesions also appear to impair recollection-based recognition, with less effect on familiarity based recognition memory. Hippocampal lesions have little effect on priming and perceptual memory.

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Human verbal memory

“Models of hippocampus” in humans have explicitly modeled performance on free recall and cued recall (Hasselmo and Wyble, 1997) as well as performance in recollection-based recognition (Norman and O’Reilly, 2000). Few models have addressed the complex features of episodic memory such as the sense of reliving a full series of events, but recent models have addressed the episodic encoding and retrieval of complex spatial trajectories (Hasselmo, 2007).

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Rat memory function

In rats, hippocampal lesions cause impairments in a range of different tasks. These include impairments in tasks requiring memory of spatial locations, such as the *Morris water maze, the *8-arm radial maze, as well as delayed spatial alternation and spatial reversal. These also include impairments in non-spatial tasks requiring relational memory such as the transitivity and transitive inference tasks and tasks requiring aspects of sequence memory such as the order of items in a list (Kesner) or the end of overlapping lists (Agster et al., 2002). Hippocampal lesions also impair trace conditioning in classical conditioning paradigms.

Models of hippocampus in rats have been developed to guide behavior in the Morris water maze (Redish and Touretzky, 1997; Gerstner and Abbott, 1997) as well as spatial alternation (Hasselmo and Eichenbaum, 2005). Other models have addressed the role of the hippocampus in trace conditioning (Rodriguez and Levy, 2000; Schmajuk and Grossberg, 1984) and phenomena such as blocking (Myers et al., 2003).

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Function of hippocampal subregions

Models of hippocampus have addressed the potential function of individual hippocampal subregions. These include the following:

  • Pattern separation in dentate gyrus.
  • Pattern completion in region CA3.
  • Comparison of retrieval with input in region CA1.
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Physiological phenomena

Models of hippocampus address several important physiological phenomena observed within the hippocampus. These physiological phenomena include:

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Long-term potentiation and depression

The properties of synaptic modification in the hippocampus constitute an essential component of most hippocampal models. In particular, considerable research focuses on the dependence of synaptic modification on the combination of presynaptic and post-synaptic activity. Modification dependent on presynaptic and postsynaptic activity is referred to as Hebbian. Activation of the NMDA receptor depends upon presynaptic glutamate release and postsynaptic depolarization, and plays an important role in many forms of Hebbian long-term potentiation. The timing properties of the NMDA receptor appear to result in a requirement for a tight timing relationship between presynaptic spikes and post-synaptic activity (Holmes and Levy, 1990). This requirement has been shown in a number of studies and is referred to currently as spike timing dependent plasticity (STDP). Many synaptic models address the mechanisms of long-term potentiation and depression. Specific models address the role of molecular pathways in regulating synaptic strength, including the role of bistability of autophosphorylation, or the role of different concentrations of calcium induced by different patterns of input. Many models have addressed the potential cellular mechanisms for spike timing dependent plasticity.

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Intrinsic properties

Biophysically detailed models of cells in the hippocampal formation have explored the cellular mechanisms for spiking properties of the hippocampus, including phenomena such as Bursting and spike-frequency accommodation or adaptation. Single cell models have demonstrated potential cellular mechanisms for bursting activity and adaptation (Traub et al., 2000). These spiking properties can be represented in simplified models (Izhikevich, 2000).

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Spiking activity in behaving animals

Hippocampal models have addressed physiological data showing correlations between hippocampal spiking activity and a number of different variables of behavior. For example, models have addressed the appearance of place cells in the hippocampus. These are neurons that fire in a highly localized manner within the environment. Early models showed how these could arise from competitive self-organization of inputs from sensory cues (Sharp, 1990) or from error-correcting rules guiding formation of place cells. Later models have addressed how they could arise from the properties of grid cells in the entorhinal cortex, that provides afferent input to the hippocampus (Solstad et al. 2006). Models have also demonstrated how spiking activity can depend upon variables other than current location, including the presence of specific sensory stimuli or on prior history (Hasselmo and Eichenbaum, 2005).

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Local field potential dynamics

Hippocampal models have also addressed properties of the oscillatory dynamics of the hippocampal formation measured by electroencephalographic recordings of the local field potential. In particular, during active movement through the environment, the hippocampus shows prominent activity in the theta frequency band, with a peak in the power spectra around 6 to 7 Hz. In anesthetized rats some of the same mechanisms contribute to oscillations around 3-4 Hz that are also referred to as theta rhythm. The mechanisms of theta rhythm have been modeled as due to feedback interactions between the medial septum and hippocampus (Borisyuk and Dehnam, 2003).

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Interaction of spiking and local field potentials

The spiking activity of the hippocampus shows clear relationships to local field potential oscillations. In particular, the firing of place cells on a linear track shows a systematic change in phase of firing relative to theta rhythm oscillations. When the rat first enters the field of firing of a place cell, the spiking occurs at late phases, and then it shifts to earlier theta phases as the rat moves through the place field. This phenomenon is referred to as theta phase precession. A number of models have addressed theta phase precession using different mechanisms. These can be grouped into three broad categories:

  • Phase precession from sequence readout. In these models, each location cues the retrieval of a sequence of place cell spiking activity due to Hebbian modification of associations between place cells firing to adjacent locations (Tsodyks et al., 1996; Jensen and Lisman, 1996; Wallenstein and Hasselmo, 1997).
  • Phase precession from oscillatory interference. In these models, precession arises from interference between oscillators with different frequency. This can involve interference of intrinsic oscillations with network oscillations (O’Keefe and Recce, 1993; Lengyel et al., 2003) or between oscillations in different groups of neurons (Bose et al., 2000).
  • Phase precession from progressive change in depolarization relative to network theta. In these models, precession arises from a gradual change in depolarization that results in spiking at a different phase of theta (Kamondi et al., 1998; Mehta et al., 2002).
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References

  • Anderson, J. A. (1972). A simple neural network generating an interactive memory. Math Biosci 14, 197-220.
  • Bi, G. Q., and Poo, M. M. (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18, 10464-10472.
  • Blum, K. I., and Abbott, L. F. (1996). A model of spatial map formation in the hippocampus of the rat. Neural Comput 8, 85-93.
  • Burgess, N., Donnett, J. G., Jeffery, K. J., and O'Keefe, J. (1997). Robotic and neuronal simulation of the hippocampus and rat navigation. Philos Trans R Soc Lond B Biol Sci 352, 1535-1543.
  • Buzsaki, G. (2002). Theta oscillations in the hippocampus. Neuron 33, 325-340.
  • Cannon, R. C., Hasselmo, M. E., and Koene, R. A. (2003). From biophysics to behavior: Catacomb2 and the design of biologically-plausible models for spatial navigation. Neuroinformatics 1, 3-42.
  • Grossberg, S. (1975). A neural model of attention, reinforcement, and discrimination learning. International Review of Neurobiology 18, 263-327.
  • Hasselmo, M., Cannon, R. C., and Koene, R. A. (2002a). A simulation of parahippocampal and hippocampal structures guiding spatial navigation of a virtual rat in a virtual environment: A functional framework for theta theory. In The parahippocampal region: organisation and role in cognitive functions, F. G. Wouterlood, ed. (Oxford, Oxford University Press), pp. 139-161.
  • Hasselmo, M. E., Bodelon, C., and Wyble, B. P. (2002b). A proposed function for hippocampal theta rhythm: Separate phases of encoding and retrieval enhance reversal of prior learning. Neural Computation 14, 793-817.
  • Hasselmo, M. E., and Eichenbaum, H. (2005). Hippocampal mechanisms for the context-dependent retrieval of episodes. Neural Networks in press.
  • Hasselmo, M. E., Hay, J., Ilyn, M., and Gorchetchnikov, A. (2002c). Neuromodulation, theta rhythm and rat spatial navigation. Neural Networks 15, 689-707.
  • Hasselmo, M. E., and Schnell, E. (1994). Laminar selectivity of the cholinergic suppression of synaptic transmission in rat hippocampal region CA1: computational modeling and brain slice physiology. J Neurosci 14, 3898-3914.
  • Hasselmo, M. E., and Wyble, B. P. (1997). Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function. Behav Brain Res 89, 1-34.
  • Howard, M. W., Fotedar, M. S., Datey, A. S., and Hasselmo, M. (2004). The temporal context model in spatial navigation and relational learning: Explaining medial temporal lobe function across domains. Psychological Review 112, 75-116.
  • Howard, M. W., and Kahana, M. J. (2002). A distributed representation of temporal context. J Math Psychol 46, 269-299.
  • Jensen, O., and Lisman, J. E. (1996a). Hippocampal CA3 region predicts memory sequences: accounting for the phase precession of place cells. Learning and Memory 3, 279-287.
  • Jensen, O., and Lisman, J. E. (1996b). Novel lists of 7+/-2 known items can be reliably stored in an oscillatory short-term memory network: interaction with long-term memory. Learning and Memory 3, 257-263.
  • Kamondi, A., Acsady, L., Wang, X. J., and Buzsaki, G. (1998). Theta oscillations in somata and dendrites of hippocampal pyramidal cells in vivo: activity-dependent phase-precession of action potentials. Hippocampus 8, 244-261.
  • Koene, R. A., Gorchetchnikov, A., Cannon, R. C., and Hasselmo, M. E. (2003). Modeling goal-directed spatial navigation in the rat based on physiological data from the hippocampal formation. Neural Netw 16, 577-584.
  • Leung, L.-W. S. (1984). Model of gradual phase shift of theta rhythm in the rat. J Neurophysiol 52, 1051-1065.
  • Levy, W. B. (1996). A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks. Hippocampus 6, 579-590.
  • Levy, W. B., and Steward, O. (1983). Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8, 791-797.
  • Lisman, J. E. (1999). Relating hippocampal circuitry to function: recall of memory sequences by reciprocal dentate-CA3 interactions. Neuron 22, 233-242.
  • Lisman, J. E., and Idiart, M. A. (1995). Storage of 7 +/- 2 short-term memories in oscillatory subcycles. Science 267, 1512-1515.
  • Marr, D. (1971). Simple memory: A theory for archicortex. Phil Trans Roy Soc B B262, 23-81.
  • McNaughton, B. L. (1991). Associative pattern completion in hippocampal circuits: New evidence and new questions. Brain Res Rev 16, 193-220.
  • McNaughton, B. L., and Morris, R. G. M. (1987). Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci 10, 408-415.
  • Mehta, M. R., Lee, A. K., and Wilson, M. A. (2002). Role of experience and oscillations in transforming a rate code into a temporal code. Nature 417, 741-746.
  • Muller, R. U., and Stead, M. (1996). Hippocampal place cells connected by Hebbian synapses can solve spatial problems. Hippocampus 6, 709-719.
  • Norman, K. A., and O'Reilly, R. C. (2003). Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol Rev 110, 611-646.
  • O'Keefe, J., and Nadel, L. (1978). The Hippocampus as a Cognitive Map (Oxford, UK, Oxford University Press).
  • O'Keefe, J., and Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317-330.
  • O'Reilly, R. C., and McClelland, J. L. (1994). Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus 4, 661-682.
  • Redish, A. D., and Touretzky, D. S. (1998). The role of the hippocampus in solving the Morris water maze. Neural Comput 10, 73-111.
  • Skaggs, W. E., McNaughton, B. L., Wilson, M. A., and Barnes, C. A. (1996). Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6, 149-172.
  • Sohal, V. S., and Hasselmo, M. E. (1998a). Changes in GABAB modulation during a theta cycle may be analogous to the fall of temperature during annealing. Neural Comput 10, 869-882.
  • Sohal, V. S., and Hasselmo, M. E. (1998b). GABA(B) modulation improves sequence disambiguation in computational models of hippocampal region CA3. Hippocampus 8, 171-193.
  • Treves, A., and Rolls, E. T. (1992). Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus 2, 189-199.
  • Treves, A., and Rolls, E. T. (1994). Computational analysis of the role of the hippocampus in memory. Hippocampus 4, 374-391.
  • Tsodyks, M. V., Skaggs, W. E., Sejnowski, T. J., and McNaughton, B. L. (1996). Population dynamics and theta rhythm phase precession of hippocampal place cell firing: a spiking neuron model. Hippocampus 6, 271-280.
  • Wallenstein, G. V., and Hasselmo, M. E. (1997). GABAergic modulation of hippocampal population activity: sequence learning, place field development, and the phase precession effect. J Neurophysiol 78, 393-408.
  • Wyble, B. P., Linster, C., and Hasselmo, M. E. (2000). Size of CA1-evoked synaptic potentials is related to theta rhythm phase in rat hippocampus. J Neurophysiol 83, 2138-2144.
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Further reading

  • Hasselmo, M.E. and Eichenbaum, H. (2005) Hippocampal mechanisms for the context-dependent retrieval of episodes. Neural Networks, 18(9):1172-1190. PDF
  • Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. PDF
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See also

Hodgkin-Huxley model, Integrate-and-fire neuron, Wilson-Cowan model, Connectionist models,Hippocampus,Memory

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External links

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