Hippocampus

• Prof. Gyorgy Buzsaki, NYU Neuroscience Institute, New York University, New York, NY, USA

Figure 1: Regions of the hippocampus in the human (left) and rat (right) brains. Uncus and corpus (body) of the human hippocampus correspond to the ventral quadrant, while the tail to the dorsal part in the rat, respectively.

The hippocampus is a part of the forebrain, located in the medial temporal lobe. It is critical for the formation of those kinds of memories, which can be consciously declared. Due to its self-generated network patterns, newly acquired memories are gradually transferred to neocortical stores through the process of memory consolidation. The hippocampus, as the brain’s search engine also allows a fast and efficient search among the deposited memories in the neocortex, which is a process essential for planning the future and generating creative ideas. Most physiological studies on the hippocampus have been performed in rodents and gave rise to the spatial navigation theory. This, however, does not reflect a species difference. Instead, it appears that neural algorithms, perhaps evolved initially for computing first-order (neighborhood) and higher order (e.g., short-cuts, detours) distances in the physical world, are the same as those used for the navigation in cognitive space during recall and planning. Nearly all hippocampal functions are performed in collaboration with several of its partners, of which the most prominent is the entorhinal cortex, and strongly influenced by subcortical neuromodulators.

Hippocampal connections constrain functional operations

Figure 2: Main excitatory paths in the entorhinal-hippocampal feedforward loop. Parallel-organized local circuits (gc, granule cells; CA1 and layer 3 entorhinal pyramidal neurons) alternate with varying degrees of recurrent circuits (CA3 and principal cells of layers 5 and 2 of entorhinal cortex). Such organization allows for repeated segregation and integration of information in successively coupled recurrent and parallel circuits, respectively. Main excitatory inputs and outputs from the loop are indicated by arrows. The subicular complex (Sub-c; subiculum, presubiculum and parasubiculum) is another loop between the hippocampus and entorhinal cortex. The main cortical output of the hippocampus is CA1, whereas hippocampal information is routed to subcortical structures largely by way of the subiculum.

The banana-shaped hippocampus can be conceived as an appendage to the neocortex (Fig. 1). By way of its main partner, the entorhinal cortex, it communicates with all parts of the neocortex (Amaral and Lavenex, 2006). Two important principles emerge from such connectivity. First, since the main output target of the hippocampus are the same as its input source, its expected physiological contribution is to modify the connections of its inputs (i.e., circuits in the neocortex). Second, since all parts of the neocortex are represented in the hippocampus in a compressed manner and all neocortical regions can be addressed by the hippocampal-entorhinal output, hippocampal operations in different species largely reflect the nature of neocortical operations. With the large expansion of the neocortex during the mammalian evolution, the relatively small representation of associational areas in the ventral quadrant of the rodent hippocampus has become enlarged in primates (uncus and body; Fig. 1). A consequence of this anatomical organization is that insights about the global function of the hippocampus depend on the portion of the hippocampus being investigated and the species. Studying the physiological pattern of the dorsal hippocampus of the rat (corresponding largely to the tail of the primate hippocampus) gave rise to the cognitive map theory (O’Keefe and Nadel, 1978). In contrast, human studies involving surgical removal of the uncus and body of the hippocampus in drug treatment-resistant cases of epilepsy, such as the famous patient H. M., lead to the conclusion that the hippocampus is responsible for generating personal (autobiographical) or episodic memories (Scoville and Milner, 1957). These conceptual differences therefore should not be viewed as different functions from the viewpoint of the hippocampus (since hippocampal operations remain largely the same across all species and in all parts of the hippocampus). Instead, one may hypothesize that the neural algorithms that evolved initially for the computation of first-order (neighborhood) and higher order (e.g., short-cuts, detours) distances in the physical world are fundamentally the same as those used for navigation in cognitive space and for the computation of relationships among perceived, conceived or imagined items. Making a map requires exploration of the environment by self-referenced (egocentric) dead-reckoning type of navigation, (i.e., the same method as used by Christopher Columbus to discover the New World). Similarly, generation of semantic (i.e., self-independent or allocentric) knowledge requires prior self-referenced episodic experience. For these reasons, it has been hypothesized that the mechanisms underlying dead reckoning navigation and episodic memory ('navigation in cognitive space') are the same. Similarly, the mechanisms that support the cognitive map and semantic knowledge are also identical (Buzsáki, 2006).

Figure 3: Relationship between navigation and memory.

Information in the multisynaptic feedforward loops of the entorhinal-hippocampal system is propelled mainly unidirectionally (Fig. 2). An important feature of this complex circuit is that recurrent excitatory networks are interposed between layers with largely parallel organization. The most extensive recurrent system in the brain is formed by the extensive collaterals of the CA3 neurons. Entorhinal layer 2 and layer 5 axon collaterals are also extensive. The advantage of this alternating pattern type of organization is that in successive layers the neuronal representations can be iteratively segregated (at parallel stages) and integrated (at recursive stages). These operations require time for computation before the results of local processing can be transmitted forward to the next computational stage. The speed of layer-to-layer transfer is controlled by the complex system of inhibitory interneurons. Such control dynamics, often in the form of network oscillations, provide time for local computation and enable the hippocampal system to communicate effectively with various domains of the neocortex in discrete temporal windows. The relatively simple cortical circuitry of the hippocampus has inspired many connectionist theories.

Network patterns of the hippocampus

Figure 4: Two network states of the hippocampus are defined by theta oscillations (~5 sec left) and sharp waves. Recordings from str. radiatum of the left and right hippocampus during exploration (walk, theta) to immobility (still, SPW) transition.

Three major network patterns characterize the temporal dynamics of the hippocampal system: theta oscillations (4–10 Hz), sharp waves and associated ripples (140–200 Hz), and gamma (30–130 Hz) oscillations. These patterns also define states of the hippocampus. The theta state is associated with exploratory (preparatory) movement and REM sleep, while intermittent sharp waves mark immobility, consummatory behaviors, and slow-wave sleep (Fig. 4; MOVIE). These two competing states also largely determine the main direction of information flow, with neocortico-hippocampal transfer taking place mainly during theta oscillations and hippocampo-neocortical transfer during sharp waves. These two states also affect the regularity of gamma oscillations and switching between the states is largely determined by the subcortical neuromodulatory inputs to the hippocampal system.

Gamma frequency oscillations are present in all brain structures where fast inhibition is provided by soma-targeting interneurons (Whittington et al., 1995; Wang and Buzsáki, 1996; Bartos et al.; neural inhibition by Jonas and Buzsáki, 2007). In the simplest case, an interconnected network of basket interneurons can generate sustained gamma oscillations, provided that their depolarization and spiking are secured by some means (such as subcortical neurotransmitters). In the intact brain, gamma oscillations are mainly generated by the interaction between principal cells and interneurons. In both scenarios, the frequency of oscillations is mainly determined by the time course of GABA_A receptor–mediated inhibition. Neurons that discharge within the time period of a gamma cycle (10–30 msec) define a cell assembly (Harris et al., 2003). Because the membrane time constant of pyramidal neurons in vivo is also within this temporal range, recruiting neurons into this assembly time window is the most effective mechanism for discharging the downstream postsynaptic neuron(s) on which the assembly members converge (Buzsáki, 2010). Although gamma oscillations can emerge in each hippocampal region, they can be coordinated across regions by either excitatory connections or by long-range interneurons.

The LFP theta oscillation is the result of coherent membrane potential oscillations across neurons in all hippocampal subregions (Buzsáki, 2002). Theta currents derive from multiple sources, including synaptic currents, intrinsic currents of neurons, dendritic Ca²⁺ spikes, resonance and other voltage-dependent membrane oscillations. The theta rhythm modulation of perisomatic interneurons provides an outward current in somatic layers. The theta rhythm phase therefore biases the power of gamma oscillations, the results of which is a theta-nested gamma burst. Excitatory afferents form active sinks (inward current) at confined dendritic domains of the cytoarchitecturally organized layers of all regions. Since each layer-specific input is complemented by one or more families of interneurons with similar axonal projections, such layer-specific inhibitory dipoles can compete with the excitatory inputs. The resulting rich consortium of theta generators in hippocampal and parahippocampal regions is coordinated by the medial septum and a network of long-range interneurons (Freund and Buzsáki, 1996; Klausberger and Somogyi, 2008). Although theta oscillations are generally coherent throughout the hippocampal system, the momentary power, coherence and phase of theta oscillators can fluctuate significantly in different regions and layers as a function of overt and covert behaviors.

When subcortical modulatory inputs decrease in tone, as it happens in the absence of ambulatory movement and during sleep, theta oscillations are replaced by intermittently occurring, large-amplitude field potentials, or sharp waves (SPWs). SPWs are initiated by the self-organized population bursts of the CA3 pyramidal cells (Buzsáki et al., 1983). The CA3-induced depolarization of CA1 pyramidal cell apical dendrites is reflected by an extracellular negative wave, that is, the SPW, which is most prominent in stratum radiatum. SPWs are associated with fast-field oscillations (140–200 Hz), or ripples, confined to the CA1 pyramidal cell layer (O’Keefe and Nadel, 1978; Buzsáki et al., 1992; fast oscillations by Traub, 2006). In the time window of SPWs, 50,000–100,000 neurons discharge synchronously in the CA3–CA1–subicular complex–entorhinal axis of the rat. The population burst is characterized by a three- to five-fold gain of network excitability in the CA1 region, preparing local circuits for synaptic plasticity and, at the same time, exerting a powerful effect on cortical targets.

Hippocampal place cells and episode cells

Figure 5: Interleaved cell assemblies. A. Three example model neurons (color-coded) with identical oscillation frequency but different phase onset, according to their maximal discharge location. Temporal distance \(T\) is the time needed for the rat to run the distance between the peaks of the two place fields (real time). \(\tau\ ,\) time offset between the two neurons within the theta cycle (theta time). Bottom, the summed activity of the entire population of model neurons (black dashed line) oscillates slower than each transiently active individual neuron (color-coded). B. The phase of the three example neurons with respect to the oscillation of the population is plotted against time. Note that the neuronal spikes phase-precess approximately 360° due to the interference between the oscillatory spiking frequency of the most active neurons and the oscillation frequency of the entire population. Right: spike density for the example neurons. C. Interleaved neuron sequences represent position and distance relationships. The width of the bars indicates firing intensity of the hypothesized assemblies while the theta-time scale temporal (phase) differences between assemblies reflect their respective distance representations. In successive theta cycles, assemblies representing overlapping place fields (P1 to P8) shift together in time and sustain a temporal order relationship with each other so that the assembly that fires on the earliest phase represents a place field whose center the animal traverses first. This "temporal compression" mechanism (Skaggs et al., 1996) allows distances to be translated into time. Approximately, 7±2 assemblies/gamma cycles are present in a given theta period (Bragin et al., 1995; Lisman and Idiart, 1995). A and B, modified after Geisler et al. (2010). C, modified after Dragoi and Buzsáki (2006).

A striking and reliable correlate of firing patterns of hippocampal pyramidal cells is the spatial location of the rat in a given environment, for which reason these cells are known as place cells, discovered by John O'Keefe. (Fig. 6; courtesy of D. Robbe; O’Keefe and Nadel, 1978; Moser et al., 2008). From a physiological perspective, place cells are speed-dependent oscillators, since their oscillation frequency is determined by the animal’s traveling velocity. Every place cell oscillates faster than the ongoing LFP theta, i.e., the extracellular field, which is largely generated by the coherent membrane potential fluctuations of the contributing place cells.

Figure 6: Spiking activity of a CA1 pyramidal neuron (place cell) during maze behavior illustrates combination of rate and temporal coding. A short segment of local field potential and spikes (vertical ticks) are shown for a single trial. The neuron consistently fires at the T junction of the maze. The discharges over multiple trials are converted to a tuning curve or 'place map' (left); intensity of firing is illustrated by colors. Listening to the neuron's firing illustrates its rhythmic nature. The rhythm of the neuron is faster (approximately 9 Hz) than the frequency of the simultaneous local field potential theta oscillation (approximately 8.5 Hz), resulting in a phase interference (or 'phase precession', O'Keefe and Recce, 1993), as illustrated by the dots on the theta cycle. When the rat enters the field the neuron discharges on the peak of the theta cycle. When it reaches the place field center it fires the most spikes at the trough of the theta cycle. When it leaves the center the spike continues to shift to earlier phases. The precise spike timing of individual hippocampal neurons relative to the reference population activity is an illustration of temporal coding. (Movie)

The paradox of how a slower population theta is generated by place cells, each of which oscillates faster than the population mean, can be explained as follows. The activity of place cells is modulated by a Gaussian function of the animal's position and by the theta frequency oscillation (Fig. 5; Burgess et al., 2007; Geisler et al., 2010). The place fields of sequentially active place cells can overlap and their temporal relationships are governed by a compression rule: within the theta cycle, the spike timing sequence of neurons predicts the upcoming sequence of locations in the path of the rat, with larger time lags representing larger distances (O’Keefe and Recce, 1993; Skaggs et al., 1996; Dragoi and Buzsáki, 2006). Because of the time lags between the spikes of the transiently oscillating neurons, the oscillation frequency of their population output, also reflected by the local LFP, is slower than the mean of the oscillating frequencies of the constituent neurons. The tripartite relationship between LFP theta frequency \(f_\theta\ ,\) the oscillation frequency of single neurons \(f_0\) and the distance-related, theta time-scale temporal lags of spikes (time compressed sequences) has important consequences on the assembly organization of hippocampal neurons. First, the difference in oscillation frequency between the population (\(f_\theta\)) and active single neurons generates an interference pattern, known as phase precession of place cells (O’Keefe and Recce, 1993) so that the distance traveled from the beginning of the place field can be instantly inferred from the theta phase of place cell spikes (Fig. 5). Second, the slope of the phase precession defines the size of the place field. Third, the field size (i.e., the lifetime of activity) is inversely related to the oscillation frequency of the neuron. In sum, neurons which oscillate faster have smaller place fields and display steeper phase-precession slopes, as it is the case in the septal portion of the hippocampus, compared to neurons in more caudal (temporal) parts, which oscillate slower, have larger place fields and less steep phase-precession slopes (Maurer et al., 2005). The dynamic local adjustment of these interdependent parameters is responsible for the globally coherent theta oscillation in the hippocampal system (Geisler et al., 2010).

The bidirectional dynamic relationship between single neurons and their population product has important functional consequences. First, despite the variable running speed of the rat, place cells continue to represent the same positions and distances in a given environment. Second, the duration of the theta cycle (120–150 msec in the rat) sets a natural upper limit of distance coding by theta-scale time lags (~50 cm for neurons in the dorsal hippocampus). The behavioral consequence of this constraint is that objects and locations > 50 cm ahead of the rat are initially less distinguishable, but as the animal approaches, they are progressively better resolved by the interleaved cell assemblies. Third, the number of cell assemblies that can nest in a given theta period (5 to 9, as reflected by the number of gamma cycles/theta; Bragin et al., 1995), determines the spatial resolution of distance representation. A consequence of the limited number of theta-nested assemblies is that distance resolution scales with the size of the environment; temporal lags that represent fine spatial resolution in small enclosures correspond to coarser distance representations in larger environments.

Although our current understanding of the dynamics of hippocampal networks derives largely from experiments carried out in spatial tasks, it is important to recognize that the above physiological mechanisms do not necessarily reflect the imposition of environmental stimuli on hippocampal neurons (Eichenbaum and Cohen, 1993; McNaughton et al., 1996). In fact, all of the above-described patterns can emerge by internal mechanisms, without a reliance on external cues or reafferent body signals (Pastalkova et al., 2008; Gelbard-Sagiv et al., 2008). Conceptualizing locations as discrete items, the temporal compression mechanism can limit the attention span and the register capacity of the memory buffer of the gamma-nested theta-cycle to 5 to 9 items (Lisman and Idiart, 1995). Due to the compression mechanism, the spatiotemporal resolution of an episodic recall is high for the conditions/context that surround a recalled event, whereas the relationships among items representing the far past or far future, relative to the recalled event, are progressively less resolved. However, as the content of the recall moves forward in perceived time, subsequent events gain high contextual resolution. The compression dynamic can also allow that not only adjacent but more distant assemblies are linked, as long as they consistently co-occur in the same theta cycles. These higher order connections, in turn, can provide a substrate for alternative combinations of different assembly sequences, mechanisms necessary e.g., for solving detour and transitive inference problems (Muller et al., 1996) and for higher-order associations of items in episodic memory (Polyn and Kahana, 2008).

Consolidation and transfer of hippocampal memories

Figure 7: Time-compressed off-line replay of learned neural patterns. Replay of waking assembly sequences during sleep. Smoothed place fields (colored lines) of 8 place cells during runs from left to right on a track (average of 30 trials). Vertical bars mark the positions of the normalized peaks of the smoothed fields. Non-uniform time axis below shows time within average lap when above positions were passed. Bottom panels, three SPW-R-related sequences from slow wave sleep after the waking session. Note similar sequences during SPW-Rs and run. Note also difference in timescale. Bar = 50 ms. Modified after Lee and Wilson (2002).

Learning an episode typically requires only a single exposure. After an experience, the memory trace either disappears or consolidates into a long-term form. It has been hypothesized that the theta-SPW switch supports a two-stage memory mechanism with a rapid acquisition stage during theta oscillations, followed by repeated reactivation of acquired information during post-experience SPWs (Buzsáki, 1989).

SPWs represent the most synchronous assembly pattern in the mammalian brain, characterized by a three- to five-fold gain of network excitability, creating short time windows for efficient transfer of hippocampal information to the neocortex. Both place cell sequences and the distances between the place fields experienced during exploration are reflected in the temporal structure of neuronal sequences during SPW (Fig. 7; Wilson and McNaughton, 1994; Lee and Wilson, 2002; Dupret et al., 2010), and their selective elimination after learning interferes with memory consolidation (Girardeau et al., 2009). In the waking animal, SPW-related sequences can be replayed in either a forward manner, typically prior to initiating a journey, or in a reverse order after reaching the goal (Foster et al., 2006; Diba and Buzsáki, 2007). This bidirectional re-enactment of temporal sequences may also contribute to the establishment of higher-order associations in episodic memory. Generating windows of high excitability states for teaching the neocortex comes with a price. A slight perturbation of the balance between inhibition and excitation can lead to interictal spikes and seizures. The highly excitable SPWs explain why the hippocampus is the most seizure-prone structure in the brain. Impairment of any of the multiple temporal dynamics can underlie diseases such as schizophrenia and Alzheimer's disease.

Other functions and implications

This overview is meant to be a progress report of our current knowledge of hippocampal dynamics, embedded in a hypothetical framework. I recognize that not all observations fit this framework and acknowledge the many other or related viewpoints, advanced by outstanding colleagues. Hippocampus-like structures are also present in reptiles and birds, and potentially serve navigation functions (http://en.wikipedia.org/wiki/Evolution_of_the_Hippocampus). Granule cells are the specialty neuron of the hippocampus, not present in the neocortex. Granule cells are generated postnatally (Bayer and Altman, 1974; Gage, 2000) and involved in a multitude of functions (Scharfmann, 2007). Revealing the physiological roles of granule cells is, therefore, a key step for understanding hippocampal computation. Furthermore, while this entry focuses on hippocampal-neocortical interactions, the hippocampus also exerts a critical effect on its downstream targets, such as the hypothalamus and basal ganglia, mostly by way of the subiculum.

References

• Amaral, D; Lavenex P (2006). Hippocampal Neuroanatomy, in Andersen P, Morris R, Amaral D, Bliss T, O’Keefe J: The Hippocampus Book. Oxford University Press.
• Bayer, S.; Altman, J. (1974). "Hippocampal development in the rat: cytogenesis and morphogenesis examined with autoradiography and low-level X-irradiation". The Journal of comparative neurology 158 (1): 55–79.
• Bragin A, Jando G, Nadasdy Z, Hetke J, Wise K and Buzsáki G (1995) Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat. J Neurosci 15: 47-60.
• Burgess N, Barry C, O'Keefe J. (2007) An oscillatory interference model of grid cell firing.Hippocampus. 17:801-12
• Buzsáki, G (2002). Theta oscillations in the hippocampus. Neuron 33: 325–340.
• Buzsáki, G (2006). Rhythms of the Brain, Oxford University Press.
• Buzsaki G (2010). Neural syntax: assembly sequences, synapsembles and readers. Neuron
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• Diba K, Buzsáki G (2007) Forward and reverse hippocampal place-cell sequences during ripples. Nat Neurosci 10:1241-1242.
• Dragoi G, Buzsáki G (2006) Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50:145-157.
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• Foster DJ, Wilson MA (2006) Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440:680-683.
• Gage, F. H. Mammalian neural stem cells (2000) Science 287:1433-1438.
• Geisler C, Diba K, Pastalkova E, Mizuseki K, Royer S, Buzsáki G (2010) Temporal delays among place cells determine the frequency of population theta oscillations in the hippocampus. Proc Natl Acad Sci U S A. 107:7957-7962.
• Gelbard-Sagiv H, Mukamel R, Harel M, Malach R, Fried I (2008) Internally generated reactivation of single neurons in human hippocampus during free recall. Science 322:96-101.
• Girardeau G, Benchenane K, Wiener SI, Buzsáki G, Zugaro MB (2009) Selective suppression of hippocampal ripples impairs spatial memory. Nat Neurosci. 12:1222-1223.
• Harris,K.D., Csicsvari,J., Hirase,H., Dragoi,G., and Buzsáki,G. (2003). Organization of cell assemblies in the hippocampus. Nature 424, 552-556.
• Lee, A.K., Wilson, M.A. (2002) Memory of sequential experience in the hippocampus during slow wave sleep. Neuron, 36:1183-1194.
• Lisman JE, Idiart MA (1995) Storage of 7 +/- 2 short-term memories in oscillatory subcycles. Science 267:1512-1515.
• Maurer AP, Vanrhoads SR, Sutherland GR, Lipa P, McNaughton BL (2005) Self-motion and the origin of differential spatial scaling along the septo-temporal axis of the hippocampus. Hippocampus 15:841-852.
• McNaughton,B.L., Barnes,C.A., Gerrard,J.L., Gothard,K., Jung,M.W., Knierim,J.J., Kudrimoti,H., Qin,Y., Skaggs,W.E., Suster,M., and Weaver,K.L. (1996). Deciphering the hippocampal polyglot: the hippocampus as a path integration system. J Exp Biol 199, 173-185.
• Moser EI, Kropff E, Moser MB (2008) Place cells, grid cells, and the brain's spatial representation system. Annu Rev Neurosci. 31:69-89.
• Muller, R. U., Stead, M., & Pach, J. (1996). The hippocampus as a cognitive graph. J Gen Physiol, 107, 663-694.
• O’Keefe, J; Nadel L (1978). The Hippocampus as a Cognitive Map. Oxford University Press.
• O'Keefe J, Recce ML.(1993) Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus. 3:317-30.
• O'Neill J, Pleydell-Bouverie B, Dupret D, Csicsvari J (2010) Play it again: reactivation of waking experience and memory. Trends Neurosci. 33:220-229.
• Pastalkova E, Itskov V, Amarasingham A, Buzsáki G (2008) Internally generated cell assembly sequences in the rat hippocampus. Science. 321:1322-1327.
• Polyn SM, Kahana MJ. (2008) Memory search and the neural representation of context. Trends Cogn Sci. 12:24-30.
• Scharfman H, ed (2007). The Dentate Gyrus: A comprehensive guide to structure, function, and clinical imiplications. 163. 1–840.
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• Skaggs, WE; McNaughton BL, Wilson MA, Barnes CA (1996) Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6: 149–176.
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Further reading

• Andersen P, Morris R, Amaral D, Bliss TVP, O'Keefe J (2007) The Hippocampus Book. Oxford University Press.
• Derdikman D, Knierim JJ (ed.) (2014) Space, Time and Memory in the Hippocampal Formation. Springer, Wien.
• Duvernoy HM (2005) The Human Hippocampus. Springer, Berlin.
• Squire LR and Schacter DL (2003) Neuropsychology of Memory, Third Edition. Guilford Publications, NY.

External links

• Buzsáki lab's webpage

See also

Brain, Neuron inhibition, oscillations, memory, planning, brain networks

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Reviewed by: Anonymous

Accepted on: 2010-12-10 19:02:43 GMT