The Theta-Gamma Neural Code Lisman, John E.; Jensen, Ole
Neuron (Cambridge, Mass.),
03/2013, Letnik:
77, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: ...that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes.
Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, Lisman and Jensen review evidence in support of the hypothesis that the dual oscillations form a code for representing multiple items in an ordered way.
Many risk genes interact synergistically to produce schizophrenia and many neurotransmitter interactions have been implicated. We have developed a circuit-based framework for understanding gene and ...neurotransmitter interactions. NMDAR hypofunction has been implicated in schizophrenia because NMDAR antagonists reproduce symptoms of the disease. One action of antagonists is to reduce the excitation of fast-spiking interneurons, resulting in disinhibition of pyramidal cells. Overactive pyramidal cells, notably those in the hippocampus, can drive a hyperdopaminergic state that produces psychosis. Additional aspects of interneuron function can be understood in this framework, as follows. (i) In animal models, NMDAR antagonists reduce parvalbumin and GAD67, as found in schizophrenia. These changes produce further disinhibition and can be viewed as the aberrant response of a homeostatic system having a faulty activity sensor (the NMDAR). (ii) Disinhibition decreases the power of gamma oscillation and might thereby produce negative and cognitive symptoms. (iii) Nicotine enhances the output of interneurons, and might thereby contribute to its therapeutic effect in schizophrenia.
In this article we develop the concept that the hippocampus and the midbrain dopaminergic neurons of the ventral tegmental area (VTA) form a functional loop. Activation of the loop begins when the ...hippocampus detects newly arrived information that is not already stored in its long-term memory. The resulting novelty signal is conveyed through the subiculum, accumbens, and ventral pallidum to the VTA where it contributes (along with salience and goal information) to the novelty-dependent firing of these cells. In the upward arm of the loop, dopamine (DA) is released within the hippocampus; this produces an enhancement of LTP and learning. These findings support a model whereby the hippocampal-VTA loop regulates the entry of information into long-term memory.
Several lines of evidence indicate that brief (
<
25 ms) bursts of high-frequency firing have special importance in brain function. Recent work shows that many central synapses are surprisingly ...unreliable at signaling the arrival of single presynaptic action potentials to the postsynaptic neuron. However, bursts are reliably signaled because transmitter release is facilitated. Thus, these synapses can be viewed as filters that transmit bursts, but filter out single spikes. Bursts appear to have a special role in synaptic plasticity and information processing. In the hippocampus, a single burst can produce long-term synaptic modifications. In brain structures whose computational role is known, action potentials that arrive in bursts provide more-precise information than action potentials that arrive singly. These results, and the requirement for multiple inputs to fire a cell suggest that the best stimulus for exciting a cell (that is, a neural code) is coincident bursts. Trends Neurosci. (1997) 20, 38–43
Complex spatial working memory tasks have been shown to require both hippocampal sharp-wave ripple (SWR) activity and dentate gyrus (DG) neuronal activity. We therefore asked whether DG inputs to CA3 ...contribute to spatial working memory by promoting SWR generation. Recordings from DG and CA3 while rats performed a dentate-dependent working memory task on an eight-arm radial maze revealed that the activity of dentate neurons and the incidence rate of SWRs both increased during reward consumption. We then found reduced reward-related CA3 SWR generation without direct input from dentate granule neurons. Furthermore, CA3 cells with place fields in not-yet-visited arms preferentially fired during SWRs at reward locations, and these prospective CA3 firing patterns were more pronounced for correct trials and were dentate-dependent. These results indicate that coordination of CA3 neuronal activity patterns by DG is necessary for the generation of neuronal firing patterns that support goal-directed behavior and memory.
Encoding and recall of memory sequences is an important process. Memory encoding is thought to occur by long-term potentiation (LTP) in the hippocampus; however, it remains unclear how LTP, which has ...a time window for induction of ∼100
ms, could encode the linkage between sequential items that arrive with a temporal separation >100
ms. Here, we argue that LTP can underlie the learning of such memory sequences, provided the input to the hippocampus is from a cortical multi-item working memory buffer in which theta and gamma oscillations have an important role. In such a buffer, memory items that occurred seconds apart are represented with a temporal separation of 20–30
ms, thereby bringing them within the LTP window. The physiological and behavioral evidence for such a buffer will be reviewed.
We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major ...themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the meeting and finally suggest potential directions of research that we believe are most promising to develop an understanding of how these two forms of plasticity interact to facilitate functional changes in the brain.
This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
Grid cells in the rat medial entorhinal cortex fire (periodically) over the entire environment. These cells provide input to hippocampal granule cells whose output is characterized by one or more ...small place fields. We sought to understand how this input-output transformation occurs. Available information allows simulation of this process with no freely adjustable parameters. We first examined the spatial distribution of excitation in granule cells produced by the convergence of excitatory inputs from randomly chosen grid cells. Because the resulting summation depends on the number of inputs, it is necessary to use a realistic number (approximately 1200) and to take into consideration their 20-fold variation in strength. The resulting excitation maps have only modest peaks and valleys. To analyze how this excitation interacts with inhibition, we used an E%-max (percentage of maximal suprathreshold excitation) winner-take-all rule that describes how gamma-frequency inhibition affects firing. We found that simulated granule cells have firing maps that have one or more place fields whose size and number approximates those observed experimentally. A substantial fraction of granule cells have no place fields, as observed experimentally. Because the input firing rates and synaptic properties are known, the excitatory charge into granule cells could be calculated (2-3 pC) and was found to be only somewhat larger than required to fire granule cells (1 pC). We conclude that the input-output transformation of dentate granule does not depend strongly on synaptic modification; place field formation can be understood in terms of simple summation of randomly chosen excitatory inputs, in conjunction with a winner-take-all network mechanism.
The role of gamma oscillations in producing synchronized firing of groups of principal cells is well known. Here, we argue that gamma oscillations have a second function: they select which principal ...cells fire. This selection process occurs through the interaction of excitation with gamma frequency feedback inhibition. We sought to understand the rules that govern this process. One possibility is that a constant fraction of cells fire. Our analysis shows, however, that the fraction is not robust because it depends on the distribution of excitation to different cells. A robust description is termed E%-max: cells fire if they have suprathreshold excitation (E) within E% of the cell that has maximum excitation. The value of E%-max is approximated by the ratio of the delay of feedback inhibition to the membrane time constant. From measured values, we estimate that E%-max is 5-15%. Thus, an E%-max winner-take-all process can discriminate between groups of cells that have only small differences in excitation. To test the utility of this framework, we analyzed the role of oscillations in V1, one of the few systems in which both spiking and intracellular excitation have been directly measured. We show that an E%-max winner-take-all process provides a simple explanation for why the orientation tuning of firing is narrower than that of the excitatory input and why this difference is not affected by increasing excitation. Because gamma oscillations occur in many brain regions, the framework we have developed for understanding the second function of gamma is likely to have wide applicability.
Because CaMKII is the critical Ca(2+) sensor that triggers long-term potentiation (LTP), understanding its activation and deactivation is important. A major advance has been the development of a FRET ...indicator of the conformational state of CaMKII called Camui. Experiments using Camui have demonstrated that the open (active) conformation increases during LTP induction and then decays in tens of seconds, with the major fast component decaying with a time-constant of ~ 6 sec (tau1). Because this decay is faster if autophosphorylation of T286 is prevented (the autophosphorylation prolongs activity by making the enzyme active even after Ca(2+) falls), it seemed likely that the fast decay is due to the T286 dephosphorylation. To test this interpretation, we studied the effect of phosphatase inhibitors on the single-spine Camui signal evoked by two-photon glutamate uncaging. We applied inhibitors of PP1 and PP2A, two phosphatases that are present at synapses and that have been shown to dephosphorylate CaMKII in vitro. The inhibitors increased the basal Camui activation state, indicating their effectiveness in cells. However, in no case did we find that tau1 was prolonged, contrary to what would be expected if the decay was phosphatase-dependent. This could either mean that decay was due to some unknown phosphatase or that the decay was not due to dephosphorylation. To distinguish between these possibilities, we expressed pseudo-phosphorylated Camui (T286D) (plus additional mutations T/A that prevented inhibitory 305/306 phosphorylation). This form had an elevated basal activation state, but was further activated during glutamate uncaging; importantly the activation state decayed with tau1 nearly the same as that of WT Camui. Therefore, the data strongly indicate that tau1 is not due to T286 dephosphorylation. We conclude that, although Camui is an excellent tool for observing CaMKII signaling, further experimentation is needed to determine how CaMKII is turned off by its dephosphorylation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
