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Resonance, Oscillation and the Intrinsic Frequency Preferences of Neurons

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Resonance, Oscillation and the Intrinsic Frequency Preferences of Neurons R EVIEW M.E. Rice – Brain ascorbate 45 Siesjö, B.K. (1980) Regional metabolic rates in the brain. In Brain 59 Lafon-Cazal, M. et al. (1993) NMDA-dependent superoxide Energy Metabolism, pp. 131–150, Wiley production and neurotoxicity. Nature 354, 535–537 46 Lutz, P.L. (1992) Mechanisms for anoxic survival in the vertebrate 60 Prehn, J.H. (1998) Mitochondrial transmembrane potential and brain. Annu. Rev. Physiol. 54, 601–618 free radical production in excitotoxic neurodegeneration. Naunyn- 47 Sick, T.J. et al. (1992) Maintaining coupled metabolism and Schmiedebergs’s Arch. Pharmacol. 357, 316–322 membrane function in anoxic brain: A comparison between turtle 61 Ciani, E. et al. (1996) Inhibition of free radical production or free and rat. In Surviving Hypoxia (Hochachka, P. et al., eds), pp. 351–363, radical scavenging protects from the excitotoxic cell death mediated CRC by glutamate in cultures of cerebellar granule cells. Brain Res. 48 Rice, M.E. and Cammack, J. (1991) Anoxia-resistant turtle brain 728, 1–6 maintains ascorbic acid content in vitro. Neurosci. Lett. 132, 62 Atlante, A. et al. (1997) Glutamate neurotoxicity in rat cerebellar 141–145 granule cells: a major role for xanthine oxidase in oxygen radical 49 Halliwell, B. (1996) Vitamin C: antioxidant or pro-oxidant in vivo. formation. J. Neurochem. 68, 2038–2045 Free Radic. Res. 25, 439–454 63 Brahma, B. et al. (2000) Ascorbate inhibits edema in brain slices. J. 50 Hillered, L. et al. (1988) Increased extracellular levels of ascorbate Neurochem. 74, 1263–1270 in striatum after middle cerebral artery occlusion in the rat 64 Sato, K. et al. (1993) Synergism of tocopherol and ascorbate on the monitored by intracerebral microdialysis. Neurosci. Lett. 95, survival of cultured neurones. NeuroReport 4, 1179–1182 286–290 65 Sharma, P. (1997) Consequences of hypoxia on the cell size of 51 Lyrer, P. et al. (1991) Levels of low molecular weight scavengers neuropeptide-Y neurons and the role of ascorbate in cultured in the rat brain during focal ischemia. Brain Res. 567, 317–320 neurons from chick embryo. Neurochem. Int. 30, 337–344 52 Cao, W. et al. (1988) Oxygen free radical involvement in ischemia 66 Majewska, M.D. et al. (1990) Regulation of the NMDA receptor by and reperfusion injury to brain. Neurosci. Lett. 88, 233–238 redox phenomena: inhibitory role of ascorbate. Brain Res. 537, 53 Sciamanna, M.A. and Lee, C.P. (1993) Ischemia/reperfusion- 328–332 induced injury of forebrain mitochondria and protection by 67 Buettner, G.R. (1993) The pecking order of free radicals and ascorbate. Arch. Biochem. Biophys. 305, 215–224 antioxidants: lipid peroxidation, a-tocopherol, and ascorbate. 54 Ranjan, A. et al. (1993) Ascorbic acid and focal ischaemia in a Arch. Biochem. Biophys. 300, 535–543 primate model. Acta Neurochir. 123, 87–91 68 Kuo, C-H. et al. (1978) Subcellular distribution of ascorbic acid in 55 Henry, P.T. and Chandry, M.J. (1998) Effect of ascorbic acid on rat brain. Jpn. J. Pharmacol. 28, 789–791 infarct size in experimental focal cerebral ischemia and reperfusion 69 Glembotski, C.C. (1987) The role of ascorbic acid in the biosynthesis in a primate model. Acta Neurochir. 140, 977–980 of the neuroendocrine peptides α-MSH and TRH. Ann. New York 56 Pérez-Pinzón, M.A. et al. (1997) Brain Res. 754, 163–170 Acad. Sci. 498, 54–62 57 Dykens, J.A. et al. (1987) Mechanism of kainate toxicity to cerebellar 70 Miller, B.T. and Cicero, T.J. (1987) Ascorbate-induced release of LHRH: neurons in vitro is analogous to reperfusion tissue injury. noradrenergic and opioid modulation. Brain Res. Bull. 19, 95–99 J. Neurochem. 49, 1222–1228 71 Eldridge, C.F. et al. (1987) Differentiation of axon-related Schwann 58 Coyle, J.T. and Puttfarcken, P. (1993) Oxidative stress, glutamate, cells in vitro. I. Ascorbic acid regulates basal lamina assembly and and neurodegenerative disorders. Science 262, 689–695 myelin formation. J. Cell Biol. 105, 1023–1034 Resonance, oscillation and the intrinsic frequency preferences of neurons Bruce Hutcheon and Yosef Yarom The realization that different behavioural and perceptual states of the brain are associated with different brain rhythms has sparked growing interest in the oscillatory behaviours of neurons. Recent research has uncovered a close association between electrical oscillations and resonance in neurons. Resonance is an easily measurable property that describes the ability of neurons to respond selectively to inputs at preferred frequencies. A variety of ionic mechanisms support resonance and oscillation in neurons. Understanding the basic principles involved in the production of resonance allows for a simplified classification of these mechanisms. The characterization of resonance and frequency preference captures those essential properties of neurons that can serve as a substrate for coordinating network activity around a particular Bruce Hutcheon is at the Institute for frequency in the brain. Biological Science, Trends Neurosci. (2000) 23, 216–222 National Research Council of Canada, HE WORKING BRAIN is characterized by the rhyth- types of explanation. One invokes patterns of connec- Ottawa, Canada Tmic activation of large numbers of its neurons on tivity between neurons and the dynamic properties of K1A 0R6, and characteristic temporal and spatial scales. These modes the intervening synapses. For example, reverberating Yosef Yarom is at of coherent activity appear as the various brain rhythms. activity within re-entrant neural circuits could result the Dept of A series of firmly established empirical associations with in the rhythmic activation of fundamentally non- Neurobiology, and the behavioural states of organisms provides compelling oscillatory neurons within well-defined frequency the Center for evidence that brain rhythms reflect basic modes of dy- bands2. A different explanation states that network Neuronal namical organization in the brain1. However, the mecha- rhythmicity arises via the coupling of oscillatory sub- Computation, nisms that bind neurons into these rhythmical coherent units, each of which possesses an intrinsically deter- Hebrew University, ensembles are not well understood. mined frequency preference3. These two explanations Jerusalem, Israel What determines the characteristic frequency range are not mutually exclusive (network connectivity could 91904. of each brain rhythm? Broadly speaking, there are two reinforce the patterns of excitation produced by coupled 216 TINS Vol. 23, No. 5, 2000 0166-2236/00/$ – see front matter © 2000 Elsevier Science Ltd. All rights reserved. PII: S0166-2236(00)01547-2 B. Hutcheon and Y. Yarom – Frequency preferences of neurons R EVIEW Box 1. Hunting resonance in circuits and cells Physicists and engineers have long used frequency-domain techniques of oscillatory signals. The impedance of a simple circuit can be deter- to describe the wave-like existence of particles, the motions of elec- mined by probing it with an input current and observing the voltage trons in atoms, or the movement of a pendulum. These methods response at each frequency. Although any input that has a known regard oscillation as a fundamental mode of behaviour and frequency frequency composition can be used, the process is illustrated in this as its natural unit of measure. For neurons, frequency-domain tech- case using a signal that sweeps through many frequencies over time niques provide an alternative to time-based descriptions of activity: (the so called ‘ZAP’ inputa). This input is useful because it is poised a simpler and more-natural one for neurons that spend much of their almost equally between the time and frequency domains. Each fre- existence immersed in sea of rhythmic inputs. Measurement of the quency in the input is isolated briefly in time so that the frequency electrical impedance characterizes the input–output relationship of response can be judged by eye as well as by later analysis. For the ex- neurons in a frequency-dependent way. Resonance is a property of perimentalist who is probing neuronal circuits this real-time feedback the impedance. To explain further, it helps to explore first the prin- is valuable. ciples of impedance and resonance in electrical circuit caricatures of The simplest of impedance relationships occurs for the simplest of neuronal behaviour. all circuits, a resistor connected to ground (see Fig. Ia). In this case, as Impedance is the frequency-domain extension of the concept of in all others, the impedance is found by dividing the Fourier spectrum resistance for electrical circuits. Like resistance, it is a relationship (calculated using the Fast Fourier Transform, or FFT) of the output by between voltage and current. Unlike resistance, impedance is a fre- that of the input. In this example, the impedance is simply a constant quency-dependent relationship between the amplitudes (and phases) with a value equal the resistance. A slightly more-sophisticated circuit comprises a resistor and capacitor connected in parallel (Fig. Ib). This is a com- Input currentCircuit Output voltage Impedance mon model for the passive electrical properties of an iso- potential neuron. In this case, the impedance is a more-com- (a) plicated function, the decline in impedance with increasing frequency indicates that an oscillatory input current of unit = FFT output FFT input amplitude produces a smaller and smaller voltage response as the frequency rises. This circuit, therefore, acts in a way that is similar to a low-pass filter, that is, current inputs arriv- ing at low frequencies yield relatively large voltage responses (b) but higher frequency inputs are attenuated or blocked. All neurons have some contribution from a low-pass mecha- nism such as this in their frequency response. Finally, adding an inductive element to the circuit results in a qualitatively different impedance relation (Fig. Ic). A resonant peak appears so that instead of acting in the same (c) way as a low-pass filter, the system responds like a bandpass. The meaning of this is seen in the time-domain response to the ZAP input.
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