Dynamic characteristics of visual evoked potentials in the dog. II. Beta frequency selectivity in evoked potentials and background activity

Silva, F.H Lopes da; Rotterdam, A. van; Leeuwen, W. Storm van; Tielen, A.M.

doi:10.1016/0013-4694(70)90138-0

Cited by 86 publications

(34 citation statements)

References 10 publications

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“…Attentive sniffing in a motivated rabbit elicits 55-to 75-Hz olfactory lobe oscillations (30), and a monkey's manual exploration for a raisin elicits large 25-to 35-Hz oscillations in the sensorimotor cortex (7). Similarly, attentive visual behaviors cause large increases in 15-to 25-Hz activity in the visual cortex of cat (31) and dog (32), as well as increases in the 20-to 40-Hz range in the monkey (33). The oscillations observed in the awake turtle may also represent attention-sensitive responses because they occur with spontaneous shifts of gaze (13) and they are largest in response to salient, or attention-capturing, stimuli, such as the investigator's hand movements.…”

Section: Discussionmentioning

confidence: 99%

Visual motion induces synchronous oscillations in turtle visual cortex.

Prechtl

1994

Proc. Natl. Acad. Sci. U.S.A.

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In mammalian brains, multielectrode recordings during sensory stimulation have revealed oscillations in different cortical areas that are transiently synchronous. These synchronizations have been hypothesized to support integration of sensory information or represent the operation of attentional mechanisms, but their stimulus requirements and prevalence are still unclear. Here I report an analogous synchronization in a reptilian cortex induced by moving visual stimuli. The synchronization, as measured by the coherence function, applies to spindle-like 20-Hz oscillations recorded with multiple electrodes implanted in the dorsal cortex and the dorsal ventricular ridge of the pond turtle. Additionally, widespread increases in coherence are observed in the 1-to 2-Hz band, and widespread decreases in coherence are seen in the 10-and 30-to 45-Hz bands. The 20-Hz oscillations induced by the moving bar or more natural stimuli are nonstationary and can be sustained for seconds. Early reptile studies may have interpreted similar spindles as electroencephalogram correlates of arousal; however, the absence of these spindles during arousing stimuli in the dark suggests a more specific role in visual processing. Thus, visually induced synchronous oscillations are not unique to the mammalian cortex but also occur in the visual area of the primitive three-layered cortex of reptiles.

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Section: Discussionmentioning

confidence: 99%

Visual motion induces synchronous oscillations in turtle visual cortex.

Prechtl

1994

Proc. Natl. Acad. Sci. U.S.A.

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“…These small-amplitude background excitatory events triggered action potentials when concomitant with depolarizing envelopes, sculpted by the temporal summation of synaptic potentials resulting from sporadic epochs of relatively coherent cortical activity (Lopes da Silva et al, 1970;Murthy and Fetz, 1992). Because thalamic projection neurons fire tonically during alertness (Steriade, 2000), we cannot exclude that sustained discharges of glutamatergic thalamostriatal neurons (Wilson et al, 1983) contribute to the excitatory synaptic bombardment of striatal cells during wakefulness.…”

Section: Membrane Properties Of Msns In the Alert Ratmentioning

confidence: 99%

Distinct Patterns of Striatal Medium Spiny Neuron Activity during the Natural Sleep–Wake Cycle

Mahon

Vautrelle²,

Pézard³

et al. 2006

J. Neurosci.

172

137

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Striatal medium-sized spiny neurons (MSNs) integrate and convey information from the cerebral cortex to the output nuclei of the basal ganglia. Intracellular recordings from anesthetized animals show that MSNs undergo spontaneous transitions between hyperpolarized and depolarized states. State transitions, regarded as necessary for eliciting action potential firing in MSNs, are thought to control basal ganglia function by shaping striatal output. Here, we use an anesthetic-free rat preparation to show that the intracellular activity of MSNs is not stereotyped and depends critically on vigilance state. During slow-wave sleep, much as during anesthesia, MSNs displayed rhythmic step-like membrane potential shifts, correlated with cortical field potentials. However, wakefulness was associated with a completely different pattern of temporally disorganized depolarizing synaptic events of variable amplitude. Transitions from slow-wave sleep to wakefulness converted striatal discharge from a cyclic brisk firing to an irregular pattern of action potentials. These findings illuminate different capabilities of information processing in basal ganglia networks, suggesting in particular that a novel style of striatal computation is associated with the waking state.

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“…There is ample experimental evidence of the existence of linear EEG phenomena in lower mammals [54,55] and humans [46,56] which occurs over limited ranges of experimental conditions (modulation depth of sinusoidal driving of the brain, for example). However, the use of linear and quasi-linear theories and the neglect of interactions across spatial scales are evidently crude approximations.…”

Section: A Backgroundmentioning

confidence: 99%

Statistical mechanics of neocortical interactions: A scaling paradigm applied to electroencephalography

Ingber

1991

Phys. Rev. A

124

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A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. In this paper, a more stringent test is placed on SMNI: The algebraic and numerical algorithms previously developed in this and similar systems are brought to bear to fit large sets of EEG and evoked potential data being collected to investigate genetic predispositions to alcoholism and to extract brain "signatures" of short-term memory. Using the numerical algorithm of Very Fast Simulated Re-Annealing, it is demonstrated that SMNI can indeed fit this data within experimentally observed ranges of its underlying neuronal-synaptic parameters, and use the quantitative modeling results to examine physical neocortical mechanisms to discriminate between high-risk and low-risk populations genetically predisposed to alcoholism. Since this first study is a control to span relatively long time epochs, similar to earlier attempts to establish such correlations, this discrimination is inconclusive because of other neuronal activity which can mask such effects. However, the SMNI model is shown to be consistent with EEG data during selective attention tasks and with neocortical mechanisms describing short-term memory previously published using this approach. This paper explicitly identifies similar nonlinear stochastic mechanisms of interaction at the microscopic-neuronal, mesoscopic-columnar and macroscopic-regional scales of neocortical interactions. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons.PA

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Dynamic characteristics of visual evoked potentials in the dog. II. Beta frequency selectivity in evoked potentials and background activity

Cited by 86 publications

References 10 publications

Visual motion induces synchronous oscillations in turtle visual cortex.

Visual motion induces synchronous oscillations in turtle visual cortex.

Distinct Patterns of Striatal Medium Spiny Neuron Activity during the Natural Sleep–Wake Cycle

Statistical mechanics of neocortical interactions: A scaling paradigm applied to electroencephalography

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