3匹の異なる大きさのSouth American cichlid, the blue arcara Aequidens pulcherを用いて、displaced AC(DAC)は、calcium-binding protein parvalbuminの抗体で染め、また、弱いがcholine acetyl transferase (ChaT)でも染め、rhodamine dextranで逆行性に染めた網膜神経節細胞とともに分布を観察すると、大きい魚ではどちらの細胞腫の密度は低かったが、魚の大きさに関わらず両者の存在比は、周辺では一定(ratio of DACs to GCs, 0.62)であったのに、中心ではGCが多く(DAC-GC ratio as low as 0.25)、眼球の成長とともに中心でのdACが減少することを示唆した。

【文献】

　◆ヒトEEG
　　　・8 to 12 Hz rhythm
　　　　　・the alpha waves of Berger(1)
　　　　　・a barrage of intensive clinical and basic research
　　　　　・覚醒時にリズム消失(2)
　　　　　　　　anesthesia and epilepsyでオシレーション増大
　　　　　・平均周波数は線形回帰可能(23)
　　　　　　　　周波数帯域がはっきり分離
　　　・周期的発火は哺乳類ではよく保存(7, 13, 14, 17, 20, 34)
　◆機能
　　　・入力選択
　　　　　・感覚刺激への周期的注意(cell assemblies: 15–16)
　　　　　・紡錘波は外部からの入力の影響を減衰させる(46)
　　　　　・海馬歯状回の入力とγ振動の関係(25)
　　　・錐体細胞の受容野内高頻度発火
　　　　　特定の籠状細胞をenslavesして周辺錐体細胞の発火抑制(42)
　　　・学習との関連
　　　　　・STDPとの関連(47, 48)
　　　　　・θ振動と同期した入力でLTP、非同期でLTD(49)
　　　・睡眠時リズムは覚醒時の経験と関係(11, 12)
　　　　　synchronized networksの生理的重要性に注目(13–17)
　　　　　単一細胞活動と行動の"middle ground"(2–6, 15)
　　　　　様々な分野と関係(2–11, 13–22)
　　　　　同じ回路が異なる活動状態で異なる発火パターン
　　　　　異なる周波数のリズムの混在(2, 25)
　　　　　This 1/f power relationship
　　　　　　　電位強さは周波数と負の相関(27)
　　　　　　　低周波数成分が変わると高周波数のエネルギーが大きく影響(28)
　　　　　　　　　低周波数成分が高周波数成分を調節(2, 25, 29)
　　　　　　　　　この特徴は軸索伝導速度やシナプス遅延による(30)
　　　　　　　低周波数成分は広い範囲で生じうるが高周波数成分は局所的
　　　　　　　　　(2, 25)
　　　　　　　　　したがって異なる周波数成分共在可能(32)
　◆リズム作る研究(3–8)
　　　・細胞内振動機構(9, 10)
　◆リズム生成機構
　　　・リズムは0.05 Hz to 500 Hz
　　　・内的機構と回路機構の相互作用(2–5, 7–10)
　　　　　チャネルは高周波数透過で膜受動的特性は低周波数
　　　　　　　膜電位や膜コンダクタンス変化で調節も可能
　　　　　両者の特性で以下の機能達成(40–43)
　　　　　　・resonators(band-pass filters)
　　　　　　　　　弱信号の削除や増強(2, 7, 44, 45)
　　　　　　・notch or band-stop filters
　　　　　　・subthreshold oscillators
　　　・同周波数特性の神経群で生じた周期的発火は弱い入力でも長時間持続(21, 32)
　　　・単一皮質主要細胞の発火はポワソン分布(35)
　　　・神経群では振動しがち(7, 8, 13, 54)
　　　　　抑制性介在神経が時を刻む回路(19, 32)
　　　　　　　介在神経にも様々な周波数特性(19, 40, 43)
　　　・電気シナプス(3, 36–38)
　　　・長距離の軸索がない「小さい世界」の回路(21, 31, 32, 39)
　　　・構成部分の時定数で周波数決定(8, 53)
　　　・初期状態も重要(22, 33, 53)
　◆周期的発火の意義
　　　・共通入力に比べ同期を取るのにコストがかからない(21, 22)
　　　・harmonic / relaxation oscillators (22, 33)
　　　　　・harmonic oscillatorsの意義
　　　　　　　　短期間の応答を見るだけで長期間の予測可能
　　　　　　　　神経群は必ずしも互いに同期しない(5)
　　　　　・relaxation oscillators
　　　　　　　　phase-dependent excitability
　　　　　　　　情報転送位相(duty cycle)と受信位相の分離可能
　　　　　　　　同期は頑強で安定(5)
　　・結合問題
　　　　・γ振動(15) (but see 50, 51)
　　　　・cell assembliesとγ振動(52)

　　・学習
　　　　・global oscillation
　　・

(53, 55). In other words, the
conditions that gave rise to a rhythm are
“frozen” into the deterministic nature of the
oscillatory dynamics.
The “default” state of the unperturbed,
sleeping brain is a complex system of
numerous self-governed oscillations, particularly
in the thalamocortical system
(2, 45, 46). The
content of these
oscillations reflects
spike sequence patterns
created by
prior waking experience
(2, 7, 11, 12).
Synaptic modifications
brought about
by learning are thus
frozen into the various
time windows
of self-organized
oscillatory networks
of sleep to be turned
ultimately into longterm
memory by
means of functional
and structural synaptic
modifications
(11, 12). This selfsustained
replay of
learned information
allows for the dissemination
and combination
of temporally
discontiguous patterns
of activity
acquired during
previous waking
behaviors. This “off
line,” assembly
grouping mechanism may be the physiological
basis for the creativity and insightpromoting
nature of sleep (56).
Representation by phase information. The
timing of neuronal spikes in oscillatory networks
is under the combined influence of
external inputs and the internal dynamics of
the network (52). This is the basis for information
representation by phase. Consider the
consequences of phase-coupled rhythmic somatic
inhibition and dendritic depolarization
in a single pyramidal neuron, a typical scenario
during sustained oscillations (7, 19). If
the somatic inhibitory oscillation remains unchanged
but dendritic depolarization increases,
spike threshold will be reached at progressively
earlier phases of the inhibitory cycles
(19). Generalizing this scenario to a network
of cells, neurons with stronger dendritic
inputs will discharge earlier in the
cycle than neurons with weak dendritic
excitation. This property is universal for
oscillators: The coupling strength is proportional
to the magnitude of phase advancement
(21, 57). Thus, the input
magnitude– dependent forward phase shift
of action potentials (19) may be exploited
for short-term storage of information (55).
The first experimental support for representation
by phase came from work on the
hippocampus (18). When the rat walks
through the receptive field of a recorded pyramidal
cell, the phase assignment of spikes
progressively advances from the peak to the
trough of theta while the rat enters into the
place field and reaches its center (Fig. 3, A
and B), independent of the size or shape of
the field or the speed of the rat (18, 58, 59).
A consequence of this relation is that the
future positions of place fields can be predicted
from the phase sequence of spikes of
neuronal assemblies in a single theta cycle
(Fig. 3, C and D) (57, 60). At least part of the
spike phase precession effect is accounted for
by the mathematical rules of relaxation oscillators
(57) (Fig. 3E). On the other hand,
prediction of long-term behavior from phase
information is a characteristic feature of harmonic
oscillators. The within-cycle phase sequences
of assemblies are discrete quanta of
information, the beginning and end of which
are marked by an oscillatory cycle. The repeating
temporal sequences of spikes over
several cycles can exploit spike-timing–
dependent plasticity (48) for consolidating
representations (61). Without oscillations,
such packaging is not possible, as evidenced
by the impairment of learned spatial behavior
after interfering with theta oscillation (62).
Unexplored Benefits of Brain
Oscillations
Oscillatory coupling of neuronal assemblies
is usually examined within single frequency
bands. However, different oscillatory classes
might carry different dimensions of brain integration,
and the coupling of two or more
oscillators could provide enhanced combinatorial
opportunities for storing complex temporal
patterns and optimizing synaptic
weights when used in conjunction with appropriate
algorithms. The nature of these algorithms
in the brain remains to be discovered.
Slow rhythms synchronize large spatial
domains and can bind together specific assemblies
by the appropriate timing of higher
frequency localized oscillations (15, 16, 29,
45). The sleeping brain is a rich source of
self-organized multiple oscillators, but the
content of these rhythms is poorly understood
(7, 45). Large-scale, simultaneous recording
of multiple neuron activity across interacting
brain systems will be required to reveal how
neuronal assemblies are specifically organized
by sleep rhythms. The study of oscillations
has always been entwined with the
study of self-organization. Understanding
the physiological mechanisms of selfemerging
oscillations not only will provide
insight into their functions but also may
assist in the diagnosis and treatment of
brain disorders (63, 64). Uncovering the
relation between neuronal oscillators and
the much slower biochemical-molecular oscillators,
including ultradian and circadian
rhythms (33), is yet another daunting challenge.
An important function of the brain is
the prediction of future probabilities. Feedforward
and feedback networks predict
well what happens next. Oscillators are
very good at predicting when.