We examined whether excitation and inhibition are balanced in hippocampal cortical networks. Extracellular field and single-unit activity were recorded by multiple tetrodes and multisite silicon probes to reveal the timing of the activity of hippocampal CA1 pyramidal cells and classes of interneurons during theta waves and sharp wave burst (SPW)-associated field ripples. The somatic and dendritic inhibition of pyramidal cells was deduced from the activity of interneurons in the pyramidal layer [int(p)] and in the alveus and st. oriens [int(a/o)], respectively. Int(p) and int(a/o) discharged an average of 60 and 20 degrees before the population discharge of pyramidal cells during the theta cycle, respectively. SPW ripples were associated with a 2.5-fold net increase of excitation. The discharge frequency of int(a/o) increased, decreased ("anti-SPW" cells), or did not change ("SPW-independent" cells) during SPW, suggesting that not all interneurons are innervated by pyramidal cells. Int(p) either fired together with (unimodal cells) or both before and after (bimodal cells) the pyramidal cell burst. During fast-ripple oscillation, the activity of interneurons in both the int(p) and int(a/o) groups lagged the maximum discharge probability of pyramidal neurons by 1-2 msec. Network state changes, as reflected by field activity, covaried with changes in the spike train dynamics of single cells and their interactions. Summed activity of parallel-recorded interneurons, but not of pyramidal cells, reliably predicted theta cycles, whereas the reverse was true for the ripple cycles of SPWs. We suggest that network-driven excitability changes provide temporal windows of opportunity for single pyramidal cells to suppress, enable, or facilitate selective synaptic inputs.

Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.

Gamma frequency oscillations (30-100 Hz) have been suggested to underlie various cognitive and motor functions. Here, we examine the generation of gamma oscillation currents in the hippocampus, using two-dimensional, 96-site silicon probes. Two gamma generators were identified, one in the dentate gyrus and another in the CA3-CA1 regions. The coupling strength between the two oscillators varied during both theta and nontheta states. Both pyramidal cells and interneurons were phase-locked to gamma waves. Anatomical connectivity, rather than physical distance, determined the coupling strength of the oscillating neurons. CA3 pyramidal neurons discharged CA3 and CA1 interneurons at latencies indicative of monosynaptic connections. Intrahippocampal gamma oscillation emerges in the CA3 recurrent system, which entrains the CA1 region via its interneurons.

Both neocortical and hippocampal networks organize the firing patterns of their neurons by prominent oscillations during sleep, but the functional role of these rhythms is not well understood. Here, we show a robust correlation of neuronal discharges between the somatosensory cortex and hippocampus on both slow and fine time scales in the mouse and rat. Neuronal bursts in deep cortical layers, associated with sleep spindles and delta waves͞ slow rhythm, effectively triggered hippocampal discharges related to fast (ripple) oscillations. We hypothesize that oscillation-mediated temporal links coordinate specific information transfer between neocortical and hippocampal cell assemblies. Such a neocortical-hippocampal interplay may be important for memory consolidation.T he means by which ensembles of neurons interact with each other for an effective transfer and storage of information are not well understood. It is generally believed that network oscillatory rhythms within and across structures support these functions (1, 2). One current working hypothesis is that information transfer at the hippocampal-neocortical interface in sleep-wake cycle is involved in the encoding and consolidation of memories (3-12). The oscillatory patterns in these structures are fundamentally different in states of waking and sleep (13-15). Attentive, exploratory behavior is characterized by neocortical fast gamma activity and hippocampal theta͞gamma oscillations (16-18). The most prominent hippocampal pattern during slow wave sleep is a transient burst of CA3-CA1 pyramidal cells associated with a sharp wave in stratum (str.) radiatum and fast field oscillation (140-to 200-Hz ripple) in the CA1 pyramidal layer (19-21). Sleep patterns of the neocortex include thalamocortical sleep spindles (12-18 Hz) and delta waves (1-4 Hz) (13,22). Whether and how these macroscopic rhythms support the coding, transfer, and consolidation of information is not understood.Recently, it has been suggested that hippocampal ripples and sleep spindles in the prefrontal cortex are temporally linked on the scale of Ϸ1 s (23). Whether neurons of the hippocampus and neocortex are involved in a more specific spatiotemporal interaction during sleep remains to be explored. To address this issue, we used multisite recording of unit and field activity in the hippocampus and somatosensory cortex. The somatosensory cortex was chosen because it is the most prominent source of sensory information in the rat (24), and, unlike the prefrontal cortex (25), it does not receive monosynaptic input from the hippocampus. Here, we demonstrate temporal coupling of neuronal activity between the somatosensory cortex and hippocampus on both slow and fine time scales and show that neocortical activity can time the occurrence of hippocampal sharp waves and ripples. Such coupling may assist linking neocortical and hippocampal cell assemblies during slow wave sleep. Materials and MethodsTen male mice (six progeny of C57B6͞J and 129S6͞SvEvTac hybrid mice and four connexin36 knockout; ref. 26;...

Neurons can produce action potentials with high temporal precision. A fundamental issue is whether, and how, this capability is used in information processing. According to the 'cell assembly' hypothesis, transient synchrony of anatomically distributed groups of neurons underlies processing of both external sensory input and internal cognitive mechanisms. Accordingly, neuron populations should be arranged into groups whose synchrony exceeds that predicted by common modulation by sensory input. Here we find that the spike times of hippocampal pyramidal cells can be predicted more accurately by using the spike times of simultaneously recorded neurons in addition to the animals location in space. This improvement remained when the spatial prediction was refined with a spatially dependent theta phase modulation. The time window in which spike times are best predicted from simultaneous peer activity is 10-30 ms, suggesting that cell assemblies are synchronized at this timescale. Because this temporal window matches the membrane time constant of pyramidal neurons, the period of the hippocampal gamma oscillation and the time window for synaptic plasticity, we propose that cooperative activity at this timescale is optimal for information transmission and storage in cortical circuits.