Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

Fujisawa, Shigeyoshi; Amarasingham, Asohan; Harrison, Matthew T.; Buzsáki, György

doi:10.1038/nn.2134

Cited by 646 publications

(902 citation statements)

References 47 publications

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“…We found that the short-latency monosynaptic effects from spike-train cross-correlograms were confined to the same or very proximal electrodes (Fig. 3D), in agreement with the rapid decrement with distance of synaptic contact probability (24,33). The extent of monosynaptic contacts in local circuits is still a subject of debate (1,2,24,33), and it is important to bear in mind that spike-train correlation analysis (33) is likely to underestimate the number of actual contacts because it is based only on suprathreshold activity from pre-and postsynaptic neurons.…”

Section: Discussionsupporting

confidence: 76%

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Peyrache

Dehghani

Eskandar

et al. 2012

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

175

229

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Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.spontaneous activity | ensemble recordings | single unit | functional dynamics F rom columnar microcircuits (1-3) to higher-order neuronal functional units, neocortical dynamics are characterized by a large range of spatial and temporal scales (4, 5). Recent technical improvements have allowed the nature of these dynamics in the human brain to be directly explored: Single-neuron activity in conjunction with local field potentials (LFPs) can be detected from the cerebral cortex and hippocampus in the course of intense monitoring of brain activity before surgical treatment of epileptic foci (6). Modern electrode systems provide the possibility of extracellular recordings of neuronal ensembles by using either microwires (7) or high-density microelectrode arrays (8,9). Prior efforts have demonstrated excellent recordings of single-neuron activity in human cerebral cortex (10-12).Separation of units between "regular-spiking" (RS) and "fastspiking" (FS) neurons, presumably excitatory (pyramidal) and inhibitory (interneuron) cells, respectively, is commonly practiced in animal experiments. In the neocortex of various mammalian species, RS and FS cells can be reliably separated based on spike waveform, duration, and firing rates (13,14). Similar criteria were also used to successfully separate units into putative pyramidal (Pyr) cells and inhibitory interneurons (Int) in human hippocampus...

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Section: Discussionsupporting

confidence: 76%

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Peyrache

Dehghani

Eskandar

et al. 2012

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

175

229

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“…Note the presence of spikes on several sites of the same shank and lack of the same spikes across the different shanks, indicating that electrodes placed Ն200 m laterally record from different cell populations. Spikes and local field potentials are both visible in this wide-band (1 Hz to 10 kHz) recording (Fujisawa et al, 2008). Modified from Buzsáki (2004), his Figure 2A, reprinted with permission.…”

mentioning

confidence: 92%

“…Rectangles are unidentified units. Note a large "hub" formed by an interneuron and its multiple partners [Fujisawa et al (2008), their supplemental Fig. 11].…”

mentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

258

176

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IntroductionTechnological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.

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“…Self-organized neuronal sequences have been observed in several cortical structures (2)(3)(4)(5) and neuronal models (6)(7). In the hippocampus, sequential activity of place cells (8) may be induced by external landmarks perceived by the animal during spatial navigation (9) and conveyed to CA1 by the upstream CA3 region or layer 3 of the entorhinal cortex (10).…”

mentioning

confidence: 99%

Local generation of multineuronal spike sequences in the hippocampal CA1 region

Stark

Roux

Eichler

et al. 2015

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

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103

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Sequential activity of multineuronal spiking can be observed during theta and high-frequency ripple oscillations in the hippocampal CA1 region and is linked to experience, but the mechanisms underlying such sequences are unknown. We compared multineuronal spiking during theta oscillations, spontaneous ripples, and focal optically induced high-frequency oscillations ("synthetic" ripples) in freely moving mice. Firing rates and rate modulations of individual neurons, and multineuronal sequences of pyramidal cell and interneuron spiking, were correlated during theta oscillations, spontaneous ripples, and synthetic ripples. Interneuron spiking was crucial for sequence consistency. These results suggest that participation of single neurons and their sequential order in population events are not strictly determined by extrinsic inputs but also influenced by local-circuit properties, including synapses between local neurons and single-neuron biophysics.A hypothesized hallmark of cognition is self-organized sequential activation of neuronal assemblies (1). Self-organized neuronal sequences have been observed in several cortical structures (2-5) and neuronal models (6-7). In the hippocampus, sequential activity of place cells (8) may be induced by external landmarks perceived by the animal during spatial navigation (9) and conveyed to CA1 by the upstream CA3 region or layer 3 of the entorhinal cortex (10). Internally generated sequences have been also described in CA1 during theta oscillations in memory tasks (4, 11), raising the possibility that a given neuronal substrate is responsible for generating sequences at multiple time scales. The extensive recurrent excitatory collateral system of the CA3 region has been postulated to be critical in this process (4,7,12,13).The sequential activity of place cells is "replayed" during sharp waves (SPW) in a temporally compressed form compared with rate modulation of place cells (14)(15)(16)(17)(18)(19)(20) and may arise from the CA3 recurrent excitatory networks during immobility and slow wave sleep. The SPW-related convergent depolarization of CA1 neurons gives rise to a local, fast oscillatory event in the CA1 region ("ripple, refs. 8 and 21). Selective elimination of ripples during or after learning impairs memory performance (22-24), suggesting that SPW ripple-related replay assists memory consolidation (12, 13). Although the local origin of the ripple oscillations is well demonstrated (25, 26), it has been tacitly assumed that the ripple-associated, sequentially ordered firing of CA1 neurons is synaptically driven by the upstream CA3 cell assemblies (12, 15), largely because excitatory recurrent collaterals in the CA1 region are sparse (27). However, sequential activity may also emerge by local mechanisms, patterned by the different biophysical properties of CA1 pyramidal cells and their interactions with local interneurons, which discharge at different times during a ripple (28-30). A putative function of the rich variety of interneurons is temporal organization of princip...

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Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

Cited by 646 publications

References 47 publications

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Local generation of multineuronal spike sequences in the hippocampal CA1 region

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