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The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike–field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical.

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. [ABSTRACT FROM AUTHOR]

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike-field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical., Competing Interests: ZD, ND, TF, LM, JR No competing interests declared, (© 2023, Davis et al.)

Mouse hippocampus CA1 place-cell discharge typically encodes current location, but during slow gamma dominance (SG dom), when SG oscillations (30–50 Hz) dominate mid-frequency gamma oscillations (70–90 Hz) in CA1 local field potentials, CA1 discharge switches to represent distant recollected locations. We report that dentate spike type 2 (DS M) events initiated by medial entorhinal cortex II (MECII)→ dentate gyrus (DG) inputs promote SG dom and change excitation-inhibition coordinated discharge in DG, CA3, and CA1, whereas type 1 (DS L) events initiated by lateral entorhinal cortex II (LECII)→DG inputs do not. Just before SG dom , LECII-originating SG oscillations in DG and CA3-originating SG oscillations in CA1 phase and frequency synchronize at the DS M peak when discharge within DG and CA3 increases to promote excitation-inhibition cofiring within and across the DG→CA3→CA1 pathway. This optimizes discharge for the 5–10 ms DG-to-CA1 neuro-transmission that SG dom initiates. DS M properties identify extrahippocampal control of SG dom and a cortico-hippocampal mechanism that switches between memory-related modes of information processing. [Display omitted] • CA1 represents distant, recollected locations during slow gamma dominance (SG dom) • Medial entorhinal cortex-originating dentate spikes (DS M) promote non-local firing • DS M promotes coordinated E-I coupled discharge in DG, CA3, and CA1 leading to SG dom • DG and CA1 sync during DS M , optimizing spike-field timing for information transfer MECII-originating type 2 dentate spikes (DS M) promote dominance of CA1 slow (∼40-Hz) over mid-frequency (∼80-Hz) gamma oscillations when CA1 represents non-local recollections. Dvorak et al. show that DS M coordinates cofiring within DG, CA3, and CA1 networks and optimizes discharge timing between DG and CA1 for information transfer during memory recall. [ABSTRACT FROM AUTHOR]