The hippocampal dentate gyrus is often viewed as a segregator of upstream information. Physiological support for such function has been hampered by a lack of well-defined characteristics that can ...identify granule cells and mossy cells. We developed an electrophysiology-based classification of dentate granule cells and mossy cells in mice that we validated by optogenetic tagging of mossy cells. Granule cells exhibited sparse firing, had a single place field, and showed only modest changes when the mouse was tested in different mazes in the same room. In contrast, mossy cells were more active, had multiple place fields and showed stronger remapping of place fields under the same conditions. Although the granule cell-mossy cell synapse was strong and facilitating, mossy cells rarely “inherited” place fields from single granule cells. Our findings suggest that the granule cells and mossy cells could be modulated separately and their joint action may be critical for pattern separation.
•Dentate granule cells and mossy cells have distinct physiological characteristics•Granule cells have none or a single place field and remap less in different mazes•Mossy cells have mainly multiple place fields and remap more in different mazes•Mossy cells rarely “inherit” place fields from single granule cells
Senzai and Buzsáki developed an electrophysiology-based classification of dentate granule and mossy cells. Mossy cells and CA3 pyramidal cells remap more in different mazes than their upstream granule cells, and they rarely “inherit” place fields from single granule cells.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We often assume that the variables of functional and structural brain parameters - such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, ...the number of synaptic contacts between neurons and the size of dendritic boutons - have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions - from synapses to cognition - are related to each other.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Despite the several-thousand-fold increase of brain volume during the course of mammalian evolution, the hierarchy of brain oscillations remains remarkably preserved, allowing for multiple-time-scale ...communication within and across neuronal networks at approximately the same speed, irrespective of brain size. Deployment of large-diameter axons of long-range neurons could be a key factor in the preserved time management in growing brains. We discuss the consequences of such preserved network constellation in mental disease, drug discovery, and interventional therapies.
Gyorgy Buzsáki, Wolf Singer, and Nikos Logothetis argue that brain oscillations and rhythms and the temporal organization of neural activity across the brain not only are key constraints to the scaling of brain size and hence to the evolution of the brain but also contribute significantly to disorders of the brain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Neural computations are often compared to instrument-measured distance or duration, and such relationships are interpreted by a human observer. However, neural circuits do not depend on human-made ...instruments but perform computations relative to an internally defined rate-of-change. While neuronal correlations with external measures, such as distance or duration, can be observed in spike rates or other measures of neuronal activity, what matters for the brain is how such activity patterns are utilized by downstream neural observers. We suggest that hippocampal operations can be described by the sequential activity of neuronal assemblies and their internally defined rate of change without resorting to the concept of space or time.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
During non-rapid eye movement (NREM) sleep, neuronal populations in the mammalian forebrain alternate between periods of spiking and inactivity. Termed the slow oscillation in the neocortex and sharp ...wave-ripples in the hippocampus, these alternations are often considered separately but are both crucial for NREM functions. By directly comparing experimental observations of naturally-sleeping rats with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep in the rodent, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.
Theories on the functions of the hippocampal system are based largely on two fundamental discoveries: the amnestic consequences of removing the hippocampus and associated structures in the famous ...patient H.M. and the observation that spiking activity of hippocampal neurons is associated with the spatial position of the rat. In the footsteps of these discoveries, many attempts were made to reconcile these seemingly disparate functions. Here we propose that mechanisms of memory and planning have evolved from mechanisms of navigation in the physical world and hypothesize that the neuronal algorithms underlying navigation in real and mental space are fundamentally the same. We review experimental data in support of this hypothesis and discuss how specific firing patterns and oscillatory dynamics in the entorhinal cortex and hippocampus can support both navigation and memory.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The hippocampus constructs a map of the environment. How this “cognitive map” is utilized by other brain regions to guide behavior remains unexplored. To examine how neuronal firing patterns in the ...hippocampus are transmitted and transformed, we recorded neurons in its principal subcortical target, the lateral septum (LS). We observed that LS neurons carry reliable spatial information in the phase of action potentials, relative to hippocampal theta oscillations, while the firing rates of LS neurons remained uninformative. Furthermore, this spatial phase code had an anatomical microstructure within the LS and was bound to the hippocampal spatial code by synchronous gamma frequency cell assemblies. Using a data-driven model, we show that rate-independent spatial tuning arises through the dynamic weighting of CA1 and CA3 cell assemblies. Our findings demonstrate that transformation of the hippocampal spatial map depends on higher-order theta-dependent neuronal sequences.
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•LS neurons carry a rate-independent phase code for position•The LS spatial phase code is as reliable as the hippocampal rate or phase codes•LS phase code is a transformation of the distributed CA1/CA3 cognitive map•The Hippocampus-LS circuit may be the conduit for translating representation to action
How abstract representations are translated into action is unknown. Tingley and Buzsáki describe how the hippocampal cognitive map is “read out” by a target region. The transformation relies on population coordination referenced to theta oscillations rather than the tuning of individual neurons.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The relationship between mesoscopic local field potentials (LFPs) and single-neuron firing in the multi-layered neocortex is poorly understood. Simultaneous recordings from all layers in the primary ...visual cortex (V1) of the behaving mouse revealed functionally defined layers in V1. The depth of maximum spike power and sink-source distributions of LFPs provided consistent laminar landmarks across animals. Coherence of gamma oscillations (30–100 Hz) and spike-LFP coupling identified six physiological layers and further sublayers. Firing rates, burstiness, and other electrophysiological features of neurons displayed unique layer and brain state dependence. Spike transmission strength from layer 2/3 cells to layer 5 pyramidal cells and interneurons was stronger during waking compared with non-REM sleep but stronger during non-REM sleep among deep-layer excitatory neurons. A subset of deep-layer neurons was active exclusively in the DOWN state of non-REM sleep. These results bridge mesoscopic LFPs and single-neuron interactions with laminar structure in V1.
•Multisite LFP recording and LFP-spike coupling identified physiological layers in V1•The prominent 3–6 Hz LFP shared characteristic features with primate alpha rhythm•Spike transmission strength from layer 2/3 to layer 5 neurons was stronger during waking•A subset of layer 6 neurons was active selectively in the DOWN state of non-REM sleep
The relationship between LFP patterns and single-neuron firing in the visual cortex is identified by Senzai et al. by using high-density silicon probe recordings, ICA-based LFP analysis, LFP-spike coupling, and spike transmission probability in freely moving mice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The consolidation of context-dependent emotional memory requires communication between the hippocampus and the basolateral amygdala (BLA), but the mechanisms of this process are unknown. We recorded ...neuronal ensembles in the hippocampus and BLA while rats learned the location of an aversive air puff on a linear track, as well as during sleep before and after training. We found coordinated reactivations between the hippocampus and the BLA during non-REM sleep following training. These reactivations peaked during hippocampal sharp wave-ripples (SPW-Rs) and involved a subgroup of BLA cells positively modulated during hippocampal SPW-Rs. Notably, reactivation was stronger for the hippocampus-BLA correlation patterns representing the run direction that involved the air puff than for the 'safe' direction. These findings suggest that consolidation of contextual emotional memory occurs during ripple-reactivation of hippocampus-amygdala circuits.
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