scholarly journals Glutamatergic drive along the septo-temporal axis of hippocampus boosts prelimbic oscillations in the neonatal mouse

2017 ◽  
Author(s):  
Joachim Ahlbeck ◽  
Lingzhen Song ◽  
Mattia Chini ◽  
Antonio Candela ◽  
Sebastian H. Bitzenhofer ◽  
...  
Keyword(s):  
Long Range ◽  
Hippocampal Neurons ◽  
High Efficiency ◽  
Dorsal Hippocampus ◽  
Phase Locking ◽  
Ventral Hippocampus ◽  
Neuronal Firing ◽  
Projection Neurons ◽  
List Type ◽  
Axonal Projections

SUMMARYThe long-range coupling within prefrontal-hippocampal networks that account for cognitive performance emerges early in life. The discontinuous hippocampal theta bursts have been proposed to drive the generation of neonatal prefrontal oscillations, yet the cellular substrate of these early interactions is still unresolved. Here, we selectively target optogenetic manipulation of glutamatergic projection neurons in the CA1 area of either dorsal or intermediate/ventral hippocampus at neonatal age to elucidate their contribution to the emergence of prefrontal oscillatory entrainment. We show that despite stronger theta and ripples power in dorsal hippocampus, the prefrontal cortex is mainly coupled with intermediate/ventral hippocampus by phase-locking of neuronal firing via dense direct axonal projections. Theta band-confined activation by light of pyramidal neurons in intermediate/ventral but not dorsal CA1 that were transfected by in utero electroporation with high-efficiency channelrhodopsin boosts prefrontal oscillations. Our data causally elucidates the cellular origin of the long-range coupling in the developing brain.HighlightsNeonatal theta bursts, sharp waves and ripples vary along septo-temporal axisHippocampal activity times prefrontal oscillations via direct axonal projectionsSelective hippocampal targeting along septo-temporal axis causes precise firingLight stimulation of hippocampal neurons at 8 Hz boosts prefrontal oscillations

scholarly journals Glutamatergic drive along the septo-temporal axis of hippocampus boosts prelimbic oscillations in the neonatal mouse

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Joachim Ahlbeck ◽  
Lingzhen Song ◽  
Mattia Chini ◽  
Sebastian H Bitzenhofer ◽  
Ileana L Hanganu-Opatz
Keyword(s):  
Long Range ◽  
Pyramidal Neurons ◽  
High Efficiency ◽  
Dorsal Hippocampus ◽  
Phase Locking ◽  
Ventral Hippocampus ◽  
Neuronal Firing ◽  
Projection Neurons ◽  
Cellular Origin ◽  
Hippocampal Networks

The long-range coupling within prefrontal-hippocampal networks that account for cognitive performance emerges early in life. The discontinuous hippocampal theta bursts have been proposed to drive the generation of neonatal prefrontal oscillations, yet the cellular substrate of these early interactions is still unresolved. Here, we selectively target optogenetic manipulation of glutamatergic projection neurons in the CA1 area of either dorsal or intermediate/ventral hippocampus at neonatal age to elucidate their contribution to the emergence of prefrontal oscillatory entrainment. We show that despite stronger theta and ripples power in dorsal hippocampus, the prefrontal cortex is mainly coupled with intermediate/ventral hippocampus by phase-locking of neuronal firing via dense direct axonal projections. Theta band-confined activation by light of pyramidal neurons in intermediate/ventral but not dorsal CA1 that were transfected by in utero electroporation with high-efficiency channelrhodopsin boosts prefrontal oscillations. Our data causally elucidate the cellular origin of the long-range coupling in the developing brain.

scholarly journals Robust information routing by dorsal subiculum neurons

2021 ◽  
Vol 7 (11) ◽  
pp. eabf1913
Author(s):  
Takuma Kitanishi ◽  
Ryoko Umaba ◽  
Kenji Mizuseki
Keyword(s):  
Nucleus Accumbens ◽  
Large Scale ◽  
Dorsal Hippocampus ◽  
Retrosplenial Cortex ◽  
Projection Neurons ◽  
Target Region ◽  
Ca1 Neurons ◽  
Axonal Projections ◽  
Output Structure ◽  
Hippocampal Ca1

The dorsal hippocampus conveys various information associated with spatial navigation; however, how the information is distributed to multiple downstream areas remains unknown. We investigated this by identifying axonal projections using optogenetics during large-scale recordings from the rat subiculum, the major hippocampal output structure. Subicular neurons demonstrated a noise-resistant representation of place, speed, and trajectory, which was as accurate as or even more accurate than that of hippocampal CA1 neurons. Speed- and trajectory-dependent firings were most prominent in neurons projecting to the retrosplenial cortex and nucleus accumbens, respectively. Place-related firing was uniformly observed in neurons targeting the retrosplenial cortex, nucleus accumbens, anteroventral thalamus, and medial mammillary body. Theta oscillations and sharp-wave/ripples tightly controlled the firing of projection neurons in a target region–specific manner. In conclusion, the dorsal subiculum robustly routes diverse navigation-associated information to downstream areas.

scholarly journals Different encoding of reward location in dorsal and ventral hippocampus

2021 ◽  
Author(s):  
Przemyslaw Jarzebowski ◽  
Y. Audrey Hay ◽  
Benjamin F. Grewe ◽  
Ole Paulsen
Keyword(s):  
Calcium Imaging ◽  
Hippocampal Neurons ◽  
Dorsal Hippocampus ◽  
Spatial Navigation ◽  
Ventral Hippocampus ◽  
Place Cells ◽  
Cognitive Map ◽  
Population Activity ◽  
Reward Seeking ◽  
Seeking Behavior

SummaryHippocampal neurons encode a cognitive map for spatial navigation1. When they fire at specific locations in the environment, they are known as place cells2. In the dorsal hippocampus place cells accumulate at current navigational goals, such as learned reward locations3–6. In the intermediate-to-ventral hippocampus (here collectively referred to as ventral hippocampus), neurons fire across larger place fields7–10 and regulate reward- seeking behavior11–16, but little is known about their involvement in reward-directed navigation. Here, we compared the encoding of learned reward locations in the dorsal and ventral hippocampus during spatial navigation. We used calcium imaging with a head- mounted microscope to track the activity of CA1 cells over multiple days during which mice learned different reward locations. In dorsal CA1 (dCA1), the overall number of active place cells increased in anticipation of reward but the recruited cells changed with the reward location. In ventral CA1 (vCA1), the activity of the same cells anticipated the reward locations. Our results support a model in which the dCA1 cognitive map incorporates a changing population of cells to encode reward proximity through increased population activity, while the vCA1 provides a reward-predictive code in the activity of a specific subpopulation of cells. Both of these location-invariant codes persisted over time, and together they provide a dual hippocampal reward-location code, assisting goal- directed navigation17, 18.

scholarly journals Striatal Versus Hippocampal Representations During Win-Stay Maze Performance

2009 ◽  
Vol 101 (3) ◽  
pp. 1575-1587 ◽  
Author(s):  
Joshua D. Berke ◽  
Jason T. Breck ◽  
Howard Eichenbaum
Keyword(s):  
Working Memory ◽  
Neural Coding ◽  
Hippocampal Neurons ◽  
Spatial Information ◽  
Spatial Working Memory ◽  
Dorsal Hippocampus ◽  
Place Cell ◽  
Projection Neurons ◽  
Behavioral Strategies ◽  
Cell Type

The striatum and hippocampus are widely held to be components of distinct memory systems that can guide competing behavioral strategies. However, some electrophysiological studies have suggested that neurons in both structures encode spatial information and may therefore make similar contributions to behavior. In rats well trained to perform a win-stay radial maze task, we recorded simultaneously from dorsal hippocampus and from multiple striatal subregions, including both lateral areas implicated in motor responses to cues and medial areas that work cooperatively with hippocampus in cognitive operations. In each brain region, movement through the maze was accompanied by the continuous sequential activation of sets of projection neurons. Hippocampal neurons overwhelmingly were active at a single spatial location (place cells). Striatal projection neurons were active at discrete points within the progression of every trial—especially during choices or following reward delivery—regardless of spatial position. Place-cell–type firing was not observed even for medial striatal cells entrained to the hippocampal theta rhythm. We also examined neural coding in earlier training sessions, when rats made use of spatial working memory to guide choices, and again found that striatal cells did not show place-cell–type firing. Prospective or retrospective encoding of trajectory was not observed in either hippocampus or striatum, at either training stage. Our results indicate that, at least in this task, dorsal hippocampus uses a spatial foundation for information processing that is not substantially modulated by spatial working memory demands. By contrast, striatal cells do not use such a spatial foundation, even in medial subregions that cooperate with hippocampus in the selection of spatial strategies. The progressive dominance of a striatum-dependent strategy does not appear to be accompanied by large changes in striatal or hippocampal single-cell representations, suggesting that the conflict between strategies may be resolved elsewhere.

scholarly journals Novel long-range inhibitory nNOS-expressing hippocampal cells

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Zoé Christenson Wick ◽  
Madison R Tetzlaff ◽  
Esther Krook-Magnuson
Keyword(s):  
Long Range ◽  
Hippocampal Neurons ◽  
Viral Vector ◽  
Muscarinic Acetylcholine Receptor ◽  
Neuronal Cell ◽  
Cell Types ◽  
Projection Neurons ◽  
Neuronal Diversity ◽  
Diagonal Band ◽  
Rich Diversity

The hippocampus, a brain region that is important for spatial navigation and episodic memory, benefits from a rich diversity of neuronal cell-types. Through the use of an intersectional genetic viral vector approach in mice, we report novel hippocampal neurons which we refer to as LINCs, as they are long-range inhibitory neuronal nitric oxide synthase (nNOS)-expressing cells. LINCs project to several extrahippocampal regions including the tenia tecta, diagonal band, and retromammillary nucleus, but also broadly target local CA1 cells. LINCs are thus both interneurons and projection neurons. LINCs display regular spiking non-pyramidal firing patterns, are primarily located in the stratum oriens or pyramidale, have sparsely spiny dendrites, and do not typically express somatostatin, VIP, or the muscarinic acetylcholine receptor M2. We further demonstrate that LINCs can strongly influence hippocampal function and oscillations, including interregional coherence. The identification and characterization of these novel cells advances our basic understanding of both hippocampal circuitry and neuronal diversity.

scholarly journals Regulation des Angstverhaltens — zur Rolle neuronaler Netzwerke

BIOspektrum ◽  
2019 ◽  
Vol 25 (7) ◽  
pp. 711-714
Author(s):  
Nina Dedic ◽  
Jan M. Deussing
Keyword(s):  
Stress Response ◽  
Ventral Tegmental Area ◽  
Long Range ◽  
Dopamine Release ◽  
Receptor Type ◽  
Projection Neurons ◽  
Neuronal Circuit ◽  
Brain Reward ◽  
Crh Receptor Type 1

AbstractThe corticotropin-releasing hormone (CRH) system orchestrates the organism’s stress response including the regulation of adaptive be haviours. Here we describe a novel neuronal circuit, which acts anxiety suppressing and positively modulates dopamine release. This anxiolytic circuit comprises inhibitory CRH-expressing, long-range projection neurons within the extended amygdala. These neurons innervate the ventral tegmental area, a prominent brain reward center that expresses high levels of CRH receptor type 1.

scholarly journals Efficient neural decoding of self-location with a deep recurrent network

2018 ◽  
Author(s):  
Ardi Tampuu ◽  
Tambet Matiisen ◽  
H. Freyja Ólafsdóttir ◽  
Caswell Barry ◽  
Raul Vicente
Keyword(s):  
Hippocampal Neurons ◽  
Cell Activity ◽  
Recurrent Network ◽  
Brain Network ◽  
Place Cells ◽  
Neuronal Firing ◽  
Temporal Context ◽  
Central Component ◽  
Neural Decoding ◽  
Freely Moving

AbstractPlace cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal’s location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings. RNNs are biologically plausible models of neural circuits that learn to incorporate relevant temporal context without the need to make complicated assumptions about the use of prior information to predict the current state. When decoding animal position from spike counts in 1D and 2D-environments, we show that the RNN consistently outperforms a standard Bayesian model with flat priors. In addition, we also conducted a set of sensitivity analysis on the RNN decoder to determine which neurons and sections of firing fields were the most influential. We found that the application of RNNs to neural data allowed flexible integration of temporal context, yielding improved accuracy relative to a commonly used Bayesian approach and opens new avenues for exploration of the neural code.Author summaryBeing able to accurately self-localize is critical for most motile organisms. In mammals, place cells in the hippocampus appear to be a central component of the brain network responsible for this ability. In this work we recorded the activity of a population of hippocampal neurons from freely moving rodents and carried out neural decoding to determine the animals’ locations. We found that a machine learning approach using recurrent neural networks (RNNs) allowed us to predict the rodents’ true positions more accurately than a standard Bayesian method with flat priors. The RNNs are able to take into account past neural activity without making assumptions about the statistics of neuronal firing. Further, by analyzing the representations learned by the network we were able to determine which neurons, and which aspects of their activity, contributed most strongly to the accurate decoding.

scholarly journals Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex

Nature ◽  
2013 ◽  
Vol 499 (7458) ◽  
pp. 336-340 ◽  
Author(s):  
Jerry L. Chen ◽  
Stefano Carta ◽  
Joana Soldado-Magraner ◽  
Bernard L. Schneider ◽  
Fritjof Helmchen
Keyword(s):  
Somatosensory Cortex ◽  
Long Range ◽  
Projection Neurons

Long-range GABAergic projection neurons in the cat neocortex

2007 ◽  
Vol 503 (3) ◽  
pp. 421-431 ◽  
Author(s):  
Shigeyoshi Higo ◽  
Naoko Udaka ◽  
Nobuaki Tamamaki
Keyword(s):  
Long Range ◽  
Projection Neurons

scholarly journals Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

2018 ◽  
Vol 115 (13) ◽  
pp. E3017-E3025 ◽  
Author(s):  
James P. Roach ◽  
Aleksandra Pidde ◽  
Eitan Katz ◽  
Jiaxing Wu ◽  
Nicolette Ognjanovski ◽  
...  
Keyword(s):  
Hippocampal Neurons ◽  
External Input ◽  
Neuronal Firing ◽  
Network Activity ◽  
Dependent Manner ◽  
Information Encoding ◽  
Subthreshold Oscillations ◽  
Neuronal Network Model ◽  
And Performance

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations’ spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further “learning” (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

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