Different encoding of reward location in dorsal and ventral hippocampus

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.

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Persistent trajectory-modulated hippocampal neurons support memory-guided navigation

10.1101/676593 ◽

2019 ◽

Author(s):

Nathaniel R. Kinsky ◽

William Mau ◽

David W. Sullivan ◽

Samuel J. Levy ◽

Evan A. Ruesch ◽

...

Keyword(s):

Calcium Imaging ◽

Hippocampal Neurons ◽

Spatial Information ◽

Single Photon ◽

Place Cells ◽

Dependent Activity ◽

Freely Moving ◽

Stable Trajectory ◽

Alternation Task

ABSTRACTTrajectory-dependent splitter neurons in the hippocampus encode information about a rodent’s prior trajectory during performance of a continuous alternation task. As such, they provide valuable information for supporting memory-guided behavior. Here, we employed single-photon calcium imaging in freely moving mice to investigate the emergence and fate of trajectory-dependent activity through learning and mastery of a continuous spatial alternation task. We found that the quality of trajectory-dependent information in hippocampal neurons correlated with task performance. We thus hypothesized that, due to their utility, splitter neurons would exhibit heightened stability. We found that splitter neurons were more likely to remain active and retained more consistent spatial information across multiple days than did place cells. Furthermore, we found that both splitter neurons and place cells emerged rapidly and maintained stable trajectory-dependent/spatial activity thereafter. Our results suggest that neurons with useful functional coding properties exhibit heightened stability to support memory guided behavior.

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Probing variability in a cognitive map using manifold inference from neural dynamics

10.1101/418939 ◽

2018 ◽

Cited By ~ 8

Author(s):

Ryan J. Low ◽

Sam Lewallen ◽

Dmitriy Aronov ◽

Rhino Nevers ◽

David W. Tank

Keyword(s):

Extra Dimension ◽

Network Architecture ◽

Hippocampal Neurons ◽

Temporal Dynamics ◽

Learning Algorithm ◽

Level Structure ◽

Cognitive Map ◽

Population Activity ◽

Behavioral Variables ◽

Behavioral Conditions

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task,1-3 and are thought to form a cognitive map of task-relevant variables.1,4,5 However, their activity varies over repeated behavioral conditions,6 such as different runs through the same position or repeated trials. Although widely observed across the brain,7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information.6,11-13 Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold—a space of possible network states. The manifold’s structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory,1,5,14 planning,12,15,16 and reinforcement learning.17,18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

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Hippocampal Activity Dynamics During Contextual Reward Association in Virtual Reality Place Conditioning

10.1101/545608 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sidney B. Williams ◽

Moises Arriaga ◽

William W. Post ◽

Akshata A. Korgaonkar ◽

Jose A. Morón ◽

...

Keyword(s):

Virtual Reality ◽

Drug Use ◽

Pyramidal Neurons ◽

Specific Activity ◽

Dorsal Hippocampus ◽

Place Conditioning ◽

Opioid Use ◽

Reward Seeking ◽

Seeking Behavior

ABSTRACTExposure to environmental contexts associated with drug use can induce cravings that promote continued use and/or relapse. Opioid abuse is marked by high relapse rates, suggesting that contextual memories formed during opioid use may be particularly strong. While it is known that reward-seeking behavior is controlled by the mesolimbic reward circuit, little is understood about how contextual memories are altered by drug use. The dorsal hippocampus (dHPC) is necessary for multiple types of contextual learning and the place-specific activity of CA1 place cells map out space in a given environment. Here we examined the neuronal representation of context as animals developed morphine-paired environmental associations using a conditioned place preference (CPP) paradigm. To investigate changes in the hippocampal encoding before, during, and after drug-pairing, we developed a virtual reality (VR) morphine CPP (Mor-CPP) paradigm and used in vivo two-photon calcium imaging to record the activity of CA1 pyramidal neurons. We found increased activity in rewarded contexts following real-time operant conditioning with water rewards, but not after Mor-CPP training, suggesting different neural encoding mechanisms for natural reinforcers and morphine.

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

10.1101/222166 ◽

2017 ◽

Cited By ~ 1

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

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Recurrent network model for learning goal-directed sequences through reverse replay

eLife ◽

10.7554/elife.34171 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 16

Author(s):

Tatsuya Haga ◽

Tomoki Fukai

Keyword(s):

Sequence Learning ◽

Spatial Navigation ◽

Recurrent Network ◽

Place Cells ◽

Spike Timing ◽

Directed Path ◽

Short Term ◽

Reward Seeking ◽

Sequential Activation ◽

First Time

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed path learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward synaptic pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric STDP in the co-presence of short-term synaptic depression or afterdepolarization. We demonstrate that significant biases are created in biologically realistic simulation settings, and this bias enables reverse replay to enhance goal-directed spatial memory on a W-maze. Further, we show that essentially the same mechanism works in a two-dimensional open field. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

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Recurrent network model for learning goal-directed sequences through reverse replay

10.1101/217422 ◽

2017 ◽

Author(s):

Tatsuya Haga ◽

Tomoki Fukai

Keyword(s):

Sequence Learning ◽

Spatial Navigation ◽

Recurrent Network ◽

Place Cells ◽

Spike Timing ◽

Short Term ◽

Reward Seeking ◽

Significant Bias ◽

Sequential Activation ◽

First Time

ABSTRACTReverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed pathway learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric, but not asymmetric, STDP in the co-presence of short-term synaptic plasticity. We demonstrate that a significant bias can be created in biologically realistic simulation settings, and that this bias enables reverse replay to enhance goal-directed spatial memory on a T-maze. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

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Calcium Imaging Reveals Fast Tuning Dynamics of Hippocampal Place Cells and CA1 Population Activity during Free Exploration Task in Mice

International Journal of Molecular Sciences ◽

10.3390/ijms23020638 ◽

2022 ◽

Vol 23 (2) ◽

pp. 638

Author(s):

Vladimir P. Sotskov ◽

Nikita A. Pospelov ◽

Viktor V. Plusnin ◽

Konstantin V. Anokhin

Keyword(s):

Neuronal Activity ◽

Calcium Imaging ◽

Viral Vector ◽

Place Cells ◽

Place Field ◽

Population Activity ◽

Novel Environment ◽

Mouse Hippocampus ◽

Place Fields

Hippocampal place cells are a well-known object in neuroscience, but their place field formation in the first moments of navigating in a novel environment remains an ill-defined process. To address these dynamics, we performed in vivo imaging of neuronal activity in the CA1 field of the mouse hippocampus using genetically encoded green calcium indicators, including the novel NCaMP7 and FGCaMP7, designed specifically for in vivo calcium imaging. Mice were injected with a viral vector encoding calcium sensor, head-mounted with an NVista HD miniscope, and allowed to explore a completely novel environment (circular track surrounded by visual cues) without any reinforcement stimuli, in order to avoid potential interference from reward-related behavior. First, we calculated the average time required for each CA1 cell to acquire its place field. We found that 25% of CA1 place fields were formed at the first arrival in the corresponding place, while the average tuning latency for all place fields in a novel environment equaled 247 s. After 24 h, when the environment was familiar to the animals, place fields formed faster, independent of retention of cognitive maps during this session. No cumulation of selectivity score was observed between these two sessions. Using dimensionality reduction, we demonstrated that the population activity of rapidly tuned CA1 place cells allowed the reconstruction of the geometry of the navigated circular maze; the distribution of reconstruction error between the mice was consistent with the distribution of the average place field selectivity score in them. Our data thus show that neuronal activity recorded with genetically encoded calcium sensors revealed fast behavior-dependent plasticity in the mouse hippocampus, resulting in the rapid formation of place fields and population activity that allowed the reconstruction of the geometry of the navigated maze.

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Subcircuits of deep and superficial CA1 place cells support efficient spatial coding across heterogeneous environments

10.1101/2020.04.17.047399 ◽

2020 ◽

Author(s):

Farnaz Sharif ◽

Behnam Tayebi ◽

György Buzsáki ◽

Sebastien Royer ◽

Antonio Fernandez-Ruiz

Keyword(s):

Firing Rate ◽

Hippocampal Neurons ◽

Open Space ◽

Place Cells ◽

Cognitive Map ◽

Heterogeneous Environments ◽

Rate Code ◽

Spatial Coding ◽

Local Inhibition ◽

Phase Code

AbstractThe hippocampus is thought to guide navigation by forming a cognitive map of space. However, the behavioral demands for such a map can vary depending on particular features of a given environment. For example, an environment rich in cues may require a finer resolution map than an open space. It is unclear how the hippocampal cognitive map adjusts to meet these distinct behavioral demands. To address this issue, we examined the spatial coding characteristics of hippocampal neurons in mice and rats navigating different environments. We found that CA1 place cells located in the superficial sublayer were more active in cue-poor environments, and preferentially used a firing rate code driven by intra-hippocampal inputs. In contrast, place cells located in the deep sublayer were more active in cue-rich environments and expressed a phase code driven by entorhinal inputs. Switching between these two spatial coding modes was supported by the interaction between excitatory gamma inputs and local inhibition.

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