Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex.

Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given behavioral significance and how cortical networks might encode this information. We first demonstrate that rats can associate different speed patterns on the same trajectory with distinct behavioral choices. In this novel experimental paradigm, rats follow a small baited robot in a large megaspace environment where the rat's speed is precisely controlled by the robot's speed. Based on this proof of concept and research showing that recurrent reservoir networks are ideal for representing spatio-temporal structures, we then test reservoir networks in simulated navigation contexts and demonstrate they can discriminate between traversals of the same path with identical durations but different speed profiles. We then test the networks in an embodied robotic setup, where we use place cell representations from physically navigating robots as input and again successfully discriminate between traversals. To demonstrate that this capability is inherent to recurrent networks, we compared the model against simple linear integrators. Interestingly, although the linear integrators could also perform the speed profile discrimination, a clear difference emerged when examining information coding in both models. Reservoir neurons displayed a form of statistical mixed selectivity as a complex interaction between spatial location and speed that was not as abundant in the linear integrators. This mixed selectivity is characteristic of cortex and reservoirs and allows us to generate specific predictions about the neural activity that will be recorded in rat cortex in future experiments.

Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation.

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call "snippets". These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as 'reservoir computing' to model prefrontal cortex (PFC) in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model consolidates snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to "learn" trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.

Conjunctive reward-place coding properties of dorsal distal CA1 hippocampus cells.

Autonomous motivated spatial navigation in animals or robots requires the association between spatial location and value. Hippocampal place cells are involved in goal-directed spatial navigation and the consolidation of spatial memories. Recently, Gauthier and Tank (Neuron 99(1):179-193, 2018. https://doi.org/10.1016/j.neuron.2018.06.008) have identified a subpopulation of hippocampal cells selectively activated in relation to rewarded goals. However, the relationship between these cells' spiking activity and goal representation remains elusive. We analyzed data from experiments in which rats underwent five consecutive tasks in which reward locations and spatial context were manipulated. We found CA1 populations with properties continuously ranging from place cells to reward cells. Specifically, we found typical place cells insensitive to reward locations, reward cells that only fired at correct rewarded feeders in each task regardless of context, and "hybrid cells" that responded to spatial locations and change of reward locations. Reward cells responded mostly to the reward delivery rather than to its expectation. In addition, we found a small group of neurons that transitioned between place and reward cells properties within the 5-task session. We conclude that some pyramidal cells (if not all) integrate both spatial and reward inputs to various degrees. These results provide insights into the integrative coding properties of CA1 pyramidal cells, focusing on their abilities to carry both spatial and reward information in a mixed and plastic manner. This conjunctive coding property prompts a re-thinking of current computational models of spatial navigation in which hippocampal spatial and subcortical value representations are independent.

A computational model for spatial cognition combining dorsal and ventral hippocampal place field maps: multiscale navigation.

Classic studies have shown that place cells are organized along the dorsoventral axis of the hippocampus according to their field size, with dorsal hippocampal place cells having smaller field sizes than ventral place cells. Studies have also suggested that dorsal place cells are primarily involved in spatial navigation, while ventral place cells are primarily involved in context and emotional encoding. Additionally, recent work has shown that the entire longitudinal axis of the hippocampus may be involved in navigation. Based on the latter, in this paper we present a spatial cognition reinforcement learning model inspired by the multiscale organization of the dorsal-ventral axis of the hippocampus. The model analyzes possible benefits of a multiscale architecture in terms of the learning speed, the path optimality, and the number of cells in the context of spatial navigation. The model is evaluated in a goal-oriented task where simulated rats need to learn a path to the goal from multiple starting locations in various open-field maze configurations. The results show that smaller scales of representation are useful for improving path optimality, whereas larger scales are useful for reducing learning time and the number of cells required. The results also show that combining scales can enhance the performance of the multiscale model, with a trade-off between path optimality and learning time.

The ventral hippocampus is involved in multi-goal obstacle-rich spatial navigation.

A large body of evidence shows that the hippocampus is necessary for successful spatial navigation. Various studies have shown anatomical and functional differences between the dorsal (DHC) and ventral (VHC) portions of this structure. The DHC is primarily involved in spatial navigation and contains cells with small place fields. The VHC is primarily involved in context and emotional encoding contains cells with large place fields and receives major projections from the medial prefrontal cortex. In the past, spatial navigation experiments have used relatively simple tasks that may not have required a strong coordination along the dorsoventral hippocampal axis. In this study, we tested the hypothesis that the DHC and VHC may be critical for goal-directed navigation in obstacle-rich environments. We used a learning task in which animals memorize the location of a set of rewarded feeders, and recall these locations in the presence of small or large obstacles. We report that bilateral DHC or VHC inactivation impaired spatial navigation in both large and small obstacle conditions. Importantly, this impairment did not result from a deficit in the spatial memory for the set of feeders (i.e., recognition of the goal locations) because DHC or VHC inactivation did not affect recall performance when there was no obstacle on the maze. We also show that the behavioral performance of the animals was correlated with several measures of maze complexity and that these correlations were significantly affected by inactivation only in the large object condition. These results suggest that as the complexity of the environment increases, both DHC and VHC are required for spatial navigation.

Hippocampal CA1 Ripples as Inhibitory Transients.

Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep. Neurons active during behavior reactivate in both structures during sleep, in conjunction with characteristic brain oscillations that may form the neural substrate of memory consolidation. In the hippocampus, replay occurs within sharp wave-ripples: short bouts of high-frequency activity in area CA1 caused by excitatory activation from area CA3. In this work, we develop a computational model of ripple generation, motivated by in vivo rat data showing that ripples have a broad frequency distribution, exponential inter-arrival times and yet highly non-variable durations. Our study predicts that ripples are not persistent oscillations but result from a transient network behavior, induced by input from CA3, in which the high frequency synchronous firing of perisomatic interneurons does not depend on the time scale of synaptic inhibition. We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration. Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

Beneficial effects of chronic oxytocin administration and social co-housing in a rodent model of post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is in part due to a deficit in memory consolidation and extinction. Oxytocin (OXT) has anxiolytic effects and promotes prosocial behaviors in both rodents and humans, and evidence suggests that it plays a role in memory consolidation. We studied the effects of administered OXT and social co-housing in a rodent model of PTSD. Acute OXT yielded a short-term increase in the recall of the traumatic memory if administered immediately after trauma. Low doses of OXT delivered chronically had a cumulating anxiolytic effect that became apparent after 4 days and persisted. Repeated injections of OXT after short re-exposures to the trauma apparatus yielded a long-term reduction in anxiety. Co-housing with naive nonshocked animals decreased the memory of the traumatic context compared with single-housed animals. In the long term, these animals showed less thigmotaxis and increased interest in novel objects, and a low OXT plasma level. Co-housed PTSD animals showed an increase in risk-taking behavior. These results suggest beneficial effects of OXT if administered chronically through increases in memory consolidation after re-exposure to a safe trauma context. We also show differences between the benefits of social co-housing with naive rats and co-housing with other shocked animals on trauma-induced long-term anxiety.

Offline reactivation of experience-dependent neuronal firing patterns in the rat ventral tegmental area.

In a rest period immediately after a task, neurons in the hippocampus, neocortex, and striatum exhibit spatiotemporal correlation patterns resembling those observed during the task. This reactivation has been proposed as a neurophysiological substrate for memory consolidation. We provide new evidence that rodent ventral tegmental area (VTA) neurons are selective for different types of food stimuli and that stimulus-sensitive neurons strongly reactivate during the rest period following a task that involved those stimuli. Reactivation occurred primarily during slow wave sleep and during quiet awakeness. In these experiments, VTA reactivation patterns were uncompressed and occurred at the firing rate level, rather than on a spike-to-spike basis. Mildly aversive stimuli were reactivated more often than positive ones. The VTA is a pivotal structure involved in the perception and prediction of reward and stimulus salience and is a key neuromodulatory system involved in synaptic plasticity. These results suggest new ways in which dopaminergic signals could contribute to the biophysical mechanisms of selective, system-wide, memory consolidation, and reconsolidation during sleep.

Contextual reminders fail to trigger memory reconsolidation in aged rats and aged humans.

There is strong evidence that hippocampal memory returns to a labile state upon reactivation, initiating a reconsolidation process that restabilizes it and allows for its updating. Normal aging is associated with deficits in episodic memory processes. However, the effects of aging on memory reconsolidation and its neural substrate remain largely unknown, and an animal model is lacking. In this study we investigated the effects of aging on context-dependent reconsolidation using an episodic set-learning task in humans and an analogous set-learning spatial task in rats. In both tasks, young and older subjects learned a set of objects (humans) or feeder locations (rats; Set 1) in Context A on Day 1. On Day 2, a different set (Set 2) was learned in either Context A (Reminder condition) or Context B (No Reminder condition). On Day 3, subjects were instructed (humans) or cued (rats) to recall Set 1. Young rats and humans in the Reminder condition falsely recalled significantly more items from Set 2 than those in the No Reminder condition, suggesting that the reminder context triggered a reactivation of Set 1 on Day 2 and allowed the integration of Set 2 items into Set 1. In both species, older subjects displayed a different pattern of results than young subjects. In aged rats, there was no difference between conditions in the level of falsely recalled Set 2 items (intrusions). Older humans in the No Reminder condition made significantly more intrusions than those in the Reminder condition. Follow-up control experiments in aged rats suggested that intrusions in older animals reflected general interference, independent of context manipulations. We conclude that contextual reminders are not sufficient to trigger memory updating in aged rats or aged humans, unlike in younger individuals. Future studies using this animal model should further our understanding of the role of the hippocampus in memory maintenance and updating during normal aging.

Influence of external inputs and asymmetry of connections on information-geometric measures involving up to ten neuronal interactions.

The investigation of neural interactions is crucial for understanding information processing in the brain. Recently an analysis method based on information geometry (IG) has gained increased attention, and the property of the pairwise IG measure has been studied extensively in relation to the two-neuron interaction. However, little is known about the property of IG measures involving more neuronal interactions. In this study, we systematically investigated the influence of external inputs and the asymmetry of connections on the IG measures in cases ranging from 1-neuron to 10-neuron interactions. First, the analytical relationship between the IG measures and external inputs was derived for a network of 10 neurons with uniform connections. Our results confirmed that the single and pairwise IG measures were good estimators of the mean background input and of the sum of the connection weights, respectively. For the IG measures involving 3 to 10 neuronal interactions, we found that the influence of external inputs was highly nonlinear. Second, by computer simulation, we extended our analytical results to asymmetric connections. For a network of 10 neurons, the simulation showed that the behavior of the IG measures in relation to external inputs was similar to the analytical solution obtained for a uniformly connected network. When the network size was increased to 1000 neurons, the influence of external inputs almost disappeared. This result suggests that all IG measures from 1-neuron to 10-neuron interactions are robust against the influence of external inputs. In addition, we investigated how the strength of asymmetry influenced the IG measures. Computer simulation of a 1000-neuron network showed that all the IG measures were robust against the modulation of the asymmetry of connections. Our results provide further support for an information-geometric approach and will provide useful insights when these IG measures are applied to real experimental spike data.

Spatial scale and place field stability in a grid-to-place cell model of the dorsoventral axis of the hippocampus.

The rodent hippocampus and entorhinal cortex contain spatially modulated cells that serve as the basis for spatial coding. Both medial entorhinal grid cells and hippocampal place cells have been shown to encode spatial information across multiple spatial scales that increase along the dorsoventral axis of these structures. Place cells near the dorsal pole possess small, stable, and spatially selective firing fields, while ventral cells have larger, less stable, and less spatially selective firing fields. One possible explanation for these dorsoventral changes in place field properties is that they arise as a result of similar dorsoventral differences in the properties of the grid cell inputs to place cells. Here, we test the alternative hypothesis that dorsoventral place field differences are due to higher amounts of nonspatial inputs to ventral hippocampal cells. We use a computational model of the entorhinal-hippocampal network to assess the relative contributions of grid scale and nonspatial inputs in determining place field size and stability. In addition, we assess the consequences of grid node firing rate heterogeneity on place field stability. Our results suggest that dorsoventral differences in place cell properties can be better explained by changes in the amount of nonspatial inputs, rather than by changes in the scale of grid cell inputs, and that grid node heterogeneity may have important functional consequences. The observed gradient in field size may reflect a shift from processing primarily spatial information in the dorsal hippocampus to processing more nonspatial, contextual, and emotional information near the ventral hippocampus.

Regulation of spike timing in visual cortical circuits.

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.

Multiple spike time patterns occur at bifurcation points of membrane potential dynamics.

The response of a neuron to repeated somatic fluctuating current injections in vitro can elicit a reliable and precisely timed sequence of action potentials. The set of responses obtained across trials can also be interpreted as the response of an ensemble of similar neurons receiving the same input, with the precise spike times representing synchronous volleys that would be effective in driving postsynaptic neurons. To study the reproducibility of the output spike times for different conditions that might occur in vivo, we somatically injected aperiodic current waveforms into cortical neurons in vitro and systematically varied the amplitude and DC offset of the fluctuations. As the amplitude of the fluctuations was increased, reliability increased and the spike times remained stable over a wide range of values. However, at specific values called bifurcation points, large shifts in the spike times were obtained in response to small changes in the stimulus, resulting in multiple spike patterns that were revealed using an unsupervised classification method. Increasing the DC offset, which mimicked an overall increase in network background activity, also revealed bifurcation points and increased the reliability. Furthermore, the spike times shifted earlier with increasing offset. Although the reliability was reduced at bifurcation points, a theoretical analysis showed that the information about the stimulus time course was increased because each of the spike time patterns contained different information about the input.

Remaking memories: reconsolidation updates positively motivated spatial memory in rats.

There is strong evidence that reactivation of a memory returns it to a labile state, initiating a restabilization process termed reconsolidation, which allows for updating of the memory. In this study we investigated reactivation-dependent updating using a new positively motivated spatial task in rodents that was designed specifically to model a human list-learning paradigm. On Day 1, rats were trained to run to three feeders (List 1) for rewards. On Day 2, rats were trained to run to three different feeders (List 2) in either the same (Reminder condition) or a different (No Reminder condition) experimental context than on Day 1. On Day 3, rats were cued to recall List 1. Rats in the Reminder condition made significantly more visits to List 2 feeders (intrusions) during List 1 recall than rats in the No Reminder condition, indicating that the reminder triggered reactivation and allowed integration of List 2 items into List 1. This reminder effect was selective for the reactivated List 1 memory, as no intrusions occurred when List 2 was recalled on Day 3. No intrusions occurred when retrieval took place in a different context from the one used at encoding, indicating that the expression of the updated memory is dependent upon the retrieval context. Finally, the level of intrusions was highest when retrieval took place immediately after List 2 learning, and generally declined when retrieval occurred 1-4 h later, indicating that the List 2 memory competed with short-term retrieval of List 1. These results demonstrate the dynamic nature of memory over time and the impact of environmental context at different stages of memory processing.

The effects of time horizon and guided choices on explore-exploit decisions in rodents.

Humans and animals have to balance the need for exploring new options with exploiting known options that yield good outcomes. This tradeoff is known as the explore-exploit dilemma. To better understand the neural mechanisms underlying how humans and animals address the explore-exploit dilemma, a good animal behavioral model is critical. Most previous rodents explore-exploit studies used ethologically unrealistic operant boxes and reversal learning paradigms in which the decision to abandon a bad option is confounded by the need for exploring a novel option for information collection, making it difficult to separate different drives and heuristics for exploration. In this study, we investigated how rodents make explore-exploit decisions using a spatial navigation horizon task (Wilson et al., 2014) adapted to rats to address the above limitations. We compared the rats' performance to that of humans using identical measures. We showed that rats use prior information to effectively guide exploration. In addition, rats use information-driven directed exploration like humans, but the extent to which they explore has the opposite dependance on time horizon than humans. Moreover, we found that free choices and guided choices have different influences on exploration in rodents, a finding that has not yet been tested in humans. This study reveals that the explore-exploit spatial behavior of rats is more complex than previously thought. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Phase precession and variable spatial scaling in a periodic attractor map model of medial entorhinal grid cells with realistic after-spike dynamics.

We present a model that describes the generation of the spatial (grid fields) and temporal (phase precession) properties of medial entorhinal cortical (MEC) neurons by combining network and intrinsic cellular properties. The model incorporates network architecture derived from earlier attractor map models, and is implemented in 1D for simplicity. Periodic driving of conjunctive (position × head-direction) layer-III MEC cells at theta frequency with intensity proportional to the rat's speed, moves an 'activity bump' forward in network space at a corresponding speed. The addition of prolonged excitatory currents and simple after-spike dynamics resembling those observed in MEC stellate cells (for which new data are presented) accounts for both phase precession and the change in scale of grid fields along the dorso-ventral axis of MEC. Phase precession in the model depends on both synaptic connectivity and intrinsic currents, each of which drive neural spiking either during entry into, or during exit out of a grid field. Thus, the model predicts that the slope of phase precession changes between entry into and exit out of the field. The model also exhibits independent variation in grid spatial period and grid field size, which suggests possible experimental tests of the model.