Memory trace replay:The shaping of memory consolidation by neuromodulation

The consolidation of memories for places and events is thought to rely, at the network level, on the replay of spatially tuned neuronal firing patterns representing discrete places and spatial trajectories. This occurs in the hippocampal-entorhinal circuit during sharp wave ripple events (SWRs) that occur during sleep or rest. Here, we review theoretical models of lingering place cell excitability and behaviorally induced synaptic plasticity within cell assemblies to explain which sequences or places are replayed. We further provide new insights into how fluctuations in cholinergic tone during different behavioral states might shape the direction of replay and how dopaminergic release in response to novelty or reward can modulate which cell assemblies are replayed

Memory capacity in the hippocampus

Neural assemblies in hippocampus encode positions. During rest, the hippocam- pus replays sequences of neural activity seen during awake behavior. This replay is linked to memory consolidation and mental exploration of the environment. Re- current networks can be used to model the replay of sequential activity. Multiple sequences can be stored in the synaptic connections. To achieve a high mem- ory capacity, recurrent networks require a pattern separation mechanism. Such a mechanism is global remapping, observed in place cell populations. A place cell fires at a particular position of an environment and is silent elsewhere. Multiple place cells usually cover an environment with their firing fields. Small changes in the environment or context of a behavioral task can cause global remapping, i.e. profound changes in place cell firing fields. Global remapping causes some cells to cease firing, other silent cells to gain a place field, and other place cells to move their firing field and change their peak firing rate. The effect is strong enough to make global remapping a viable pattern separation mechanism. We model two mechanisms that improve the memory capacity of recurrent net- works. The effect of inhibition on replay in a recurrent network is modeled using binary neurons and binary synapses. A mean field approximation is used to de- termine the optimal parameters for the inhibitory neuron population. Numerical simulations of the full model were carried out to verify the predictions of the mean field model. A second model analyzes a hypothesized global remapping mecha- nism, in which grid cell firing is used as feed forward input to place cells. Grid cells have multiple firing fields in the same environment, arranged in a hexagonal grid. Grid cells can be used in a model as feed forward inputs to place cells to produce place fields. In these grid-to-place cell models, shifts in the grid cell firing patterns cause remapping in the place cell population. We analyze the capacity of such a system to create sets of separated patterns, i.e. how many different spatial codes can be generated. The limiting factor are the synapses connecting grid cells to place cells. To assess their capacity, we produce different place codes in place and grid cell populations, by shuffling place field positions and shifting grid fields of grid cells. Then we use Hebbian learning to increase the synaptic weights be- tween grid and place cells for each set of grid and place code. The capacity limit is reached when synaptic interference makes it impossible to produce a place code with sufficient spatial acuity from grid cell firing. Additionally, it is desired to also maintain the place fields compact, or sparse if seen from a coding standpoint. Of course, as more environments are stored, the sparseness is lost. Interestingly, place cells lose the sparseness of their firing fields much earlier than their spatial acuity. For the sequence replay model we are able to increase capacity in a simulated recurrent network by including an inhibitory population. We show that even in this more complicated case, capacity is improved. We observe oscillations in the average activity of both excitatory and inhibitory neuron populations. The oscillations get stronger at the capacity limit. In addition, at the capacity limit, rather than observing a sudden failure of replay, we find sequences are replayed transiently for a couple of time steps before failing. Analyzing the remapping model, we find that, as we store more spatial codes in the synapses, first the sparseness of place fields is lost. Only later do we observe a decay in spatial acuity of the code. We found two ways to maintain sparse place fields while achieving a high capacity: inhibition between place cells, and partitioning the place cell population so that learning affects only a small fraction of them in each environment. We present scaling predictions that suggest that hundreds of thousands of spatial codes can be produced by this pattern separation mechanism. The effect inhibition has on the replay model is two-fold. Capacity is increased, and the graceful transition from full replay to failure allows for higher capacities when using short sequences. Additional mechanisms not explored in this model could be at work to concatenate these short sequences, or could perform more complex operations on them. The interplay of excitatory and inhibitory populations gives rise to oscillations, which are strongest at the capacity limit. The oscillation draws a picture of how a memory mechanism can cause hippocampal oscillations as observed in experiments. In the remapping model we showed that sparseness of place cell firing is constraining the capacity of this pattern separation mechanism. Grid codes outperform place codes regarding spatial acuity, as shown in Mathis et al. (2012). Our model shows that the grid-to-place transformation is not harnessing the full spatial information from the grid code in order to maintain sparse place fields. This suggests that the two codes are independent, and communication between the areas might be mostly for synchronization. High spatial acuity seems to be a specialization of the grid code, while the place code is more suitable for memory tasks. In a detailed model of hippocampal replay we show that feedback inhibition can increase the number of sequences that can be replayed. The effect of inhibition on capacity is determined using a meanfield model, and the results are verified with numerical simulations of the full network. Transient replay is found at the capacity limit, accompanied by oscillations that resemble sharp wave ripples in hippocampus. In a second model Hippocampal replay of neuronal activity is linked to memory consolidation and mental exploration. Furthermore, replay is a potential neural correlate of episodic memory. To model hippocampal sequence replay, recurrent neural networks are used. Memory capacity of such networks is of great interest to determine their biological feasibility. And additionally, any mechanism that improves capacity has explanatory power. We investigate two such mechanisms. The first mechanism to improve capacity is global, unspecific feedback inhibition for the recurrent network. In a simplified meanfield model we show that capacity is indeed improved. The second mechanism that increases memory capacity is pattern separation. In the spatial context of hippocampal place cell firing, global remapping is one way to achieve pattern separation. Changes in the environment or context of a task cause global remapping. During global remapping, place cell firing changes in unpredictable ways: cells shift their place fields, or fully cease firing, and formerly silent cells acquire place fields. Global remapping can be triggered by subtle changes in grid cells that give feed-forward inputs to hippocampal place cells. We investigate the capacity of the underlying synaptic connections, defined as the number of different environments that can be represented at a given spatial acuity. We find two essential conditions to achieve a high capacity and sparse place fields: inhibition between place cells, and partitioning the place cell population so that learning affects only a small fraction of them in each environments. We also find that sparsity of place fields is the constraining factor of the model rather than spatial acuity. Since the hippocampal place code is sparse, we conclude that the hippocampus does not fully harness the spatial information available in the grid code. The two codes of space might thus serve different purposes

Dynamics and function of a CA1 model of the hippocampus during theta and ripples

The hippocampus is known to be involved in spatial learning in rats. Spatial learning involves the encoding and replay of temporally sequenced spatial information. Temporally sequenced spatial memories are encoded and replayed by the firing rate and phase of pyramidal cells and inhibitory interneurons with respect to ongoing network oscillations (theta and ripples). Understanding how the different hippocampal neuronal classes interact during these encoding and replay processes is of great importance. A computational model of the CA1 microcircuit [3], [4], [5] that uses biophysical representations of the major cell types, including pyramidal cells and four types of inhibitory interneurons is extended to address: (1) How are the encoding and replay (forward and reverse) of behavioural place sequences controlled in the CA1 microcircuit during theta and ripples? and (2) What roles do the various types of inhibitory interneurons play in these processes

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated (‘replayed’), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated

Hippocampal reactivation of random trajectories resembling Brownian Diffusion

Hippocampal activity patterns representing movement trajectories are reactivated in immobility and sleep periods, a process associated with memory recall, consolidation, and decision making. It is thought that only fixed, behaviorally relevant patterns can be reactivated, which are stored across hippocampal synaptic connections. To test whether some generalized rules govern reactivation, we examined trajectory reactivation following non-stereotypical exploration of familiar open-field environments. We found that random trajectories of varying lengths and timescales were reactivated, resembling that of Brownian motion of particles. The animals’ behavioral trajectory did not follow Brownian diffusion demonstrating that the exact behavioral experience is not reactivated. Therefore, hippocampal circuits are able to generate random trajectories of any recently active map by following diffusion dynamics. This ability of hippocampal circuits to generate representations of all behavioral outcome combinations, experienced or not, may underlie a wide variety of hippocampal-dependent cognitive functions such as learning, generalization, and planning

Optogenetic Interrogation of Hippocampal Circuit Stabilization

Understanding the response of excitatory and inhibitory populations to varying input is vital to understanding how a brain region transforms information. Optogenetics - the combined use of optics and genetics to control the activity of proteins, provides neuroscientists with a tool to interrogate neuronal circuits with high spatio-temporal resolution and targeted cell specificity. This thesis examines the effects of optogenetic manipulations on hippocampal circuit responses. The hippocampus is a structure required for the formation and retention of episodic memories and is comprised of anatomically distinct subregions including cornu ammonis 3 (CA3) and cornu ammonis 1 (CA1). Both regions, despite differences in local circuitry, contain excitatory cells that fire in a spatially selective manner as an animal explores an environment. Based on these differences in circuitry, studies have proposed different computational roles of each region. In order to gain insight into how distinct hippocampal networks respond to light-induced external drive we measured the responses of neurons in CA1 and CA3 to optogenetic perturbation. To date, no work has explored the differences in CA3 and CA1 network responses to acute optogenetic manipulation of the circuits. This thesis uses a combined approach of optogenetic perturbation with simultaneous high-density electrophysiological recordings to answer two fundamental questions related to the computational roles of region CA3 and CA1. The first question asks, what role does region CA3 play in shaping spiking activity in downstream CA1? To address this question, electrophysiological recordings of CA1 were combined with optogenetic silencing of CA3 using the light-driven proton pump ArchT in both freely moving and urethane-anesthetized rodents. Since the major projection from CA3 to CA1 is excitatory, our initial hypothesis predicted an overall decrease in CA1 activity due to the expected decrease in excitatory drive from CA3. Surprisingly, suppression of CA3 resulted in a robust and consistent increase in interneuron firing in CA1 (awake: 68\% increase, 10\% decrease, 22\% no response n = 87, anesthetized: 59\% increase, 26\% decrease, 15\% no response, n = 96). The second question asks, how do excitatory and inhibitory populations in CA3 and CA1 differentially respond to incoming signals? To address this question, integrated opto-electrode devices were used to simultaneously manipulate and measure the responses of CA3 and CA1 circuits to perturbations. We found that focal suppression of CA3 driven by both ArchT and the light-driven chloride channel stGtACR2 resulted in a paradoxical increase in firing of both inhibitory and excitatory cell at all distances from the site of photoinhibition. In contrast, CA1 cells responded to focal photoinhibition by showing nearly 100\% decrease in cell response at the site of illumination. Paradoxical increases in firing in response to external inhibitory input to interneurons can be a feature of networks with highly-recurrent excitatory connections that are unstable in the absence of inhibition (ISNs: inhibitory-stabilized networks. Broad (600 $\mu$m diameter) photoinhibition was applied and network responses were measured over a range of laser intensities to test whether differences in responses between CA3 and CA1 can be attributed to CA3 operating in an ISN-regime. Paradoxical increases in pyramidal cell or interneuron firing were not observed when inhibitory opsins were expressed in both pyramidal cells and interneurons. When external input was restricted to interneurons, CA1, and to a smaller extent, CA3 showed increased firing in response to varying intensities of photoinhibition, suggesting both CA1 and CA3 operate as ISNs. Taken together, these results indicate that perturbations of neuronal activity can produce paradoxical effects that affect both local and connected regions. The emerging patterns depend on the detailed interactions between excitatory and inhibitory subpopulations within a region, and can be broadly explained by network models of global stabilization through inhibition. Our results further highlight the need for simultaneous monitoring of cellular responses when using optogenetics or other manipulations that alter neuronal activities

Doctor of Philosophy

dissertationHippocampal network oscillations are important for learning and memory. Theta rhythms are involved in attention, navigation, and memory encoding, whereas sharp wave-ripple complexes (ripples) are involved in memory consolidation. Cholinergic neurons in the medial septum-diagonal band of Broca (MS-DB) influence both types of hippocampal oscillations, promoting theta rhythms and suppressing ripples. They also receive frequency-dependent hyperpolarizing feedback from hippocamposeptal connections, potentially affecting their role as neuromodulators in the septohippocampal circuit. However, little is known about how the integration properties of cholinergic MS-DB neurons change with hyperpolarization. By potentially altering firing behavior in cholinergic neurons, hyperpolarizing feedback from the hippocampal neurons may, in turn, change hippocampal network activity. To study how hyperpolarizing inputs change in membrane integration properties, we used whole-cell patch-clamp recordings targeting genetically labeled, choline acetyltransferase-positive neurons in mouse medial septal brain slices. Hyperpolarization of cholinergic MS-DB neurons resulted in a long-lasting decrease in spike firing rate and input-output gain. Additionally, voltage-clamp measures implicated a slowly inactivating, 4-AP-insensitive, outward K+ conductance. Using a conductance-based model of cholinergic MS-DB neurons, we show that the ability of this conductance to modulate firing rate and gain depends on the expression of an experimentally verified shallow intrinsic spike frequency-voltage relationship. Finally, we show that cholinergic suppression of hippocampal ripples can be achieved through an imbalance in drive, caused by cholinergic modulation, to hippocampal excitatory and inhibitory neurons. Together, these findings show possible mechanisms through which cholinergic MS-DB neurons may both influence and be influenced by hippocampal rhythms

Deciphering the Firing Patterns of Hippocampal Neurons During Sharp-Wave Ripples

The hippocampus is essential for learning and memory. Neurons in the rat hippocampus selectively fire when the animal is at specific locations - place fields - within an environment. Place fields corresponding to such place cells tile the entire environment, forming a stable spatial map supporting navigation and planning. Remarkably, the same place cells reactivate together outside of their place fields and in coincidence with sharp-wave ripples (SWRs) - dominant electrical field oscillations (150-250 Hz) in the hippocampus. These offline SWR events frequently occur during quiet wake periods in the middle of exploration and the follow-up slow-wave sleep and are associated with spatial memory performance and stabilization of spatial maps. Therefore, deciphering the firing patterns during these events is essential to understanding offline memory processing.I provide two novel methods to analyze the SWRs firing patterns in this dissertation project. The first method uses hidden Markov models (HMM), in which I model the dynamics of neural activity during SWRs in terms of transitions between distinct states of neuronal ensemble activity. This method detects consistent temporal structures over many instances of SWRs and, in contrast to standard approaches, relaxes the dependence on positional data during the behavior to interpret temporal patterns during SWRs. To validate this method, I applied the method to quiet wake SWRs. In a simple spatial memory task in which the animal ran on a linear track or in an open arena, the individual states corresponded to the activation of distinct group of neurons with inter-state transitions that resembled the animal’s trajectories during the exploration. In other words, this method enabled us to identify the topology and spatial map of the explored environment by dissecting the firings occurring during the quiescence periods’ SWRs. This result indicated that downstream brain regions may rely only on SWRs to uncover hippocampal code as a substrate for memory processing. I developed a second analysis method based on the principles of Bayesian learning. This method enabled us to track the spatial tunings over the sleep following exploration of an environment by taking neurons’ place fields in the environment as the prior belief and updating it using dynamic ensemble firing patterns unfolding over time. This method introduces a neuronal-ensemble-based approach that calculates tunings to the position encoded by ensemble firings during sleep rather than the animal’s actual position during exploration. When I applied this method to several datasets, I found that during the early slow-wave sleep after an experience, but not during late hours of sleep or sleep before the exploration, the spatial tunings highly resembled the place fields on the track. Furthermore, the fidelity of the spatial tunings to the place fields predicted the place fields’ stability when the animal was re-exposed to the same environment after ~ 9h. Moreover, even for neurons with shifted place fields during re-exposure, the spatial tunings during early sleep were predictive of the place fields during the re-exposure. These results indicated that early sleep actively maintains or retunes the place fields of neurons, explaining the representational drift of place fields across multiple exposures

Physiological mechanisms of hippocampal memory processing : experiments and applied adaptive filtering

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.Includes bibliographical references (p. 144-156).The hippocampus is necessary for the formation and storage of episodic memory, however, the computations within and between hippocampal subregions (CA1, CA3, and dentate gyrus) that mediate these memory processing functions are not completely understood. We investigate by recording in the hippocampal subregions as rats execute an augmented linear track task. From these recordings, we construct ensemble rate representations using a point process adaptive filter to characterize single-unit activity from each subregion. We compared the dynamics of these rate representations by computing average max rate and average rate modulation during different experimental epochs and on different segments of the track. We found that the representations in CA3 were modulated most when compared to CAl and DG during the first 5 minutes of experience. With more experience, we found the average rate modulation decreased gradually across all areas and converged to values that were not statistically different. These results suggest a specialized role for CA3 during initial context acquisition, and suggest that rate modulation becomes coherent across HPC subregions after familiarization. Information transfer between the hippocampus and neocortex is important for the consolidation of spatial and episodic memory. This process of information transfer is referred to as memory consolidation and may be mediated by a phenomena called "replay." We know that the process of replay is associated with a rise in multi-unit activity and the presence of ripples (100-250 Hz oscillations lasting from 75ms to 100ms) in CAl. Because ripples result from the same circuits as replay activity, the features of the ripple may allow us to deduce the mechanisms for replay induction and the nature of information transmitted during replay events.(cont.) Because ripples are relatively short events, analytical methods with limited temporal-spectral resolution are unable to fully characterize all the structure of ripples. In the thesis, we develop a framework for characterizing, classifying, and detecting ripples based on instantaneous frequency and instantaneous frequency modulation. The framework uses an autoregressive model for spectral-temporal analysis in combination with a Kalman filter for sample-to-sample estimates of frequency parameters. We show that the filter is flexible in the degree of smoothing as well as robust in the estimation of frequency. We demonstrate that under the proposed framework ripples can be classified based on high or low frequency, and positive or negative frequency modulation; can combine amplitude and frequency information for selective detection of ripple events; and can be used to determine the number of ripples participating in "long ripple" events.by David P. Nguyen.Ph.D