Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function

Hippocampal storage and recall of neocortical "What"-"Where" representations

A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

Serotonergic neuromodulation of synaptic plasticity

Synaptic plasticity constitutes a fundamental process in the reorganization of neural networks that underlie memory, cognition, emotional responses, and behavioral planning. At the core of this phenomenon lie Hebbian mechanisms, wherein frequent synaptic stimulation induces long-term potentiation (LTP), while less activation leads to long-term depression (LTD). The synaptic reorganization of neuronal networks is regulated by serotonin (5-HT), a neuromodulator capable of modify synaptic plasticity to appropriately respond to mental and behavioral states, such as alertness, attention, concentration, motivation, and mood. Lately, understanding the serotonergic Neuromodulation of synaptic plasticity has become imperative for unraveling its impact on cognitive, emotional, and behavioral functions. Through a comparative analysis across three main forebrain structures-the hippocampus, amygdala, and prefrontal cortex, this review discusses the actions of 5-HT on synaptic plasticity, offering insights into its role as a neuromodulator involved in emotional and cognitive functions. By distinguishing between plastic and metaplastic effects, we provide a comprehensive overview about the mechanisms of 5-HT neuromodulation of synaptic plasticity and associated functions across different brain regions.

Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.

Computational models can distinguish the contribution from different mechanisms to familiarity recognition

Familiarity is the strange feeling of knowing that something has already been seen in our past. Over the past decades, several attempts have been made to model familiarity using artificial neural networks. Recently, two learning algorithms successfully reproduced the functioning of the perirhinal cortex, a key structure involved during familiarity: Hebbian and anti-Hebbian learning. However, performance of these learning rules is very different from one to another thus raising the question of their complementarity. In this work, we designed two distinct computational models that combined Deep Learning and a Hebbian learning rule to reproduce familiarity on natural images, the Hebbian model and the anti-Hebbian model, respectively. We compared the performance of both models during different simulations to highlight the inner functioning of both learning rules. We showed that the anti-Hebbian model fits human behavioral data whereas the Hebbian model fails to fit the data under large training set sizes. Besides, we observed that only our Hebbian model is highly sensitive to homogeneity between images. Taken together, we interpreted these results considering the distinction between absolute and relative familiarity. With our framework, we proposed a novel way to distinguish the contribution of these familiarity mechanisms to the overall feeling of familiarity. By viewing them as complementary, our two models allow us to make new testable predictions that could be of interest to shed light on the familiarity phenomenon.

Hippocampal place code plasticity in CA1 requires postsynaptic membrane fusion

Rapid delivery of glutamate receptors to the postsynaptic membrane via vesicle fusion is a central component of synaptic plasticity. However, it is unknown how this process supports specific neural computations during behavior. To bridge this gap, we combined conditional genetic deletion of a component of the postsynaptic membrane fusion machinery, Syntaxin3 (Stx3), in hippocampal CA1 neurons of mice with population in vivo calcium imaging. This approach revealed that Stx3 is necessary for forming the neural dynamics that support novelty processing, spatial reward memory and offline memory consolidation. In contrast, CA1 Stx3 was dispensable for maintaining aspects of the neural code that exist presynaptic to CA1 such as representations of context and space. Thus, manipulating postsynaptic membrane fusion identified computations that specifically require synaptic restructuring via membrane trafficking in CA1 and distinguished them from neural representation that could be inherited from upstream brain regions or learned through other mechanisms.

Septo-hippocampal dynamics and the encoding of space and time

Encoding an event in memory requires neural activity to represent multiple dimensions of behavioral experience in space and time. Recent experiments have explored the influence of neural dynamics regulated by the medial septum on the functional encoding of space and time by neurons in the hippocampus and associated structures. This review addresses these dynamics, focusing on the role of theta rhythm, the differential effects of septal inactivation and activation on the functional coding of space and time by individual neurons, and the influence on phase coding that appears as phase precession. We also discuss data indicating that theta rhythm plays a role in timing the internal dynamics of memory encoding and retrieval, as well as the behavioral influences of these neuronal manipulations with regard to memory function.

A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions

We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: (1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and (2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer's disease (AD) conditions. The network is trained on pictures and names of digits from 0 to 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words ("odd" instead of "nine"). Under severe damage conditions, the network shows a null response ("I don't know"). Neurobiological plausibility of the model is extensively discussed.

Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans

Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to the spatial location being looked at. The representation is allocentric, in that the responses are to locations "out there" in the world, and are relatively invariant with respect to retinal position, eye position, head direction, and the place where the individual is located. The underlying connectivity in humans is from ventromedial visual cortical regions to the parahippocampal scene area, leading to the theory that spatial view cells are formed by combinations of overlapping feature inputs self-organized based on their closeness in space. Thus, although spatial view cells represent "where" for episodic memory and navigation, they are formed by ventral visual stream feature inputs in the parahippocampal gyrus in what is the parahippocampal scene area. A second "where" driver of spatial view cells are parietal inputs, which it is proposed provide the idiothetic update for spatial view cells, used for memory recall and navigation when the spatial view details are obscured. Inferior temporal object "what" inputs and orbitofrontal cortex reward inputs connect to the human hippocampal system, and in macaques can be associated in the hippocampus with spatial view cell "where" representations to implement episodic memory. Hippocampal spatial view cells also provide a basis for navigation to a series of viewed landmarks, with the orbitofrontal cortex reward inputs to the hippocampus providing the goals for navigation, which can then be implemented by hippocampal connectivity in humans to parietal cortex regions involved in visuomotor actions in space. The presence of foveate vision and the highly developed temporal lobe for object and scene processing in primates including humans provide a basis for hippocampal spatial view cells to be key to understanding episodic memory in the primate and human hippocampus, and the roles of this system in primate including human navigation.

Route-dependent spatial engram tagging in mouse dentate gyrus

The dentate gyrus (DG) of hippocampus is hypothesized to act as a pattern separator that distinguishes between similar input patterns during memory formation and retrieval. Sparse ensembles of DG cells associated with learning and memory, i.e. engrams, have been labeled and manipulated to recall novel context memories. Functional studies of DG cell activity have demonstrated the spatial specificity and stability of DG cells during navigation. To reconcile how the DG contributes to separating global context as well as individual navigational routes, we trained mice to perform a delayed-non-match-to-position (DNMP) T-maze task and labeled DG neurons during performance of this task on a novel T-maze. The following day, mice navigated a second environment: the same T-maze, the same T-maze with one route permanently blocked but still visible, or a novel open field. We found that the degree of engram reactivation across days differed based on the traversal of maze routes, such that mice traversing only one arm had higher ensemble overlap than chance but less overlap than mice running the full two-route task. Mice experiencing the open field had similar ensemble sizes to the other groups but only chance-level ensemble reactivation. Ensemble overlap differences could not be explained by behavioral variability across groups, nor did behavioral metrics correlate to degree of ensemble reactivation. Together, these results support the hypothesis that DG contributes to spatial navigation memory and that partially non-overlapping ensembles encode different routes within the context of an environment.

The geometry of representational drift in natural and artificial neural networks

Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift differs from in-session variance and most often occurs along directions that have the most in-class variance, leading to a significant turnover in the neurons used for a given representation. Interestingly, despite this significant change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days. The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computational advantageous for the brain in the same way it is for artificial neural networks, e.g. preventing overfitting.

The hearing hippocampus

The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.

Learning in deep neural networks and brains with similarity-weighted interleaved learning

Understanding how the brain learns throughout a lifetime remains a long-standing challenge. In artificial neural networks (ANNs), incorporating novel information too rapidly results in catastrophic interference, i.e., abrupt loss of previously acquired knowledge. Complementary Learning Systems Theory (CLST) suggests that new memories can be gradually integrated into the neocortex by interleaving new memories with existing knowledge. This approach, however, has been assumed to require interleaving all existing knowledge every time something new is learned, which is implausible because it is time-consuming and requires a large amount of data. We show that deep, nonlinear ANNs can learn new information by interleaving only a subset of old items that share substantial representational similarity with the new information. By using such similarity-weighted interleaved learning (SWIL), ANNs can learn new information rapidly with a similar accuracy level and minimal interference, while using a much smaller number of old items presented per epoch (fast and data-efficient). SWIL is shown to work with various standard classification datasets (Fashion-MNIST, CIFAR10, and CIFAR100), deep neural network architectures, and in sequential learning frameworks. We show that data efficiency and speedup in learning new items are increased roughly proportionally to the number of nonoverlapping classes stored in the network, which implies an enormous possible speedup in human brains, which encode a high number of separate categories. Finally, we propose a theoretical model of how SWIL might be implemented in the brain.

Action-driven remapping of hippocampal neuronal populations in jumping rats

The current dominant view of the hippocampus is that it is a navigation "device" guided by environmental inputs. Yet, a critical aspect of navigation is a sequence of planned, coordinated actions. We examined the role of action in the neuronal organization of the hippocampus by training rats to jump a gap on a linear track. Recording local field potentials and ensembles of single units in the hippocampus, we found that jumping produced a stereotypic behavior associated with consistent electrophysiological patterns, including phase reset of theta oscillations, predictable global firing-rate changes, and population vector shifts of hippocampal neurons. A subset of neurons ("jump cells") were systematically affected by the gap but only in one direction of travel. Novel place fields emerged and others were either boosted or attenuated by jumping, yet the theta spike phase versus animal position relationship remained unaltered. Thus, jumping involves an action plan for the animal to traverse the same route as without jumping, which is faithfully tracked by hippocampal neuronal activity.

Impact of optogenetic pulse design on CA3 learning and replay: A neural model

Optogenetic manipulation of hippocampal circuitry is an important tool for investigating learning in vivo. Numerous approaches to pulse design have been employed to elicit desirable circuit and behavioral outcomes. Here, we systematically test the outcome of different single-pulse waveforms in a rate-based model of hippocampal memory function at the level of mnemonic replay extension and de novo synaptic weight formation in CA3 and CA1. Lower-power waveforms with long forward or forward and backward ramps yield more natural sequence replay dynamics and induce synaptic plasticity that allows for more natural memory replay timing, in contrast to square or backward ramps. These differences between waveform shape and amplitude are preserved with the addition of noise in membrane potential, light scattering, and protein expression, improving the potential validity of predictions for in vivo work. These results inform future optogenetic experimental design choices in the field of learning and memory.

A direct lateral entorhinal cortex to hippocampal CA2 circuit conveys social information required for social memory

The hippocampus is essential for different forms of declarative memory, including social memory, the ability to recognize and remember a conspecific. Although recent studies identify the importance of the dorsal CA2 region of the hippocampus in social memory storage, little is known about its sources of social information. Because CA2, like other hippocampal regions, receives its major source of spatial and non-spatial information from the medial and lateral subdivisions of entorhinal cortex (MEC and LEC), respectively, we investigated the importance of these inputs for social memory. Whereas MEC inputs to CA2 are dispensable, the direct inputs to CA2 from LEC are both selectively activated during social exploration and required for social memory. This selective behavioral role of LEC is reflected in the stronger excitatory drive it provides to CA2 compared with MEC. Thus, a direct LEC → CA2 circuit is tuned to convey social information that is critical for social memory.

A neural network model of when to retrieve and encode episodic memories

Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions.

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

Place cells may simply be memory cells: Memory compression leads to spatial tuning and history dependence

The observation of place cells has suggested that the hippocampus plays a special role in encoding spatial information. However, place cell responses are modulated by several nonspatial variables and reported to be rather unstable. Here, we propose a memory model of the hippocampus that provides an interpretation of place cells consistent with these observations. We hypothesize that the hippocampus is a memory device that takes advantage of the correlations between sensory experiences to generate compressed representations of the episodes that are stored in memory. A simple neural network model that can efficiently compress information naturally produces place cells that are similar to those observed in experiments. It predicts that the activity of these cells is variable and that the fluctuations of the place fields encode information about the recent history of sensory experiences. Place cells may simply be a consequence of a memory compression process implemented in the hippocampus.

Retrospective memory integration accompanies reconfiguration of neural cell assemblies

Memory is a dynamic process that is based on and can be altered by experiences. Integrating memories of multiple experiences (memory integration) is the basis of flexible and complex decision-making. However, the mechanism of memory integration in neural networks of the brain remains poorly understood. In this study, we built a recurrent spiking network model and investigated the neural mechanism of memory integration before a decision is made (retrospective memory integration) at the neural circuit level. Our simulations suggest that retrospective memory integration accompanies reconfiguration of neural cell assemblies. Additionally, partially blocking neural network plasticity leads to failure of memory integration. These findings can potentially guide the experimental investigation of the neural mechanism of retrospective memory integration and serve as the basis for developing new artificial intelligence algorithms.

Procognitive activity of nitric oxide inhibitors and donors in animal models

Nitric oxide is a small gaseous molecule that plays important roles in the majority of biological functions. Impairments of NO-related pathways contribute to the majority of neurological disorders, such as Alzheimer's disease (AD), and mental disorders, such as schizophrenia. Cognitive decline is one of the most serious impairments accompanying both AD and schizophrenia. In the present study, the activities of NO donors, slow (spermine NONOate) or fast (DETANONOate) releasers, and selective inhibitor of neuronal nitric oxide synthase N(ω)-propyl-l-arginine (NPLA) were investigated in pharmacological models of schizophrenia and AD. Cognitive impairments were induced by administration of MK-801 or scopolamine and were measured in novel object recognition (NOR) and Y-maze tests. The compounds were investigated at doses of 0.05-0.5 mg/kg. The dose-dependent effectiveness of all the compounds was observed in the NOR test, while only the highest doses of spermine NONOate and NPLA were active in the Y-maze test. DETANONOate was not active in the Y-maze test. The impact of the investigated compounds on motor coordination was tested at doses of 0.5 and 1 mg/kg. Only NPLA at a dose of 1 mg/kg slightly disturbed motor coordination in animals.

Functional coupling between CA3 and laterobasal amygdala supports schema dependent memory formation

The medial temporal lobe drives semantic congruence dependent memory formation. However, the exact roles of hippocampal subfields and surrounding brain regions remain unclear. Here, we used an established paradigm and high-resolution functional magnetic resonance imaging of the medial temporal lobe together with cytoarchitectonic probability estimates in healthy humans. Behaviorally, robust congruence effects emerged in young and older adults, indicating that schema dependent learning is unimpaired during healthy aging. Within the medial temporal lobe, semantic congruence was associated with hemodynamic activity in the subiculum, CA1, CA3 and dentate gyrus, as well as the entorhinal cortex and laterobasal amygdala. Importantly, a subsequent memory analysis showed increased activity for later remembered vs. later forgotten congruent items specifically within CA3, and this subfield showed enhanced functional connectivity to the laterobasal amygdala. As such, our findings extend current models on schema dependent learning by pinpointing the functional properties of subregions within the medial temporal lobe.

Orientation selectivity enhances context generalization and generative predictive coding in the hippocampus

We learn and remember multiple new experiences throughout the day. The neural principles enabling continuous rapid learning and formation of distinct representations of numerous sequential experiences without major interference are not understood. To understand this process, here we interrogated ensembles of hippocampal place cells as rats explored 15 novel linear environments interleaved with sleep sessions over continuous 16 h periods. Remarkably, we found that a population of place cells were selective to environment orientation and topology. This orientation selectivity property biased the network-level discrimination and re/mapping between multiple environments. Novel environmental representations emerged rapidly as more generic, but repeated experience within the environments subsequently enhanced their discriminability. Generalization of prior experience with different environments consequently improved network predictability of future novel environmental representations via strengthened generative predictive codes. These coding schemes reveal a high-capacity, high-efficiency neuronal framework for rapid representation of numerous sequential experiences with optimal discrimination-generalization balance and reduced interference.

Nitric Oxide-Dependent Mechanisms Underlying MK-801- or Scopolamine-Induced Memory Dysfunction in Animals: Mechanistic Studies

MK-801, an NMDA receptor antagonist, and scopolamine, a cholinergic receptor blocker, are widely used as tool compounds to induce learning and memory deficits in animal models to study schizophrenia or Alzheimer-type dementia (AD), respectively. Memory impairments are observed after either acute or chronic administration of either compound. The present experiments were performed to study the nitric oxide (NO)-related mechanisms underlying memory dysfunction induced by acute or chronic (14 days) administration of MK-801 (0.3 mg/kg, i.p.) or scopolamine (1 mg/kg, i.p.). The levels of L-arginine and its derivatives, L-citrulline, L-glutamate, L-glutamine and L-ornithine, were measured. The expression of constitutive nitric oxide synthases (cNOS), dimethylaminohydrolase (DDAH1) and protein arginine N-methyltransferases (PMRTs) 1 and 5 was evaluated, and the impact of the studied tool compounds on cGMP production and NMDA receptors was measured. The studies were performed in both the cortex and hippocampus of mice. S-nitrosylation of selected proteins, such as GLT-1, APP and tau, was also investigated. Our results indicate that the availability of L-arginine decreased after chronic administration of MK-801 or scopolamine, as both the amino acid itself as well as its level in proportion to its derivatives (SDMA and NMMA) were decreased. Additionally, among all three methylamines, SDMA was the most abundant in the brain (~70%). Administration of either compound impaired eNOS-derived NO production, increasing the monomer levels, and had no significant impact on nNOS. Both compounds elevated DDAH1 expression, and slight decreases in PMRT1 and PMRT5 in the cortex after scopolamine (acute) and MK-801 (chronic) administration were observed in the PFC, respectively. Administration of MK-801 induced a decrease in the cGMP level in the hippocampus, accompanied by decreased NMDA expression, while increased cGMP production and decreased NMDA receptor expression were observed after scopolamine administration. Chronic MK-801 and scopolamine administration affected S-nitrosylation of GLT-1 transport protein. Our results indicate that the analyzed tool compounds used in pharmacological models of schizophrenia or AD induce changes in NO-related pathways in the brain structures involved in cognition. To some extent, the changes resemble those observed in human samples.

Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus

Memory, the process of encoding, storing, and maintaining information over time to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study, we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size, and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.

Neuronal ensembles in memory processes

A neuronal ensemble represents the concomitant activity of a specific group of neurons that could encompass a broad repertoire of brain functions such as motor, perceptual, memory or cognitive states. On the other hand, a memory engram portrays the physical manifestation of memory or the changes that enable learning and retrieval. Engram studies focused for many years on finding where memories are stored as in, which cells or brain regions represent a memory trace, and disregarded the investigation of how neuronal activity patterns give rise to such memories. Recent experiments suggest that the association and reactivation of specific neuronal groups could be the main mechanism underlying the brain's ability to remember past experiences and envision future actions. Thus, the growing consensus is that the interaction between neuronal ensembles could allow sequential activity patterns to become memories and recurrent memories to compose complex behaviors. The goal of this review is to propose how the neuronal ensemble framework could be translated and useful to understand memory processes.

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

Memory models often emphasize the need to encode novel patterns of neural activity imposed by sensory drive. Prior learning and innate architecture likely restrict neural plasticity, however. Here, we test how the incorporation of synthetic hippocampal signals is constrained by preexisting circuit dynamics. We optogenetically stimulated small groups of CA1 neurons as mice traversed a chosen segment of a linear track, mimicking the emergence of place fields. Stimulation induced persistent place field remapping in stimulated and non-stimulated neurons. The emergence of place fields could be predicted from sporadic firing in the new place field location and the temporal relationship to peer neurons before the optogenetic perturbation. Circuit modification was reflected by altered spike transmission between connected pyramidal cells and inhibitory interneurons, which persisted during post-experience sleep. We hypothesize that optogenetic perturbation unmasked sub-threshold place fields. Plasticity in recurrent/lateral inhibition may drive learning through the rapid association of existing states.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

Hippocampal spatial memory representations in mice are heterogeneously stable

The population of hippocampal neurons actively coding space continually changes across days as mice repeatedly perform tasks. Many hippocampal place cells become inactive while other previously silent neurons become active, challenging the idea that stable behaviors and memory representations are supported by stable patterns of neural activity. Active cell replacement may disambiguate unique episodes that contain overlapping memory cues, and could contribute to reorganization of memory representations. How active cell replacement affects the evolution of representations of different behaviors within a single task is unknown. We trained mice to perform a delayed nonmatching to place task over multiple weeks, and performed calcium imaging in area CA1 of the dorsal hippocampus using head-mounted miniature microscopes. Cells active on the central stem of the maze "split" their calcium activity according to the animal's upcoming turn direction (left or right), the current task phase (study or test), or both task dimensions, even while spatial cues remained unchanged. We found that, among reliably active cells, different splitter neuron populations were replaced at unequal rates, resulting in an increasing number of cells modulated by turn direction and a decreasing number of cells with combined modulation by both turn direction and task phase. Despite continual reorganization, the ensemble code stably segregated these task dimensions. These results show that hippocampal memories can heterogeneously reorganize even while behavior is unchanging.

In vivo assessment of mechanisms underlying the neurovascular basis of postictal amnesia

Long-lasting confusion and memory difficulties during the postictal state remain a major unmet problem in epilepsy that lacks pathophysiological explanation and treatment. We previously identified that long-lasting periods of severe postictal hypoperfusion/hypoxia, not seizures per se, are associated with memory impairment after temporal lobe seizures. While this observation suggests a key pathophysiological role for insufficient energy delivery, it is unclear how the networks that underlie episodic memory respond to vascular constraints that ultimately give rise to amnesia. Here, we focused on cellular/network level analyses in the CA1 of hippocampus in vivo to determine if neural activity, network oscillations, synaptic transmission, and/or synaptic plasticity are impaired following kindled seizures. Importantly, the induction of severe postictal hypoperfusion/hypoxia was prevented in animals treated by a COX-2 inhibitor, which experimentally separated seizures from their vascular consequences. We observed complete activation of CA1 pyramidal neurons during brief seizures, followed by a short period of reduced activity and flattening of the local field potential that resolved within minutes. During the postictal state, constituting tens of minutes to hours, we observed no changes in neural activity, network oscillations, and synaptic transmission. However, long-term potentiation of the temporoammonic pathway to CA1 was impaired in the postictal period, but only when severe local hypoxia occurred. Lastly, we tested the ability of rats to perform object-context discrimination, which has been proposed to require temporoammonic input to differentiate between sensory experience and the stored representation of the expected object-context pairing. Deficits in this task following seizures were reversed by COX-2 inhibition, which prevented severe postictal hypoxia. These results support a key role for hypoperfusion/hypoxia in postictal memory impairments and identify that many aspects of hippocampal network function are resilient during severe hypoxia except for long-term synaptic plasticity.

Prediction and memory: A predictive coding account

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to 'explain away' ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data.

Simulations reveal how M-currents and memory-based inputs from CA3 enable single neuron mismatch detection for EC3 inputs to the CA1 subfield of hippocampus

Significant evidence has accumulated to support the hypothesis that hippocampal region CA1 operates as an associative mismatch detector (e.g., Hasselmo ME, Schnell E, Barkai E. J Neurosci 15: 5249-5262, 1995; Duncan K, Curtis C, Davachi L. J Neurosci 29: 131-139, 2009; Kumaran D, Maguire EA. J Neurosci 27: 8517-8524, 2007; Lisman JE, Grace AA. Neuron 46: 703-713, 2005; Lisman JE, Otmakhova NA. Hippocampus 11: 551-568 2001; Lörincz A, Buzsáki G. Ann N Y Acad Sci 911: 83-111, 2000; Meeter M, Murre JMJ, Talamini LM. Hippocampus 14: 722-741, 2004; Schiffer AM, Ahlheim C, Wurm MF, Schubotz RI. PLoS One 7: e36445, 2012; Vinogradova OS. Hippocampus 11: 578-598 2001). CA1 compares predictive synaptic signals from CA3 with synaptic signals from EC3, which reflect actual sensory inputs. The new CA1 pyramidal model presented here shows that the distal-proximal segregation of synaptic inputs from EC3 versus CA3, along with other biophysical features, enable such pyramids to serve as comparators that switch output encoding from a brief burst, for a match, to prolonged tonic spiking, for a mismatch. By including often-overlooked features of CA1 pyramidal neurons, this new model allows simulation of pharmacological effects that can eliminate either the match (phasic mode) response or the mismatch (tonic mode) response. These simulations reveal that dysfunctions can arise from either too much or too little ACh stimulation of the muscarinic receptors that control KCNQ channels. Additionally, a dysfunction caused by administration of an N-methyl-d-aspartate antagonist could be rescued by simultaneous administration of a KCNQ channel agonist, such as retigabine.NEW & NOTEWORTHY Hippocampal region CA1 operates as an associative mismatch detector, comparing predictive signals from CA3 with signals from EC3 reflecting sensory inputs. This new CA1 pyramidal model shows that biophysical features enable these comparators to switch output between brief bursts for matches and tonic spiking for mismatches. This suggests that cognitive learning models (e.g., predictive coding) may require much less match/mismatch circuitry than commonly assumed. Additional simulations illuminate deficits seen in psychiatric disorders and drug-induced states.

Mnemonic prediction errors bias hippocampal states

When our experience violates our predictions, it is adaptive to upregulate encoding of novel information, while down-weighting retrieval of erroneous memory predictions to promote an updated representation of the world. We asked whether mnemonic prediction errors promote hippocampal encoding versus retrieval states, as marked by distinct network connectivity between hippocampal subfields. During fMRI scanning, participants were cued to internally retrieve well-learned complex room-images and were then presented with either an identical or a modified image (0-4 changes). In the left hemisphere, we find that CA1-entorhinal connectivity increases, and CA1-CA3 connectivity decreases, with the number of changes. Further, in the left CA1, the similarity between activity patterns during cued-retrieval of the learned room and during the image is lower when the image includes changes, consistent with a prediction error signal in CA1. Our findings provide a mechanism by which mnemonic prediction errors may drive memory updating—by biasing hippocampal states.

When our expectations are violated, it is adaptive to update our internal models to improve predictions in the future. Here, the authors show that during mnemonic violations, hippocampal networks are biased towards an encoding state and away from a retrieval state to potentially update these predictions.

The antidepressant efficacy of the muscarinic antagonist scopolamine: Past findings and future directions

Scopolamine is a nonselective muscarinic antagonist that has shown relatively rapid antidepressant effects, although to date the results are from limited clinical studies. Scopolamine reportedly has downstream signaling effects thought to be linked to neuroplasticity within glutamatergic synapses and consequent antidepressant action. In psychiatry, clinically validated pathways are unusual and thus merit further research in an effort develop more effect medicines for patients with mood disorders. Thus, we are faced with a unique opportunity to build on the clinical observation associated with scopolamine through reverse translation to identify of targets that retain the clinical efficacy while reducing the side effect profile. This chapter reviews the clinical antidepressant findings with scopolamine, including discussion of differential response across patient subgroups, as well as a review of biomarkers that predict clinical outcome. The preclinical data associated with scopolamine also are reviewed and convey a vision for narrowing in on the therapeutic muscarinic receptor subtype(s) that support the antidepressant effects to guide the development of next generation antimuscarinic drug targets for depression.

The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

Muscarinic and Nicotinic Modulation of Memory but not Verbal Problem-solving

Aspects of cognitive flexibility are modulated by the noradrenergic system, which is important in arousal and attention. Acetylcholine also modulates arousal and attention, as well as working memory. Effects of muscarinic and nicotinic antagonism on memory are well established. Our purpose was to test whether muscarinic and nicotinic antagonism affect aspects of cognitive flexibility, specifically verbal problem-solving, as well as memory, given acetylcholine's role in attention and arousal. Eighteen participants attended three testing sessions. Two hours before testing, participants received either 0.6 mg scopolamine, 10 mg mecamylamine, or placebo. Then, participants were tested on three memory tasks (Buschke Selective Reminding Test [BSRT], California Verbal Learning Test [CVLT], Rey Complex Figure Test), two verbal problem-solving/cognitive flexibility tasks (Compound Remote Associates Test, a timed anagram test), and a spatial inductive reasoning task (Raven's Progressive Matrices). Task order and drug order were counterbalanced. Memory impairment was seen on one BSRT measure and multiple CVLT measures with scopolamine, and with one BSRT measure with mecamylamine. There were no effects of either drug on any of the tasks involving cognitive flexibility, including verbal problem-solving. Specific memory impairments were detected using muscarinic, and to a marginal extent, nicotinic antagonists, as expected, but no effect was seen on cognitive flexibility. Therefore, although both the noradrenergic and cholinergic systems play important roles in arousal and cortical signal-to-noise processing, the cholinergic system does not appear to have the same effect as the noradrenergic system on cognitive flexibility, including verbal problem-solving.

Overview of computational models of hippocampus and related structures: Introduction to the special issue

Extensive computational modeling has focused on the hippocampal formation and associated cortical structures. This overview describes some of the factors that have motivated the strong focus on these structures, including major experimental findings and their impact on computational models. This overview provides a framework for describing the topics addressed by individual articles in this special issue of the journal Hippocampus.

A Simple, Biologically Plausible Feature Detector for Language Acquisition

Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. “Different” items are suppressed through inhibition, but presenting two “identical” items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.

Improving fitness increases dentate gyrus/CA3 volume in the hippocampal head and enhances memory in young adults

Converging evidence suggests a relationship between aerobic exercise and hippocampal neuroplasticity that interactively impacts hippocampally dependent memory. The majority of human studies have focused on the potential for exercise to reduce brain atrophy and attenuate cognitive decline in older adults, whereas animal studies often center on exercise-induced neurogenesis and hippocampal plasticity in the dentate gyrus (DG) of young adult animals. In the present study, initially sedentary young adults (18-35 years) participated in a moderate-intensity randomized controlled exercise intervention trial (ClinicalTrials.gov; NCT02057354) for a duration of 12 weeks. The aims of the study were to investigate the relationship between change in cardiorespiratory fitness (CRF) as determined by estimated V ˙ O 2 _MAX , hippocampally dependent mnemonic discrimination, and change in hippocampal subfield volume. Results show that improving CRF after exercise training is associated with an increased volume in the left DG/CA3 subregion in young adults. Consistent with previous studies that found exercise-induced increases in anterior hippocampus in older adults, this result was specific to the hippocampal head, or most anterior portion, of the subregion. Our results also demonstrate a positive relationship between change in CRF and change in corrected accuracy for trials requiring the highest level of discrimination on a putative behavioral pattern separation task. This relationship was observed in individuals who were initially lower-fit, suggesting that individuals who show greater improvement in their CRF may receive greater cognitive benefit. This work extends animal models by providing evidence for exercise-induced neuroplasticity specific to the neurogenic zone of the human hippocampus.

The Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition

Behavioral research in human verbal memory function led to the initial definition of episodic memory and semantic memory. A complete model of the neural mechanisms of episodic memory must include the capacity to encode and mentally reconstruct everything that humans can recall from their experience. This article proposes new model features necessary to address the complexity of episodic memory encoding and recall in the context of broader cognition and the functional properties of neurons that could contribute to this broader scope of memory. Many episodic memory models represent individual snapshots of the world with a sequence of vectors, but a full model must represent complex functions encoding and retrieving the relations between multiple stimulus features across space and time on multiple hierarchical scales. Episodic memory involves not only the space and time of an agent experiencing events within an episode but also features shown in neurophysiological data such as coding of speed, direction, boundaries, and objects. Episodic memory includes not only a spatio-temporal trajectory of a single agent but also segments of spatio-temporal trajectories for other agents and objects encountered in the environment consistent with data on encoding the position and angle of sensory features of objects and boundaries. We will discuss potential interactions of episodic memory circuits in the hippocampus and entorhinal cortex with distributed neocortical circuits that must represent all features of human cognition.

Study on the sub-regions volume of hippocampus and amygdala in schizophrenia

Background

Many studies have found volume changes in the hippocampus and amygdala in patients with schizophrenia, but these findings have not reached an agreement. Particularly, few results showed the volumes of the sub-regions of the amygdala. In this research, we aim to clarify volume changes of hippocampus and amygdala sub-regions in patients with schizophrenia.

Methods

The sample consisted of 69 patients with schizophrenia and 72 control subjects aged from 18 to 65 years. FreeSurfer 6.0 software was used on T1-weighted images to assess the volumes of hippocampus and amygdala and their sub-regions. The general linear model (GLM) was used to analyze the volume changes between the two groups. False discovery rate (FDR) correction was performed, and the significance level was set at 0.05.

Results

The hippocampus volume in schizophrenia showed reduction compared to healthy control (P<0.05). Several hippocampal subfields showed smaller volume in schizophrenia patients, including bilateral presubiculum and molecular layer, left hippocampal tail, subiculum and cornus ammonis (CA)1, and right parasubiculum (P<0.05). Left amygdala volume showed a decrease as well, sub-regions including the bilateral basal nucleus, anterior-amygdaloid-area (AAA), paralaminar nucleus and left lateral nucleus (P<0.05).

Conclusions

Several sub-regions of hippocampus and amygdala showed a volumetric decline in patients group, which suggest the key roles of these regions in the pathophysiology of schizophrenia. Based on these results, we speculate that these regions could be used to assess the early finding of schizophrenia.

The role of the hippocampus in weighting expectations during inference under uncertainty

Making inference under uncertainty requires an optimal weighting of prior expectations and observations. How this weighting is realized in the brain remains elusive. To investigate this, we recorded functional neuroimaging data while participants estimated a number based on noisy observations. Crucially, the prior expectation about the variability of observations (an expected variability) was manipulated. Consistent with normative models, when novel observations were characterized by higher expected or observed variability, participants' estimates relied more on expectations than novel observations and were characterized by higher stochasticity. Activity in hippocampus increased when novel evidence was characterized by higher expected or observed variability. Response in superior parietal cortex reflected a precision-weighted prediction error signal (i.e., the distance between observations and expectations) that was modulated by hippocampal activity. Our findings implicate the hippocampus during inference under uncertainty, suggesting a role in weighting prior representations over observations and in modulating responsivity of superior parietal cortex to prediction error.

Hippocampal Dentate Gyrus Atrophy Predicts Pattern Separation Impairment in Patients with LGI1 Encephalitis

Day-to-day life involves the perception of events that resemble one another. For the sufficient encoding and correct retrieval of similar information, the hippocampus provides two essential cognitive processes. Pattern separation refers to the differentiation of similar input information, whereas pattern completion reactivates memory representations based on noisy or degraded stimuli. It has been shown that pattern separation specifically relies on the hippocampal dentate gyrus (DG), whereas pattern completion is performed within CA3 networks. Lesions to these hippocampal networks emerging in the course of neurological disorders may thus affect both processes. In anti-leucine-rich, glioma-inactivated 1 (LGI1) encephalitis it has been shown in animal models and human imaging studies that hippocampal DG and CA3 are preferentially involved in the pathophysiology process. Thus, in order to elucidate the structure-function relationship and contribution of hippocampal subfields to pattern separation, we examined patients (n = 15, age range: 36-77 years) with the rare LGI1 encephalitis showing lesions to hippocampal subfields. Patients were tested 3.53 ± 0.65 years after the acute phase of the disease. Structural sequelae were determined by hippocampal subfield volumetry for the DG, CA1, and CA2/3. Patients showed an overall memory deficit including a significant reduction in pattern separation performance (p = 0.016). In volumetry, we found a global hippocampal volume reduction. The deficits in pattern separation performance were best predicted by the DG (p = 0.029), whereas CA1 was highly predictive of recognition memory deficits (p < 0.001). These results corroborate the framework of a regional specialization of hippocampal functions involved in cognitive processing.

Remembering rewarding futures: A simulation-selection model of the hippocampus

Despite tremendous progress, the neural circuit dynamics underlying hippocampal mnemonic processing remain poorly understood. We propose a new model for hippocampal function-the simulation-selection model-based on recent experimental findings and neuroecological considerations. Under this model, the mammalian hippocampus evolved to simulate and evaluate arbitrary navigation sequences. Specifically, we suggest that CA3 simulates unexperienced navigation sequences in addition to remembering experienced ones, and CA1 selects from among these CA3-generated sequences, reinforcing those that are likely to maximize reward during offline idling states. High-value sequences reinforced in CA1 may allow flexible navigation toward a potential rewarding location during subsequent navigation. We argue that the simulation-selection functions of the hippocampus have evolved in mammals mostly because of the unique navigational needs of land mammals. Our model may account for why the mammalian hippocampus has evolved not only to remember, but also to imagine episodes, and how this might be implemented in its neural circuits.

Exploration of the Neurobiological Basis for a Three-System, Multiattribute Model of Memory

The structure and utilization of memory is central to one's knowledge of the past, interpretation of the present, and prediction of the future. Therefore, the understanding of the structural and process components of memory systems at the psychological and neurobiological level is of paramount importance. There have been a number of attempts to divide learning and memory into multiple memory systems. Schacter and Tulving, Memory systems 1994. MIT Press, Cambridge (1994) have suggested that one needs to define memory systems in terms of the kind of information to be represented, the processes associated with the operation of each system, and the neurobiological substrates, including neural structures and mechanisms, that subserve each system. Furthermore, it is likely that within each system there are multiple forms or subsystems associated with each memory system and there are likely to be multiple processes that define the operation of each system. Finally, there are probably multiple neural structures that form the overall substrate of a memory system.

Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

The cognitive nuances of surprising events: exposure to unexpected stimuli elicits firing variations in neurons of the dorsal CA1 hippocampus

The ability to recognize novel situations is among the most fascinating and vital of the brain functions. A hypothesis posits that encoding of novelty is prompted by failures in expectancy, according to computation matching incoming information with stored events. Thus, unexpected changes in context are detected within the hippocampus and transferred to downstream structures, eliciting the arousal of the dopamine system. Nevertheless, the precise locus of detection is a matter of debate. The dorsal CA1 hippocampus (dCA1) appears as an ideal candidate for operating a mismatch computation and discriminating the occurrence of diverse stimuli within the same environment. In this study, we sought to determine dCA1 neuronal firing during the experience of novel stimuli embedded in familiar contexts. We performed population recordings while head-fixed mice navigated virtual environments. Three stimuli were employed, namely a novel pattern of visual cues, an odor, and a reward with enhanced valence. The encounter of unexpected events elicited profound variations in dCA1 that were assessed both as opposite rate directions and altered network connectivity. When experienced in sequence, novel stimuli elicited specific responses that often exhibited cross-sensitization. Short-latency, event-triggered responses were in accordance with the detection of novelty being computed within dCA1. We postulate that firing variations trigger neuronal disinhibition, and constitute a fundamental mechanism in the processing of unexpected events and in learning. Elucidating the mechanisms underlying detection and computation of novelty might help in understanding hippocampal-dependent cognitive dysfunctions associated with neuropathologies and psychiatric conditions.