Neural dynamics of the cognitive map in the hippocampus

A spatial transformation-based CAN model for information integration within grid cell modules

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

Modeling the grid cell activity based on cognitive space transformation

The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.

Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in marmoset hippocampus

The role of the hippocampus in spatial navigation has been primarily studied in nocturnal mammals, such as rats, that lack many adaptations for daylight vision. Here we demonstrate that during 3D navigation, the common marmoset, a new world primate adapted to daylight, predominantly uses rapid head-gaze shifts for visual exploration while remaining stationary. During active locomotion marmosets stabilize the head, in contrast to rats that use low-velocity head movements to scan the environment as they locomote. Pyramidal neurons in the marmoset hippocampus CA3/CA1 regions predominantly show mixed selectivity for 3D spatial view, head direction, and place. Exclusive place selectivity is scarce. Inhibitory interneurons are predominantly mixed selective for angular head velocity and translation speed. Finally, we found theta phase resetting of local field potential oscillations triggered by head-gaze shifts. Our findings indicate that marmosets adapted to their daylight ecological niche by modifying exploration/navigation strategies and their corresponding hippocampal specializations.

[A spatial localization model of mobile robot based on entorhinal-hippocampal cognitive mechanism in rat brain]

Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mechanism of spatial cells within the entorhinal-hippocampal structure of the rat brain and proposed a spatial localization model for mobile robot. Its characteristics were as follows: on the basis of the information transmission model from grid cells to place cells, the neural network model of place cells interaction was introduced to obtain the place cell plate with a single-peaked excitatory activity package. Then the solution to the robot's position was achieved by establishing a transformation relationship between the position of the excitatory activity package on the place cell plate and the robot's position in the physical environment. In this paper, simulation experiments and physical experiments were designed to verify the model. The experimental results showed that compared with RatSLAM and the model of grid cells to place cells, the positioning performance of the model in this paper was more accurate, and the cumulative error in the long-time path integration process of the robot was also smaller. The research results of this paper lay a foundation for the robot navigation method that mimics the cognitive mechanism of rat brain.

Entorhinal-hippocampal interactions lead to globally coherent representations of space

The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory.

A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network

Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.

How visual stimulus effects the time perception? The evidence from time perception of emotional videos

Time perception is defined as a subjective judgment on the elapsed time of an event. It can change according to both external and internal factors. There are two main paradigms of time perception; retrospective time perception (RTP) and prospective time perception (PTP). Two paradigms differ from each other according to whether the subject has knowledge on the importance of passage of time in the given task. Since RTP paradigm studies are harder to conduct, studies on RTP paradigm is far fewer than studies on PTP. Thus in the current study, both RTP and PTP paradigms are investigated. Also, time perception is discussed in relation to internal clock model and cognitive load. Emotional motion videos are used to create cognitive load and manipulate internal clock. Results showed the effect of emotion on time perception. Another major finding is that shorter videos are perceived longer whereas longer videos are perceived shorter as in accordance with Vierordt's Law. However, there was no difference between RTP and PTP paradigms. These results indicate that emotional videos change our internal clock while a number of changes in a motion video create cognitive load causing disturbance of time perception.

Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model

Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors.

Optimal path-finding through mental exploration based on neural energy field gradients

Rodent animal can accomplish self-locating and path-finding task by forming a cognitive map in the hippocampus representing the environment. In the classical model of the cognitive map, the system (artificial animal) needs large amounts of physical exploration to study spatial environment to solve path-finding problems, which costs too much time and energy. Although Hopfield's mental exploration model makes up for the deficiency mentioned above, the path is still not efficient enough. Moreover, his model mainly focused on the artificial neural network, and clear physiological meanings has not been addressed. In this work, based on the concept of mental exploration, neural energy coding theory has been applied to the novel calculation model to solve the path-finding problem. Energy field is constructed on the basis of the firing power of place cell clusters, and the energy field gradient can be used in mental exploration to solve path-finding problems. The study shows that the new mental exploration model can efficiently find the optimal path, and present the learning process with biophysical meaning as well. We also analyzed the parameters of the model which affect the path efficiency. This new idea verifies the importance of place cell and synapse in spatial memory and proves that energy coding is effective to study cognitive activities. This may provide the theoretical basis for the neural dynamics mechanism of spatial memory.

On the time course of short-term forgetting: a human experimental model for the sense of balance

The primary aim of this study was to establish whether the decline of the memory of an angular displacement, detected by the semicircular canals, is best characterized by an exponential function or by a power function. In 27 subjects a conflict was created between the semicircular canals and the graviceptive systems. Subjects were seated, facing forwards, in the gondola of a large centrifuge. The centrifuge was accelerated from stationary to 2.5Gz. While the swing out of the gondola (66°) during acceleration constitutes a frontal plane angular-displacement stimulus to the semicircular canals, the graviceptive systems persistently signal that the subject is upright. During 6 min at 2.5Gz the perceived head and body position was recorded; in darkness the subject repeatedly adjusted the orientation of a luminous line so that it appeared to be horizontal. Acceleration of the centrifuge induced a sensation of tilt which declined with time in a characteristic way. A three-parameter exponential function (Y = Ae(-bt) + C) and a power function (Y = At(-b) + C) were fitted to the data points. The inter-individual variability was considerable. In the vast majority of cases, however, the exponential function provided a better fit (in terms of RMS error) than the power function. The mean exponential function was: y = 27.8e(-0.018t) + 0.5°, where t is time in seconds. Findings are discussed with connection to possible underlying neural mechanisms; in particular, the head-direction system and short-term potentiation and persistent action potential firing in the hippocampus are considered.

Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats

Patients with temporal lobe epilepsy (TLE) often display cognitive deficits. However, current epilepsy therapeutic interventions mainly aim at how to reduce the frequency and degree of epileptic seizures. Recovery of cognitive impairment is not attended enough, resulting in the lack of effective approaches in this respect. In the pilocarpine-induced temporal lobe epilepsy rat model, memory impairment has been classically reported. Here we evaluated spatial cognition changes at different epileptogenesis stages in rats of this model and explored the effects of long-term Mozart music exposure on the recovery of cognitive ability. Our results showed that pilocarpine rats suffered persisting cognitive impairment during epileptogenesis. Interestingly, we found that Mozart music exposure can significantly enhance cognitive ability in epileptic rats, and music intervention may be more effective for improving cognitive function during the early stages after Status epilepticus. These findings strongly suggest that Mozart music may help to promote the recovery of cognitive damage due to seizure activities, which provides a novel intervention strategy to diminish cognitive deficits in TLE patients.

Can the activities of the large scale cortical network be expressed by neural energy? A brief review

This paper mainly discusses and summarize that the changes of biological energy in the brain can be expressed by the biophysical energy we constructed. Different from the electrochemical energy, the biophysical energy proposed in the paper not only can be used to simulate the activity of neurons but also be used to simulate the neural activity of large scale cortical networks, so that the scientific nature of the neural energy coding was discussed.

A neural network model of reliably optimized spike transmission

We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.

Spatial information enhanced by non-spatial information in hippocampal granule cells

The hippocampus organizes sequential memory composed of non-spatial information (such as objects and odors) and spatial information (places). The dentate gyrus (DG) in the hippocampus receives two types of information from the lateral and medial entorhinal cortices. Non-spatial and spatial information is delivered respectively to distal and medial dendrites (MDs) of granule cells (GCs) within the molecular layer in the DG. To investigate the role of the association of those two inputs, we measured the response characteristics of distal and MDs of a GC in a rat hippocampal slice and developed a multi-compartment GC model with dynamic synapses; this model reproduces the response characteristics of the dendrites. Upon applying random inputs or input sequences generated by a Markov process to the computational model, it was found that a high-frequency random pulse input to distal dendrites (DDs) and, separately, regular burst inputs to MDs were effective for inducing GC activation. Furthermore, when the random and theta burst inputs were simultaneously applied to the respective dendrites, the pattern discrimination for theta burst input to MDs that caused slight GC activation was enhanced in the presence of random input to DDs. These results suggest that the temporal pattern discrimination of spatial information is originally involved in a synaptic characteristic in GCs and is enhanced by non-spatial information input to DDs. Consequently, the co-activation of two separate inputs may play a crucial role in the information processing on dendrites of GCs by usefully combing each temporal sequence.

A computational model for preplay in the hippocampus

The hippocampal network produces sequences of neural activity even when there is no time-varying external drive. In offline states, the temporal sequence in which place cells fire spikes correlates with the sequence of their place fields. Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment. This preplay phenomenon suggests that OSA is generated intrinsically in the hippocampal network, and not established by external sensory inputs. Previous studies showed that continuous attractor networks with asymmetric patterns of connectivity, or with slow, local negative feedback, can generate sequential activity. This mechanism could account for preplay if the network only represented a single spatial map, or chart. However, global remapping in the hippocampus implies that multiple charts are represented simultaneously in the hippocampal network and it remains unknown whether the network with multiple charts can account for preplay. Here we show that it can. Driven with random inputs, the model generates sequences in every chart. Place fields in a given chart and OSA generated by the network are highly correlated. We also find significant correlations, albeit less frequently, even when the OSA is correlated with a new chart in which place fields are randomly scattered. These correlations arise from random correlations between the orderings of place fields in the new chart and those in a pre-existing chart. Our results suggest two different accounts for preplay. Either an existing chart is re-used to represent a novel environment or a new chart is formed.

A cognitive map model based on spatial and goal-oriented mental exploration in rodents

The rodent hippocampus has been used to represent the spatial environment as a cognitive map. Classical theories suggest that the cognitive map is a consequence of assignment of different spatial regions to variant cell populations in the framework of rate coding. The current study constructs a novel computational neural model of the cognitive map based on firing rate coding, as widely appears in associative memory, thus providing an explanation for formation and function of the two types of cognitive maps: the spatial vector map, responsible for self localization and simultaneous updating of detailed information; and the goal-oriented vector map, important in route finding. A proposed intermediate between these two map types was constructed by combining the spatial vector and goal-orientation maps to form an effective and efficient path finding mechanism. Application of such novel cognitive map based path finding methods to a mental exploration model was explored. With adaptation as a driving force, the basic knowledge of the location relationships in the spatial cognitive map was reformed and sent to the goal-oriented cognitive map, thus solving a series of new path problems through mental exploration. This method allows for rapid identification of suitable paths under variant conditions, thus providing a simpler and safer resource for path finding. Additionally, this method also provides an improved basis for potential robotic path finding applications.

A MODEL OF HIPPOCAMPAL MEMORY BASED ON AN ADAPTIVE LEARNING RULE OF SYNAPSES

We constructed a neural network of the hippocampus and proposed an adaptive learning rule of synapses to simulate the storing and retrieving processes of memory in the hippocampus by a mechanism of resonance. The hippocampus network consists of CA1, CA3 and DG, in particular, CA1 is a storage of memory, which receives inputs from both EC through perforant path (PP) and CA3 through Schaffer collaterals (SC). The stimulated results showed that the memory trace was unable to be encoded in CA1 when only a single subthreshold signal from EC or CA3 was inputted, of which the main reason might be lack of the resonance of the two signals. We calculated signal-to-noise ratio (SNR) of the network, and found it reached a peak value at appropriate SC connection strength, indicating that a typical stochastic resonance phenomenon appeared in PP signal detection. The inputs from EC and CA3 were able to enhance the memory representation in CA1, although still incomplete. We used a learning rule to modify synaptic weights by which the network could learn an external pattern. The hippocampus network tended to be stable after sufficient evolution. Some CA1 neurons show synchronized firings which are used to represent memory and are clearer than observed memory traces before learning. The model and results provide a good guidance to our understanding of the mechanism of the hippocampus memory.

Synaptic conditions for auto-associative memory storage and pattern completion in Jensen et al.'s model of hippocampal area CA3

Jensen et al. (Learn Memory 3(2-3):243-256, 1996b) proposed an auto-associative memory model using an integrated short-term memory (STM) and long-term memory (LTM) spiking neural network. Their model requires that distinct pyramidal cells encoding different STM patterns are fired in different high-frequency gamma subcycles within each low-frequency theta oscillation. Auto-associative LTM is formed by modifying the recurrent synaptic efficacy between pyramidal cells. In order to store auto-associative LTM correctly, the recurrent synaptic efficacy must be bounded. The synaptic efficacy must be upper bounded to prevent re-firing of pyramidal cells in subsequent gamma subcycles. If cells encoding one memory item were to re-fire synchronously with other cells encoding another item in subsequent gamma subcycle, LTM stored via modifiable recurrent synapses would be corrupted. The synaptic efficacy must also be lower bounded so that memory pattern completion can be performed correctly. This paper uses the original model by Jensen et al. as the basis to illustrate the following points. Firstly, the importance of coordinated long-term memory (LTM) synaptic modification. Secondly, the use of a generic mathematical formulation (spiking response model) that can theoretically extend the results to other spiking network utilizing threshold-fire spiking neuron model. Thirdly, the interaction of long-term and short-term memory networks that possibly explains the asymmetric distribution of spike density in theta cycle through the merger of STM patterns with interaction of LTM network.

Candidate hippocampal biomarkers of susceptibility and resilience to stress in a rat model of depression

Susceptibility to stress plays a crucial role in the development of psychiatric disorders such as unipolar depression and post-traumatic stress disorder. In the present study the chronic mild stress rat model of depression was used to reveal stress-susceptible and stress-resilient rats. Large-scale proteomics was used to map hippocampal protein alterations in different stress states. Membrane proteins were successfully captured by two-phase separation and peptide based proteomics. Using iTRAQ labeling coupled with mass spectrometry, more than 2000 proteins were quantified and 73 proteins were found to be differentially expressed. Stress susceptibility was associated with increased expression of a sodium-channel protein (SCN9A) currently investigated as a potential antidepressant target. Differential protein profiling also indicated stress susceptibility to be associated with deficits in synaptic vesicle release involving SNCA, SYN-1, and AP-3. Our results indicate that increased oxidative phosphorylation (COX5A, NDUFB7, NDUFS8, COX5B, and UQCRB) within the hippocampal CA regions is part of a stress-protection mechanism.

Task-dependent and independent synchronous activity of monkey hippocampal neurons in real and virtual translocation

Previous neurophysiological and behavioral studies relate hippocampal functions to place learning and memory, and encoding of task (or context)-specific information. Encoding of both task-specific information and own location is essential for episodic memory and for animals to navigate to reward-related places. It is suggested that different neural circuits with different assemblies of different hippocampal neurons are created in different environments or behavioral contexts for the hippocampal formation (HF) to encode and retrieve episodic memory. To investigate whether synchronous activity of hippocampal neurons, suggesting functional connectivity between those neurons, is task and position dependent, multiple single unit activities were recorded during performance of real and virtual translocation (VT) tasks. The monkey moved to one of four reward areas by driving a cab (real translocation) or by moving a pointer on a monitor. Of 163 neuron pairs, significant peaks in cross-correlograms (CCGs) were observed in 98 pairs. Most CCGs had positive peaks within 50 ms. Task-dependent cross-correlations (CCRs) were observed in 44% of the neuron pairs, and similarly observed in both the real and VT tasks. These CCRs were frequently observed in pyramidal vs. pyramidal neuron pairs with positive peak and peak shift. However, no consistent patterns of peak polarity, peak shift, and neuronal types were seen in task-independent CCRs. There was no significant difference in frequency of CCG peaks between real and VT tasks. These results suggest that the task-dependent information may be encoded by interaction among pyramidal neurons, and the common information across tasks may be encoded by interaction among pyramidal neurons and interneurons in the HF. These neuronal populations could provide a neural basis for episodic memory to disambiguously guide animals to places associated with reward in different situations.

A Kalman filtering approach to the representation of kinematic quantities by the hippocampal-entorhinal complex

Several regions of the brain which represent kinematic quantities are grouped under a single state-estimator framework. A theoretic effort is made to predict the activity of each cell population as a function of time using a simple state estimator (the Kalman filter). Three brain regions are considered in detail: the parietal cortex (reaching cells), the hippocampus (place cells and head-direction cells), and the entorhinal cortex (grid cells). For the reaching cell and place cell examples, we compute the perceived probability distributions of objects in the environment as a function of the observations. For the grid cell example, we show that the elastic behavior of the grids observed in experiments arises naturally from the Kalman filter. To our knowledge, the application of a tensor Kalman filter to grid cells is completely novel.

Reconciling the STDP and BCM models of synaptic plasticity in a spiking recurrent neural network

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

Spike-timing dependent plasticity and the cognitive map

Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent “theta-coded” temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Changes in the strength of synaptic connections between neurons are believed to mediate processes of learning and memory in the brain. A computational theory of this synaptic plasticity was first provided by Donald Hebb within the context of a more general neural coding mechanism, whereby phase sequences of activity directed by ongoing external and internal dynamics propagate in mutually exciting ensembles of neurons. Empirical evidence for this cell assembly model has been obtained in the hippocampus, where neuronal ensembles encoding for spatial location repeatedly fire in sequence at different phases of the ongoing theta oscillation. To investigate the encoding and reactivation of these dual coded activity patterns, we examine a biologically inspired spiking neural network model of the hippocampus with a novel synaptic plasticity rule. We demonstrate that this allows the rapid development of both symmetric and asymmetric connections between neurons that fire at concurrent or consecutive theta phase respectively. Recall activity, corresponding to both pattern completion and sequence prediction, can subsequently be produced by partial external cues. This allows the reconciliation of two previously disparate classes of hippocampal model and provides a framework for further examination of cell assembly dynamics in spiking neural networks.

Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding

The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.

Behavioral state-dependent episodic representations in rat CA1 neuronal activity during spatial alternation

Hippocampus is considered crucial for episodic memory, as confirmed by recent findings of "episode-dependent place cells" in rodent studies, and is known to show differential activity between active exploration and quiet immobility. Most place-cell studies have focused on active periods, so the hippocampal involvement in episodic representations is less well understood. Here, we draw a typology of episode-dependent hippocampal activity among three behavioral periods, presumably governed by different molecular mechanisms: Active exploration with type 1 theta, quiet alertness with type 2 theta, and consummation with large amplitude irregular activity. Five rats were trained to perform a delayed spatial alternation task with a nose-poke paradigm and 12 tetrodes were implanted for single-unit recordings. We obtained 135 CA1 pyramidal cells and found that 75 of these fired mainly during active exploration, whereas 42 fired mainly during quiet alertness and 18 during consummation. In each type of neuron, we found episode-dependent activity: 51/75, 22/42, and 15/18, respectively. These findings extend our knowledge on the hippocampal involvement in episodic memory: Episode dependency also exists during immobile periods, and functionally dissociated cell assemblies are engaged in the maintenance of episodic information throughout different events in a task sequence.

The role of competitive learning in the generation of DG fields from EC inputs

We follow up on a suggestion by Rolls and co-workers, that the effects of competitive learning should be assessed on the shape and number of spatial fields that dentate gyrus (DG) granule cells may form when receiving input from medial entorhinal cortex (mEC) grid units. We consider a simple non-dynamical model where DG units are described by a threshold-linear transfer function, and receive feedforward inputs from 1,000 mEC model grid units of various spacing, orientation and spatial phase. Feedforward weights are updated according to a Hebbian rule as the virtual rodent follows a long simulated trajectory through a single environment. Dentate activity is constrained to be very sparse. We find that indeed competitive Hebbian learning tends to result in a few active DG units with a single place field each, rounded in shape and made larger by iterative weight changes. These effects are more pronounced when produced with thousands of DG units and inputs per DG unit, which the realistic system has available, than with fewer units and inputs, in which case several DG units persists with multiple fields. The emergence of single-field units with learning is in contrast, however, to recent data indicating that most active DG units do have multiple fields. We show how multiple irregularly arranged fields can be produced by the addition of non-space selective lateral entorhinal cortex (lEC) units, which are modelled as simply providing an additional effective input specific to each DG unit. The mean number of such multiple DG fields is enhanced, in particular, when lEC and mEC inputs have overall similar variance across DG units. Finally, we show that in a restricted environment the mean size of the fields is unaltered, while their mean number is scaled down with the area of the environment.

LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise

Spike-timing-dependent synaptic plasticity (STDP) is a simple and effective learning rule for sequence learning. However, synapses being subject to STDP rules are readily influenced in noisy circumstances because synaptic conductances are modified by pre- and postsynaptic spikes elicited within a few tens of milliseconds, regardless of whether those spikes convey information or not. Noisy firing existing everywhere in the brain may induce irrelevant enhancement of synaptic connections through STDP rules and would result in uncertain memory encoding and obscure memory patterns. We will here show that the LTD windows of the STDP rules enable robust sequence learning amid background noise in cooperation with a large signal transmission delay between neurons and a theta rhythm, using a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections. The important element of the present model for robust sequence learning amid background noise is the symmetric STDP rule having LTD windows on both sides of the LTP window, in addition to the loop connections having a large signal transmission delay and the theta rhythm pacing activities of stellate cells. Above all, the LTD window in the range of positive spike-timing is important to prevent influences of noise with the progress of sequence learning.

The engram formation and the global oscillations of CA3

The investigation on the conditions which cause global population oscillatory activities in neural fields, originated some years ago with reference to a kinetic theory of neural systems, as been further deepened in this paper. In particular, the genesis of sharp waves and of some rhythmic activities, such as theta and gamma rhythms, of the hippocampal CA3 field, behaviorally important for their links to learning and memory, has been analyzed with more details. To this aim, the modeling-computational framework previously devised for the study of activities in large neural fields, has been enhanced in such a way that a greater number of biological features, extended dendritic trees-in particular, could be taken into account. By using that methodology, a two-dimensional model of the entire CA3 field has been described and its activity, as it results from the several external inputs impinging on it, has been simulated. As a consequence of these investigations, some hypotheses have been elaborated about the possible function of global oscillatory activities of neural populations of Hippocampus in the engram formation.

Transient cognitive dynamics, metastability, and decision making

The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years. One of the popular points of view is that metastable states play a key role in the execution of cognitive functions. Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise. Such transients may consist of a sequential switching between different metastable cognitive states. The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior. In this paper, we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states. The mathematical image of such transient activity is a stable heteroclinic channel, i.e., a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings. We suggest a basic mathematical model, a strongly dissipative dynamical system, and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility. Based on this approach, we describe here an effective solution for the problem of sequential decision making, represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward. As we predict and verify in computer simulations, noise plays an important role in optimizing the gain.

Theta phase precession emerges from a hybrid computational model of a CA3 place cell

The origins and functional significance of theta phase precession in the hippocampus remain obscure, in part, because of the difficulty of reproducing hippocampal place cell firing in experimental settings where the biophysical underpinnings can be examined in detail. The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell's place field. Following an initial training period, theta phase precession can be seen in the firing patterns of the model CA3 pyramidal cell. Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity. In the context of a single CA3 pyramidal cell, the model shows that each of these factors plays a role in theta phase precession within CA3 and suggests that no one single factor offers a complete explanation of the phenomenon. The model also shows parallels between theta phase encoding and pattern completion within the CA3 autoassociative network.

A unified view of theta-phase coding in the entorhinal–hippocampal system

The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the functional significance of temporal encoding in hippocampal memory. However, earlier theoretical studies on this topic seem divergent and experimental implications are invariably complicated. To obtain a unified understanding of neural dynamics in the hippocampal memory, we here review recent developments in computational models and experimental discoveries on the 'theta-phase precession' of hippocampal place cells and entorhinal grid cells. We identify a theoretical hypothesis that is well supported by experimental facts; this model reveals a significant contribution of theta-phase coding to the on-line real-time operation of episodic events, through highly parallel representation of spatiotemporal information.