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

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.

Exploring the Structure of Spatial Representations

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory.

Neural network modelling of the influence of channelopathies on reflex visual attention

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network's rate of failure to shift attention is lower than the control network's, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.

Not only … but also: REM sleep creates and NREM Stage 2 instantiates landmark junctions in cortical memory networks

This article argues both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep contribute to overnight episodic memory processes but their roles differ. Episodic memory may have evolved from memory for spatial navigation in animals and humans. Equally, mnemonic navigation in world and mental space may rely on fundamentally equivalent processes. Consequently, the basic spatial network characteristics of pathways which meet at omnidirectional nodes or junctions may be conserved in episodic brain networks. A pathway is formally identified with the unidirectional, sequential phases of an episodic memory. In contrast, the function of omnidirectional junctions is not well understood. In evolutionary terms, both animals and early humans undertook tours to a series of landmark junctions, to take advantage of resources (food, water and shelter), whilst trying to avoid predators. Such tours required memory for emotionally significant landmark resource-place-danger associations and the spatial relationships amongst these landmarks. In consequence, these tours may have driven the evolution of both spatial and episodic memory. The environment is dynamic. Resource-place associations are liable to shift and new resource-rich landmarks may be discovered, these changes may require re-wiring in neural networks. To realise these changes, REM may perform an associative, emotional encoding function between memory networks, engendering an omnidirectional landmark junction which is instantiated in the cortex during NREM Stage 2. In sum, REM may preplay associated elements of past episodes (rather than replay individual episodes), to engender an unconscious representation which can be used by the animal on approach to a landmark junction in wake.

A computational predictor of human episodic memory based on a theta phase precession network

In the rodent hippocampus, a phase precession phenomena of place cell firing with the local field potential (LFP) theta is called “theta phase precession” and is considered to contribute to memory formation with spike time dependent plasticity (STDP). On the other hand, in the primate hippocampus, the existence of theta phase precession is unclear. Our computational studies have demonstrated that theta phase precession dynamics could contribute to primate–hippocampal dependent memory formation, such as object–place association memory. In this paper, we evaluate human theta phase precession by using a theory–experiment combined analysis. Human memory recall of object–place associations was analyzed by an individual hippocampal network simulated by theta phase precession dynamics of human eye movement and EEG data during memory encoding. It was found that the computational recall of the resultant network is significantly correlated with human memory recall performance, while other computational predictors without theta phase precession are not significantly correlated with subsequent memory recall. Moreover the correlation is larger than the correlation between human recall and traditional experimental predictors. These results indicate that theta phase precession dynamics are necessary for the better prediction of human recall performance with eye movement and EEG data. In this analysis, theta phase precession dynamics appear useful for the extraction of memory-dependent components from the spatio–temporal pattern of eye movement and EEG data as an associative network. Theta phase precession may be a common neural dynamic between rodents and humans for the formation of environmental memories.

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.