Rapid Encoding of New Memories by Individual Neurons in the Human Brain

On the origin of memory neurons in the human hippocampus

The hippocampus is essential for episodic memory, yet its coding mechanism remains debated. In humans, two main theories have been proposed: one suggests that concept neurons represent specific elements of an episode, while another posits a conjunctive code, where index neurons code the entire episode. Here, we integrate new findings of index neurons in humans and other animals with the concept-specific memory framework, proposing that concept neurons evolve from index neurons through overlapping memories. This process is supported by engram literature, which posits that neurons are allocated to a memory trace based on excitability and that reactivation induces excitability. By integrating these insights, we connect two historically disparate fields of neuroscience: engram research and human single neuron episodic memory research.

A Theory and Model of Scene Representations With Hippocampal Spatial View Cells

A theory and network model are presented of how scene representations are built by forming spatial view cells in the ventromedial visual cortical scene pathway to the hippocampus in primates including humans. Layer 1, corresponding to V1-V4, connects to Layer 2 in the retrosplenial scene area and uses competitive learning to form visual feature combination neurons for the part of the scene being fixated, a visual fixation scene patch. In Layer 3, corresponding to the parahippocampal scene area and hippocampus, the visual fixation scene patches are stitched together to form whole scene representations. This is performed with a continuous attractor network for a whole scene made from the overlapping Gaussian receptive fields of the neurons as the head rotates to view the whole scene. In addition, in Layer 3, gain modulation by gaze direction maps visual fixation scene patches to the correct part of the whole scene representation when saccades are made. Each neuron in Layer 3 is thus a spatial view cell that responds to a location in a viewed scene based on visual features in a part of the scene. The novel conceptual advances are that this theory shows how scene representations may be built in primates, including humans, based on features in spatial scenes that anchor the scene representation to the world being viewed (to allocentric, world-based, space); and how gaze direction contributes to this. This offers a revolutionary approach to understanding the spatial representations for navigation and episodic memory in primates, including humans.

Reinstatement and transformation of memory traces for recognition

Episodic memory relies on the formation and retrieval of content-specific memory traces. In addition to their veridical reactivation, previous studies have indicated that traces may undergo substantial transformations. However, the exact time course and regional distribution of reinstatement and transformation during recognition memory have remained unclear. We applied representational similarity analysis to human intracranial electroencephalography to track the spatiotemporal dynamics underlying the reinstatement and transformation of memory traces. Specifically, we examined how reinstatement and transformation of item-specific representations across occipital, ventral visual, and lateral parietal cortices contribute to successful memory formation and recognition. Our findings suggest that reinstatement in temporal cortex and transformation in parietal cortex coexist and provide complementary strategies for recognition. Further, we find that generalization and differentiation of neural representations contribute to memory and probe memory-specific correspondence with deep neural network (DNN) model features. Our results suggest that memory formation is particularly supported by generalized and mnemonic representational formats beyond the visual features of a DNN.

Lack of context modulation in human single neuron responses in the medial temporal lobe

In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all neurons (97% during encoding and 100% during recall) initially responding to a person/place do not modulate their response with context. Likewise, nearly none (<1%) of the initially non-responsive neurons show conjunctive coding, responding to particular persons/places in a particular context during the tasks. In line with these findings, taking all neurons together it is possible to decode the person/place being depicted in each story, but not the particular story. Moreover, the neurons show consistent results across encoding and recall of the stories and during passive viewing of pictures. These results suggest a context invariant, non-conjunctive coding of memories at the single-neuron level in the human hippocampus and amygdala, in contrast to what has been described in other species.

Hippocampal Discoveries: Spatial View Cells, Connectivity, and Computations for Memory and Navigation, in Primates Including Humans

Two key series of discoveries about the hippocampus are described. One is the discovery of hippocampal spatial view cells in primates. This discovery opens the way to a much better understanding of human episodic memory, for episodic memory prototypically involves a memory of where people or objects or rewards have been seen in locations "out there" which could never be implemented by the place cells that encode the location of a rat or mouse. Further, spatial view cells are valuable for navigation using vision and viewed landmarks, and provide for much richer, vision-based, navigation than the place to place self-motion update performed by rats and mice who live in dark underground tunnels. Spatial view cells thus offer a revolution in our understanding of the functions of the hippocampus in memory and navigation in humans and other primates with well-developed foveate vision. The second discovery describes a computational theory of the hippocampal-neocortical memory system that includes the only quantitative theory of how information is recalled from the hippocampus to the neocortex. It is shown how foundations for this research were the discovery of reward neurons for food reward, and non-reward, in the primate orbitofrontal cortex, and representations of value including of monetary value in the human orbitofrontal cortex; and the discovery of face identity and face expression cells in the primate inferior temporal visual cortex and how they represent transform-invariant information. This research illustrates how in order to understand a brain computation, a whole series of integrated interdisciplinary discoveries is needed to build a theory of the operation of each neural system.

Fast-slow dynamics in a memristive ion channel-based bionic circuit

Electrophysiological properties of ion channels can influence the transport process of ions and the generation of firing patterns in an excitable biological neuron when applying an external stimulus and exceeding the excitable threshold. In this paper, a current stimulus is employed to emulate the external stimulus, and a second-order locally active memristor (LAM) is deployed to characterize the properties of ion channels. Then, a simple bionic circuit possessing the LAM, a capacitor, a DC voltage, and the current stimulus is constructed. Fast-slow dynamical effects of the current stimulus with low- and high-frequency are respectively explored. Numerical simulations disclose that the bionic circuit can generate bursting behaviors for the low-frequency current stimulus and spiking behaviors for the high-frequency current stimulus. Besides, fold and Hopf bifurcation sets are deduced and the bifurcation mechanisms for bursting behaviors are elaborated. Furthermore, the numerically simulated bursting and spiking behaviors are verified by PCB-based hardware experiments. These results reflect the feasibility of the bionic circuit in generating the firing patterns of spiking and bursting behaviors and the external current can be employed to regulate these firing patterns.

Human hippocampal and entorhinal neurons encode the temporal structure of experience

Extracting the underlying temporal structure of experience is a fundamental aspect of learning and memory that allows us to predict what is likely to happen next. Current knowledge about the neural underpinnings of this cognitive process in humans stems from functional neuroimaging research1-5. As these methods lack direct access to the neuronal level, it remains unknown how this process is computed by neurons in the human brain. Here we record from single neurons in individuals who have been implanted with intracranial electrodes for clinical reasons, and show that human hippocampal and entorhinal neurons gradually modify their activity to encode the temporal structure of a complex image presentation sequence. This representation was formed rapidly, without providing specific instructions to the participants, and persisted when the prescribed experience was no longer present. Furthermore, the structure recovered from the population activity of hippocampal-entorhinal neurons closely resembled the structural graph defining the sequence, but at the same time, also reflected the probability of upcoming stimuli. Finally, learning of the sequence graph was related to spontaneous, time-compressed replay of individual neurons' activity corresponding to previously experienced graph trajectories. These findings demonstrate that neurons in the hippocampus and entorhinal cortex integrate the 'what' and 'when' information to extract durable and predictive representations of the temporal structure of human experience.

Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities

Objective. Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations.Approach. We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly.Main results. We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies.Significance. The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases.

Selective Brain Activations and Connectivities Related to the Storage and Recall of Human Object-Location, Reward-Location, and Word-Pair Episodic Memories

Different cortical systems to the hippocampus were activated using fMRI during different types of episodic memory task. For object with scene location episodic memory, the activations were high in cortical systems involved in spatial processing, including the ventromedial visual and medial parahippocampal system. These activations for the medial parahippocampal system were higher in the right hemisphere. The activations in the face and object processing ventrolateral visual cortical stream regions FFC, PIT, V8 and TE2p were higher in the object-location in scene task than the reward-location task, and were higher in the right hemisphere. For reward-location in scene episodic memory, activations were also high in the ventromedial visual cortical spatial stream to the hippocampus, but were also selectively high in storage in key reward cortical regions (ventromedial prefrontal 10r, 10v, 10d; pregenual anterior cingulate d32, p24, p32, s32; and medial orbitofrontal cortex reward-related pOFC, 11l, OFC). For word-pair episodic memory, activations were lower in the ventromedial visual and medial parahippocampal spatial cortical stream, and were higher in language-related regions in Broca's area (44, 45, 47l), and were higher in the left hemisphere for these regions and for the many highly connected inferior frontal gyrus regions in the left hemisphere. Further, effective connectivity analyses during the episodic memory tasks showed that the direction of connectivity for these systems was from early visual cortical regions V2-V4 to the ventromedial visual cortical regions VMV1-3 and VVC for spatial scene processing; was from the pregenual anterior cingulate and orbitofrontal cortex reward systems to the hippocampal system; and was from the FFC/V8/PIT system to TE2p in the visual inferior temporal visual cortex, which has connectivity to lateral parahippocampal TF, which in turn has forward effective connectivity to the hippocampus.

A preserved neural code for temporal order between memory formation and recall in the human medial temporal lobe

Temporal memory enables us to remember the temporal order of events happening in our life. The human medial temporal lobe (MTL) appears to contain neural representations supporting temporal memory formation, but the cellular mechanisms that preserve temporal order information for recall are largely unknown. Here, we examined whether human MTL neuronal activity represents the temporal position of events during memory formation and recall, using invasive single and multi-unit recordings in human epilepsy patients (n = 19). Participants freely navigated a virtual environment in order to explore and remember locations and temporal positions of objects. During each exploration period, they sequentially encountered two or three different objects, placed in different locations. This allowed us to examine single- and multi-unit neuronal firing rates (FR) as a function of the temporal position the objects were presented in. We found that a significant number of multi-units and single-units in various MTL regions including the hippocampus showed selectivity to the temporal position of objects during the exploration period. During recall, patients were asked to indicate which one of two objects from the same trial was found latter. Neural firing rates during recall showed a selectivity supporting recall of temporal positions. Interestingly, most of the selective single-units that stayed selective during encoding and recall preserved their temporal position preference. Our results thus suggest that neuronal activity in the human MTL contains a preserved neural code for temporal order in memory formation and recall.

Pronouns reactivate conceptual representations in human hippocampal neurons

During discourse comprehension, every new word adds to an evolving representation of meaning that accumulates over consecutive sentences and constrains the next words. To minimize repetition and utterance length, languages use pronouns, like the word "she," to refer to nouns and phrases that were previously introduced. It has been suggested that language comprehension requires that pronouns activate the same neuronal representations as the nouns themselves. We recorded from individual neurons in the human hippocampus during a reading task. Cells that were selective to a particular noun were later reactivated by pronouns that refer to the cells' preferred noun. These results imply that concept cells contribute to a rapid and dynamic semantic memory network that is recruited during language comprehension.

Concept and location neurons in the human brain provide the 'what' and 'where' in memory formation

Our brains create new memories by capturing the 'who/what', 'where' and 'when' of everyday experiences. On a neuronal level, mechanisms facilitating a successful transfer into episodic memory are still unclear. We investigated this by measuring single neuron activity in the human medial temporal lobe during encoding of item-location associations. While previous research has found predictive effects in population activity in human MTL structures, we could attribute such effects to two specialized sub-groups of neurons: concept cells in the hippocampus, amygdala and entorhinal cortex (EC), and a second group of parahippocampal location-selective neurons. In both item- and location-selective populations, firing rates were significantly higher during successfully encoded trials. These findings are in line with theories of hippocampal indexing, since selective index neurons may act as pointers to neocortical representations. Overall, activation of distinct populations of neurons could directly support the connection of the 'what' and 'where' of episodic memory.

Investigating the effects of context, visual working memory, and inhibitory control in hybrid visual search

Introduction

In real-life scenarios, individuals frequently engage in tasks that involve searching for one of the distinct items stored in memory. This combined process of visual search and memory search is known as hybrid search. To date, most hybrid search studies have been restricted to average observers looking for previously well-memorized targets in blank backgrounds.

Methods

We investigated the effects of context and the role of memory in hybrid search by modifying the task's memorization phase to occur in all-new single trials. In addition, we aimed to assess how individual differences in visual working memory capacity and inhibitory control influence performance during hybrid search. In an online experiment, 110 participants searched for potential targets in images with and without context. A change detection and go/no-go task were also performed to measure working memory capacity and inhibitory control, respectively.

Results

We show that, in target present trials, the main hallmarks of hybrid search remain present, with a linear relationship between reaction time and visual set size and a logarithmic relationship between reaction time and memory set size. These behavioral results can be reproduced by using a simple drift-diffusion model. Finally, working memory capacity did not predict most search performance measures. Inhibitory control, when relationships were significant, could account for only a small portion of the variability in the data.

Discussion

This study provides insights into the effects of context and individual differences on search efficiency and termination.

A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

The neuroscience of active learning and direct instruction

Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.

Neural coding of space by time

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns.

A theory of hippocampal function: New developments

We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.

Two what, two where, visual cortical streams in humans

ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.

Mouse hippocampal CA1 VIP interneurons detect novelty in the environment and support recognition memory

In the CA1 hippocampus, vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) play a prominent role in disinhibitory circuit motifs. However, the specific behavioral conditions that lead to circuit disinhibition remain uncertain. To investigate the behavioral relevance of VIP-IN activity, we employed wireless technologies allowing us to monitor and manipulate their function in freely behaving mice. Our findings reveal that, during spatial exploration in new environments, VIP-INs in the CA1 hippocampal region become highly active, facilitating the rapid encoding of novel spatial information. Remarkably, both VIP-INs and pyramidal neurons (PNs) exhibit increased activity when encountering novel changes in the environment, including context- and object-related alterations. Concurrently, somatostatin- and parvalbumin-expressing inhibitory populations show an inverse relationship with VIP-IN and PN activity, revealing circuit disinhibition that occurs on a timescale of seconds. Thus, VIP-IN-mediated disinhibition may constitute a crucial element in the rapid encoding of novelty and the acquisition of recognition memory.

A Computational Framework for Memory Engrams

Memory engrams in mice brains are potentially related to groups of concept cells in human brains. A single concept cell in human hippocampus responds, for example, not only to different images of the same object or person but also to its name written down in characters. Importantly, a single mental concept (object or person) is represented by several concept cells and each concept cell can respond to more than one concept. Computational work shows how mental concepts can be embedded in recurrent artificial neural networks as memory engrams and how neurons that are shared between different engrams can lead to associations between concepts. Therefore, observations at the level of neurons can be linked to cognitive notions of memory recall and association chains between memory items.

A dynamic attractor network model of memory formation, reinforcement and forgetting

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.

Minute-scale periodicity of neuronal firing in the human entorhinal cortex

Grid cells in the entorhinal cortex demonstrate spatially periodic firing, thought to provide a spatial map on behaviorally relevant length scales. Whether such periodicity exists for behaviorally relevant time scales in the human brain remains unclear. We investigate neuronal firing during a temporally continuous experience by presenting 14 neurosurgical patients with a video while recording neuronal activity from multiple brain regions. We report on neurons that modulate their activity in a periodic manner across different time scales-from seconds to many minutes, most prevalently in the entorhinal cortex. These neurons remap their dominant periodicity to shorter time scales during a subsequent recognition memory task. When the video is presented at two different speeds, a significant percentage of these temporally periodic cells (TPCs) maintain their time scales, suggesting a degree of invariance. The TPCs' temporal periodicity might complement the spatial periodicity of grid cells and together provide scalable spatiotemporal metrics for human experience.

Hippocampal neurons code individual episodic memories in humans

The hippocampus is an essential hub for episodic memory processing. However, how human hippocampal single neurons code multi-element associations remains unknown. In particular, it is debated whether each hippocampal neuron represents an invariant element within an episode or whether single neurons bind together all the elements of a discrete episodic memory. Here we provide evidence for the latter hypothesis. Using single-neuron recordings from a total of 30 participants, we show that individual neurons, which we term episode-specific neurons, code discrete episodic memories using either a rate code or a temporal firing code. These neurons were observed exclusively in the hippocampus. Importantly, these episode-specific neurons do not reflect the coding of a particular element in the episode (that is, concept or time). Instead, they code for the conjunction of the different elements that make up the episode.

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.

Using multi-task experiments to test principles of hippocampal function

Investigations of hippocampal functions have revealed a dizzying array of findings, from lesion-based behavioral deficits, to a diverse range of characterized neural activations, to computational models of putative functionality. Across these findings, there remains an ongoing debate about the core function of the hippocampus and the generality of its representation. Researchers have debated whether the hippocampus's primary role relates to the representation of space, the neural basis of (episodic) memory, or some more general computation that generalizes across various cognitive domains. Within these different perspectives, there is much debate about the nature of feature encodings. Here, we suggest that in order to evaluate hippocampal responses-investigating, for example, whether neuronal representations are narrowly targeted to particular tasks or if they subserve domain-general purposes-a promising research strategy may be the use of multi-task experiments, or more generally switching between multiple task contexts while recording from the same neurons in a given session. We argue that this strategy-when combined with explicitly defined theoretical motivations that guide experiment design-could be a fruitful approach to better understand how hippocampal representations support different behaviors. In doing so, we briefly review key open questions in the field, as exemplified by articles in this special issue, as well as previous work using multi-task experiments, and extrapolate to consider how this strategy could be further applied to probe fundamental questions about hippocampal function.

An integrative view of human hippocampal function: Differences with other species and capacity considerations

We describe an integrative model that encodes associations between related concepts in the human hippocampal formation, constituting the skeleton of episodic memories. The model, based on partially overlapping assemblies of "concept cells," contrast markedly with the well-established notion of pattern separation, which relies on conjunctive, context dependent single neuron responses, instead of the invariant, context independent responses found in the human hippocampus. We argue that the model of partially overlapping assemblies is better suited to cope with memory capacity limitations, that the finding of different types of neurons and functions in this area is due to a flexible and temporary use of the extraordinary machinery of the hippocampus to deal with the task at hand, and that only information that is relevant and frequently revisited will consolidate into long-term hippocampal representations, using partially overlapping assemblies. Finally, we propose that concept cells are uniquely human and that they may constitute the neuronal underpinnings of cognitive abilities that are much further developed in humans compared to other species.

Interaction between Theta Phase and Spike Timing-Dependent Plasticity Simulates Theta-Induced Memory Effects

Rodent studies suggest that spike timing relative to hippocampal theta activity determines whether potentiation or depression of synapses arise. Such changes also depend on spike timing between presynaptic and postsynaptic neurons, known as spike timing-dependent plasticity (STDP). STDP, together with theta phase-dependent learning, has inspired several computational models of learning and memory. However, evidence to elucidate how these mechanisms directly link to human episodic memory is lacking. In a computational model, we modulate long-term potentiation (LTP) and long-term depression (LTD) of STDP, by opposing phases of a simulated theta rhythm. We fit parameters to a hippocampal cell culture study in which LTP and LTD were observed to occur in opposing phases of a theta rhythm. Further, we modulated two inputs by cosine waves with 0° and asynchronous phase offsets and replicate key findings in human episodic memory. Learning advantage was found for the in-phase condition, compared with the out-of-phase conditions, and was specific to theta-modulated inputs. Importantly, simulations with and without each mechanism suggest that both STDP and theta phase-dependent plasticity are necessary to replicate the findings. Together, the results indicate a role for circuit-level mechanisms, which bridge the gap between slice preparation studies and human memory.

Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells-neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons' sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series "24". First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare "computer vision" with "neuronal vision"-footprints associated with each character present in the activity of a subset of neurons-and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants' subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL.

Significance of visual scene-based learning in the hippocampal systems across mammalian species

The hippocampus and its associated cortical regions in the medial temporal lobe play essential roles when animals form a cognitive map and use it to achieve their goals. As the nature of map-making involves sampling different local views of the environment and putting them together in a spatially cohesive way, visual scenes are essential ingredients in the formative process of cognitive maps. Visual scenes also serve as important cues during information retrieval from the cognitive map. Research in humans has shown that there are regions in the brain that selectively process scenes and that the hippocampus is involved in scene-based memory tasks. The neurophysiological correlates of scene-based information processing in the hippocampus have been reported as "spatial view cells" in nonhuman primates. Like primates, it is widely accepted that rodents also use visual scenes in their background for spatial navigation and other kinds of problems. However, in rodents, it is not until recently that researchers examined the neural correlates of the hippocampus from the perspective of visual scene-based information processing. With the advent of virtual reality (VR) systems, it has been demonstrated that place cells in the hippocampus exhibit remarkably similar firing correlates in the VR environment compared with that of the real-world environment. Despite some limitations, the new trend of studying hippocampal functions in a visually controlled environment has the potential to allow investigation of the input-output relationships of network functions and experimental testing of traditional computational predictions more rigorously by providing well-defined visual stimuli. As scenes are essential for navigation and episodic memory in humans, further investigation of the rodents' hippocampal systems in scene-based tasks will provide a critical functional link across different mammalian species.

Non-spatial similarity can bias spatial distances in a cognitive map

The cognitive map theory suggests the hippocampal-entorhinal system has a representation of space that encodes geometric properties. There is also evidence that the hippocampus plays a critical role in supporting declarative memory, and recent theories have hypothesized the mechanism for encoding space is the same as that for processing memory. If space is not represented independently, it might be influenced by non-spatial properties. This study tested whether connections between non-spatial properties can distort judgments about spatial distance. In virtual reality, subjects navigated through an environment to learn the locations of target houses, and then were tested on their ability to judge the pairwise distances between houses and reconstruct a map of the environment. The environment was constructed to have pairs of houses with the same spatial distance but either the same or different color. If memory for spatial and non-spatial properties interact, similar houses would be expected to be judged as closer. In Experiment 1, the similar pairs all had the same color, while in Experiment 2, each pair had a different color to make the pairs more distinctive. We observed that similar houses were drawn closer on reconstructed maps in both experiments, and pairwise distance judgments were smaller for similar houses in Experiment 2. Biases from color similarity are difficult to reconcile with independent representation of space. Our results support theories that space is represented with other properties, and the mechanisms for encoding space in the hippocampal-entorhinal system have a broader function.

Dynamic branching in a neural network model for probabilistic prediction of sequences

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.

MMM - The molecular model of memory

Identifying mechanisms underlying neurons ability to process information including acquisition, storage, and retrieval plays an important role in the understanding of the different types of memory, pathogenesis of many neurological diseases affecting memory and therapeutic target discovery. However, the traditional understanding of the mechanisms of memory associated with the electrical signals having a unique combination of frequency and amplitude does not answer the question how the memories can survive for life-long periods of time, while exposed to synaptic noise. Recent evidence suggests that, apart from neuronal circuits, a diversity of the molecular memory (MM) carriers, are essential for memory performance. The molecular model of memory (MMM) is proposed, according to which each item of incoming information (the elementary memory item - eMI) is encoded by both circuitries, with the unique for a given MI electrical parameters, and also the MM carriers, unique by its molecular composition. While operating as the carriers of incoming information, the MMs, are functioning within the neuron plasma membrane. Inactive (latent) initially, during acquisition each of the eMIs is activated to become a virtual copy of some real fact or events bygone. This activation is accompanied by the considerable remodeling of the MM molecule associated with the resonance effect.

Two kinds of memory signals in neurons of the human hippocampus

SignificanceEpisodic memories represent the "what," "when," and "where" of specific episodes. In epilepsy patients, we detected single-unit activity reflecting episodic memory only in the hippocampus. This neural signal is sparsely coded and pattern-separated, consistent with predictions from neurocomputational models. We also detected single-unit activity reflecting a generic memory signal, coding whether an item is old or new without item-specific episodic information. Similar to concept cells, this generic repetition/novelty neural signal was found in multiple brain regions, including the hippocampus. In contrast, the item-specific signal was found only in the hippocampus. Our results indicate the coexistence of two memory signals in the human brain and suggest that the sparsely coded, hippocampus-specific signal is fundamental, whereas the often-studied generic signal is derivative.

Multiscale and Extended Retrieval of Associative Memory Structures in a Cortical Model of Local-Global Inhibition Balance

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and nonsparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their subtypes uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory subpopulations, one connected to excitatory assemblies locally and the other connected globally, can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model, while still theoretical, therefore highlights a potentially biologically-plausible and behaviorally-useful function of inhibitory diversity in memory.

Neurons as will and representation

Memory recollections and voluntary actions are often perceived as spontaneously generated irrespective of external stimuli. Although products of our neurons, they are only rarely accessible in humans at the neuronal level. Here I review insights gleaned from unique neurosurgical opportunities to record and stimulate single-neuron activity in people who can declare their thoughts, memories and wishes. I discuss evidence that the subjective experience of human recollection and that of voluntary action arise from the activity of two internal neuronal generators, the former from medial temporal lobe reactivation and the latter from frontoparietal preactivation. I characterize properties of these generators and their interaction, enabling flexible recruitment of memory-based choices for action as well as recruitment of action-based plans for the representation of conceptual knowledge in memories. Both internal generators operate on surprisingly explicit but different neuronal codes, which appear to arise with distinct single-neuron activity, often observed before participants' reports of conscious awareness. I discuss prediction of behaviour based on these codes, and the potential for their modulation. The prospects of editing human memories and volitions by enhancement, inception or deletion of specific, selected content raise therapeutic possibilities and ethical concerns.

Oscillations support short latency co-firing of neurons during human episodic memory formation

Theta and gamma oscillations in the medial temporal lobe are suggested to play a critical role for human memory formation via establishing synchrony in neural assemblies. Arguably, such synchrony facilitates efficient information transfer between neurons and enhances synaptic plasticity, both of which benefit episodic memory formation. However, to date little evidence exists from humans that would provide direct evidence for such a specific role of theta and gamma oscillations for episodic memory formation. Here, we investigate how oscillations shape the temporal structure of neural firing during memory formation in the medial temporal lobe. We measured neural firing and local field potentials in human epilepsy patients via micro-wire electrode recordings to analyze whether brain oscillations are related to co-incidences of firing between neurons during successful and unsuccessful encoding of episodic memories. The results show that phase-coupling of neurons to faster theta and gamma oscillations correlates with co-firing at short latencies (~20-30 ms) and occurs during successful memory formation. Phase-coupling at slower oscillations in these same frequency bands, in contrast, correlates with longer co-firing latencies and occurs during memory failure. Thus, our findings suggest that neural oscillations play a role for the synchronization of neural firing in the medial temporal lobe during the encoding of episodic memories.

When shared concept cells support associations: Theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction c_min of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction c_max of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds.

Experimental evidence suggests that associations between concepts are encoded in the hippocampus by cells shared between neuronal assemblies (“overlap” of concepts). What is the necessary overlap that ensures a reliable encoding of associations? Under which conditions can associations induce a simultaneous or a chain-like activation of concepts? Our theoretical model shows that the ideal overlap presents a tradeoff: the overlap should be larger than a minimum value in order to reliably encode associations, but lower than a maximum value to prevent loss of individual memories. Our theory explains experimental data from human Medial Temporal Lobe and provides a mechanism for chain-like recall in presence of inhibition, while still allowing for simultaneous recall if inhibition is weak.

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.

From Counting to Retrieving: Neural Networks Underlying Alphabet Arithmetic Learning

This fMRI study aimed at unraveling the neural basis of learning alphabet arithmetic facts, as a proxy of the transition from slow and effortful procedural counting-based processing to fast and effortless processing as it occurs in learning addition arithmetic facts. Neural changes were tracked while participants solved alphabet arithmetic problems in a verification task (e.g., F + 4 = J). Problems were repeated across four learning blocks. Two neural networks with opposed learning-related changes were identified. Activity in a network consisting of basal ganglia and parieto-frontal areas decreased with learning, which is in line with a reduction of the involvement of procedure-based processing. Conversely, activity in a network involving the left angular gyrus and, to a lesser extent, the hippocampus gradually increases with learning, evidencing the gradual involvement of retrieval-based processing. Connectivity analyses gave insight in the functional relationship between the two networks. Despite the opposing learning-related trajectories, it was found that both networks become more integrated. Taking alphabet arithmetic as a proxy for learning arithmetic, the present results have implications for current theories of learning arithmetic facts and can give direction to future developments.

Bridging the Gap Between Neurons and Cognition Through Assemblies of Neurons

During recent decades, our understanding of the brain has advanced dramatically at both the cellular and molecular levels and at the cognitive neurofunctional level; however, a huge gap remains between the microlevel of physiology and the macrolevel of cognition. We propose that computational models based on assemblies of neurons can serve as a blueprint for bridging these two scales. We discuss recently developed computational models of assemblies that have been demonstrated to mediate higher cognitive functions such as the processing of simple sentences, to be realistically realizable by neural activity, and to possess general computational power.

Neurons in the human medial temporal lobe track multiple temporal contexts during episodic memory processing

Episodic memory requires associating items with temporal context, a process for which the medial temporal lobe (MTL) is critical. This study uses recordings from 27 human subjects who were undergoing surgical intervention for intractable epilepsy. These same data were also utilized in Umbach et al. (2020). We identify 103 memory-sensitive neurons in the hippocampus and entorhinal cortex, whose firing rates predicted successful episodic memory encoding as subjects performed a verbal free recall task. These neurons exhibit important properties. First, as predicted from the temporal context model, they demonstrate reinstatement of firing patterns observed during encoding at the time of retrieval. The magnitude of reinstatement predicted the tendency of subjects to cluster retrieved memory items according to input serial position. Also, we found that spiking activity of these neurons was locked to the phase of hippocampal theta oscillations, but that the mean phase of spiking shifted between memory encoding versus retrieval. This unique observation is consistent with predictions of the “Separate Phases at Encoding And Retrieval (SPEAR)” model. Together, the properties we identify for memory-sensitive neurons characterize direct electrophysiological mechanisms for the representation of contextual information in the human MTL.

Concept neurons in the human medial temporal lobe flexibly represent abstract relations between concepts

Concept neurons in the medial temporal lobe respond to semantic features of presented stimuli. Analyzing 61 concept neurons recorded from twelve patients who underwent surgery to treat epilepsy, we show that firing patterns of concept neurons encode relations between concepts during a picture comparison task. Thirty-three of these responded to non-preferred stimuli with a delayed but well-defined onset whenever the task required a comparison to a response-eliciting concept, but not otherwise. Supporting recent theories of working memory, concept neurons increased firing whenever attention was directed towards this concept and could be reactivated after complete activity silence. Population cross-correlations of pairs of concept neurons exhibited order-dependent asymmetric peaks specifically when their response-eliciting concepts were to be compared. Our data are consistent with synaptic mechanisms that support reinstatement of concepts and their relations after activity silence, flexibly induced through task-specific sequential activation. This way arbitrary contents of experience could become interconnected in both working and long-term memory.

It is unclear how distinct concepts are processed in the brain. Here, the authors recorded from concept cells in human subjects with epilepsy and found that a subset of concept cells responded to non-preferred concepts if those non-preferred concepts required comparison to a preferred concept.

Switch-like and persistent memory formation in individual Drosophila larvae

Associative learning allows animals to use past experience to predict future events. The circuits underlying memory formation support immediate and sustained changes in function, often in response to a single example. Larval Drosophila is a genetic model for memory formation that can be accessed at molecular, synaptic, cellular, and circuit levels, often simultaneously, but existing behavioral assays for larval learning and memory do not address individual animals, and it has been difficult to form long-lasting memories, especially those requiring synaptic reorganization. We demonstrate a new assay for learning and memory capable of tracking the changing preferences of individual larvae. We use this assay to explore how activation of a pair of reward neurons changes the response to the innately aversive gas carbon dioxide (CO2). We confirm that when coupled to CO2 presentation in appropriate temporal sequence, optogenetic reward reduces avoidance of CO2. We find that learning is switch-like: all-or-none and quantized in two states. Memories can be extinguished by repeated unrewarded exposure to CO2 but are stabilized against extinction by repeated training or overnight consolidation. Finally, we demonstrate long-lasting protein synthesis dependent and independent memory formation.

A fast link between face perception and memory in the temporal pole

The question of how the brain recognizes the faces of familiar individuals has been important throughout the history of neuroscience. Cells linking visual processing to person memory have been proposed but not found. Here, we report the discovery of such cells through recordings from an area in the macaque temporal pole identified with functional magnetic resonance imaging. These cells responded to faces that were personally familiar. They responded nonlinearly to stepwise changes in face visibility and detail and holistically to face parts, reflecting key signatures of familiar face recognition. They discriminated between familiar identities, as fast as a general face identity area. The discovery of these cells establishes a new pathway for the fast recognition of familiar individuals.

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

No consolidation without representation: Correspondence between neural and psychological representations in recent and remote memory

Memory systems consolidation is often conceived as the linear, time-dependent, neurobiological shift of memory from hippocampal-cortical to cortico-cortical dependency. We argue that contrary to this unidirectional view of memory reorganization, information about events may be retained in multiple forms (e.g., event-specific sensory-near episodic memory, event-specific gist information, event-general schematic information, or abstract semantic memory). These representations can all form at the time of the event and may continue to coexist for long durations. Their relative strength, composition, and dominance of expression change with time and experience, with task demands, and through their dynamic interaction with one another. These different psychological mnemonic representations depend on distinct functional and structural neurobiological substrates such that there is a neural-psychological representation correspondence (NPRC) among them. We discuss how the dynamics of psychological memory representations are reflected in multiple levels of neurobiological markers and their interactions. By this view, there are only variations of synaptic consolidation and memory dynamics without assuming a distinct systems consolidation process.

The Sync-Fire/deSync model: Modelling the reactivation of dynamic memories from cortical alpha oscillations

We propose a neural network model to explore how humans can learn and accurately retrieve temporal sequences, such as melodies, movies, or other dynamic content. We identify target memories by their neural oscillatory signatures, as shown in recent human episodic memory paradigms. Our model comprises three plausible components for the binding of temporal content, where each component imposes unique limitations on the encoding and representation of that content. A cortical component actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding component converts each event into a discrete index, enabling repetitions through a sparse encoding of events. A timing component - consisting of an oscillatory "ticking clock" made up of hierarchical synfire chains - discretely indexes a moment in time. By encoding the absolute timing between discretised events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are reactivated in the brain. We validate this model by simulating a series of events where sequences are uniquely identifiable by analysing phasic information, as several recent EEG/MEG studies have shown. As such, we show how one can encode and retrieve complete episodic memories where the quality of such memories is modulated by the following: alpha gate keepers to content representation; binding limitations that induce a blink in temporal perception; and nested oscillations that provide preferential learning phases in order to temporally sequence events.

How Human Single-Neuron Recordings Can Help Us Understand Cognition: Insights from Memory Studies

Understanding human cognition is a key goal of contemporary neuroscience. Due to the complexity of the human brain, animal studies and noninvasive techniques, however valuable, are incapable of providing us with a full understanding of human cognition. In the light of existing cognitive theories, we describe findings obtained thanks to human single-neuron recordings, including the discovery of concept cells and novelty-dependent cells, or activity patterns behind working memory, such as persistent activity. We propose future directions for studies using human single-neuron recordings and we discuss possible opportunities of investigating pathological brain.

Neurons including hippocampal spatial view cells, and navigation in primates including humans

A new theory is proposed of mechanisms of navigation in primates including humans in which spatial view cells found in the primate hippocampus and parahippocampal gyrus are used to guide the individual from landmark to landmark. The navigation involves approach to each landmark in turn (taxis), using spatial view cells to identify the next landmark in the sequence, and does not require a topological map. Two other cell types found in primates, whole body motion cells, and head direction cells, can be utilized in the spatial view cell navigational mechanism, but are not essential. If the landmarks become obscured, then the spatial view representations can be updated by self-motion (idiothetic) path integration using spatial coordinate transform mechanisms in the primate dorsal visual system to transform from egocentric to allocentric spatial view coordinates. A continuous attractor network or time cells or working memory is used in this approach to navigation to encode and recall the spatial view sequences involved. I also propose how navigation can be performed using a further type of neuron found in primates, allocentric-bearing-to-a-landmark neurons, in which changes of direction are made when a landmark reaches a particular allocentric bearing. This is useful if a landmark cannot be approached. The theories are made explicit in models of navigation, which are then illustrated by computer simulations. These types of navigation are contrasted with triangulation, which requires a topological map. It is proposed that the first strategy utilizing spatial view cells is used frequently in humans, and is relatively simple because primates have spatial view neurons that respond allocentrically to locations in spatial scenes. An advantage of this approach to navigation is that hippocampal spatial view neurons are also useful for episodic memory, and for imagery.