Encoding of long-term associations through neural unitization in the human medial temporal lobe

A neuronal code for object representation and memory in the human amygdala and hippocampus

How the brain encodes, recognizes, and memorizes general visual objects is a fundamental question in neuroscience. Here, we investigated the neural processes underlying visual object perception and memory by recording from 3173 single neurons in the human amygdala and hippocampus across four experiments. We employed both passive-viewing and recognition memory tasks involving a diverse range of naturalistic object stimuli. Our findings reveal a region-based feature code for general objects, where neurons exhibit receptive fields in the high-level visual feature space. This code can be validated by independent new stimuli and replicated across all experiments, including fixation-based analyses with large natural scenes. This region code explains the long-standing visual category selectivity, preferentially enhances memory of encoded stimuli, predicts memory performance, encodes image memorability, and exhibits intricate interplay with memory contexts. Together, region-based feature coding provides an important mechanism for visual object processing in the human brain.

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.

Integration of valence and conflict processing through cellular-field interactions in human subgenual cingulate during emotional face processing in treatment-resistant depression

The subgenual anterior cingulate cortex (sgACC) has been identified as a key brain area involved in various cognitive and emotional processes. While the sgACC has been implicated in both emotional valuation and emotional conflict monitoring, it is still unclear how this area integrates multiple functions. We characterized both single neuron and local field oscillatory activity in 14 patients undergoing sgACC deep brain stimulation for treatment-resistant depression. During recording, patients were presented with a modified Stroop task containing emotional face images that varied in valence and congruence. We further analyzed spike-field interactions to understand how network dynamics influence single neuron activity in this area. Most single neurons responded to both valence and congruence, revealing that sgACC neuronal activity can encode multiple processes within the same task, indicative of multifunctionality. During peak neuronal response, we observed increased spectral power in low frequency oscillations, including theta-band synchronization (4-8 Hz), as well as desynchronization in beta-band frequencies (13-30 Hz). Theta activity was modulated by current trial congruency with greater increases in spectral power following non-congruent stimuli, while beta desynchronizations occurred regardless of emotional valence. Spike-field interactions revealed that local sgACC spiking was phase-locked most prominently to the beta band, whereas phase-locking to the theta band occurred in fewer neurons overall but was modulated more strongly for neurons that were responsive to task. Our findings provide the first direct evidence of spike-field interactions relating to emotional cognitive processing in the human sgACC. Furthermore, we directly related theta oscillatory dynamics in human sgACC to current trial congruency, demonstrating it as an important regulator during conflict detection. Our data endorse the sgACC as an integrative hub for cognitive emotional processing through modulation of beta and theta network activity.

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.

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.

Single neuron responses underlying face recognition in the human midfusiform face-selective cortex

Faces are critical for social interactions and their recognition constitutes one of the most important and challenging functions of the human brain. While neurons responding selectively to faces have been recorded for decades in the monkey brain, face-selective neural activations have been reported with neuroimaging primarily in the human midfusiform gyrus. Yet, the cellular mechanisms producing selective responses to faces in this hominoid neuroanatomical structure remain unknown. Here we report single neuron recordings performed in 5 human subjects (1 male, 4 females) implanted with intracerebral microelectrodes in the face-selective midfusiform gyrus, while they viewed pictures of familiar and unknown faces and places. We observed similar responses to faces and places at the single cell level, but a significantly higher number of neurons responding to faces, thus offering a mechanistic account for the face-selective activations observed in this region. Although individual neurons did not respond preferentially to familiar faces, a population level analysis could consistently determine whether or not the faces (but not the places) were familiar, only about 50 ms after the initial recognition of the stimuli as faces. These results provide insights into the neural mechanisms of face processing in the human brain.

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.

Single-neuron mechanisms of neural adaptation in the human temporal lobe

A central function of the human brain is to adapt to new situations based on past experience. Adaptation is reflected behaviorally by shorter reaction times to repeating or similar stimuli, and neurophysiologically by reduced neural activity in bulk-tissue measurements with fMRI or EEG. Several potential single-neuron mechanisms have been hypothesized to cause this reduction of activity at the macroscopic level. We here explore these mechanisms using an adaptation paradigm with visual stimuli bearing abstract semantic similarity. We recorded intracranial EEG (iEEG) simultaneously with spiking activity of single neurons in the medial temporal lobes of 25 neurosurgical patients. Recording from 4917 single neurons, we demonstrate that reduced event-related potentials in the macroscopic iEEG signal are associated with a sharpening of single-neuron tuning curves in the amygdala, but with an overall reduction of single-neuron activity in the hippocampus, entorhinal cortex, and parahippocampal cortex, consistent with fatiguing in these areas.

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.

Model Sharing in the Human Medial Temporal Lobe

Effective planning involves knowing where different actions take us. However, natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16 female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low-dimensional (cue independent) and a high-dimensional (cue specific) representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses were less correlated between trials with differing input features but similar state associations in the high dimensional than in the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared with negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world.SIGNIFICANCE STATEMENT Effective planning involves maintaining an accurate model of which actions take us to which locations. But in a world awash with information, mapping actions to states with the right level of complexity is critical. Using a new decision-making "heist task" in conjunction with computational modeling and fMRI, we show that patterns of BOLD responses in the medial temporal lobe-a brain region key for prospective planning-become less sensitive to the presence of visual features when these are irrelevant to the task at hand. By flexibly adapting the complexity of task-state representations in this way, state-action mappings learned under one set of features can be used to plan in the presence of others.

Face identity coding in the deep neural network and primate brain

A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.

An Event-Related Potential Study on Differences Between Higher and Lower Easy of Learning Judgments: Evidence for the Ease-of-Processing Hypothesis

Easy of learning (EOL) judgments occur before active learning begins, and it is a prediction of how difficult it will be to learn new material in future learning. This study compared the amplitude of event-related potential (ERP) components and brain activation regions between high and low EOL judgments by adopting ERPs with a classical EOL judgment paradigm, aiming to confirm the ease-of-processing hypothesis. The results showed that (1) the magnitudes of EOL judgments are affected by encoding fluency cues, and the judgment magnitude increases with encoding fluency; (2) low EOL judgments are associated with higher N400 amplitude at the left superior frontal gyrus (SFG) and left middle frontal gyrus (MFG). High EOL judgments showed enlarged slow-wave (600–1,000 ms) potentials than low EOL judgments at the left medial temporal lobe (MTL), right ventromedial prefrontal cortex (VMPFC), and dorsolateral prefrontal cortex (DLPFC). Our results support the ease-of-processing hypothesis, particularly, by affirming that EOL judgments are affected by encoding fluency in two processing stages. N400 reflects the process of acquiring encoding fluency cues, while slow-wave indicates that individuals use encoding fluency cues for metacognitive monitoring.

Brain activity patterns underlying memory confidence

The primary aim of this review is to examine the brain activity patterns that are related to subjectively perceived memory confidence. We focus on the main brain regions involved in episodic memory: the medial temporal lobe (MTL), prefrontal cortex (PFC), and posterior parietal cortex (PPC), and relate activity in their subregions to memory confidence. How this brain activity in both the encoding and retrieval phase is related to (subsequent) memory confidence ratings will be discussed. Specifically, encoding related activity in MTL regions and ventrolateral PFC mainly shows a positive linear increase with subsequent memory confidence, while dorsolateral and ventromedial PFC activity show mixed patterns. In addition, encoding-related PPC activity seems to only have indirect effects on memory confidence ratings. Activity during retrieval in both the hippocampus and parahippocampal cortex increases with memory confidence, especially during high-confident recognition. Retrieval-related activity in the PFC and PPC show mixed relationships with memory confidence, likely related to post-retrieval monitoring and attentional processes, respectively. In this review, these MTL, PFC, and PPC activity patterns are examined in detail and related to their functional roles in memory processes. This insight into brain activity that underlies memory confidence is important for our understanding of brain-behaviour relations and memory-guided decision making.

Single neuron evidence of inattentional blindness in humans

Recording directly from the brain of a patient undergoing neurosurgery with electrodes implanted deep in her skull, we identified neurons that change their properties when the patient became consciously aware of content. Specifically, we showed the patient an established clip of a gorilla passing through the screen, unnoticeable, in a classic inattentional blindness task, and identified a neuron in the right amygdala that fired only when the patient was aware of the gorilla. A different neuron coded the moment of insight, when the patient realized that she had missed the salient gorilla in previous trials. A third cluster of neurons fired when the patient was exposed to a post-clip question ("How many passes did you count?") and reflected on the content. Neurons in this cluster altered their response behavior between unaware and aware states. To investigate the interplay between the neurons' activity and characterize the potential cascade of information flow in the brain that leads to conscious awareness, we looked at the neurons' properties change, their activities' alignment and the correlation across the cells. Examining the coherence between the spiking activity of the responsive neurons and the field potentials in neighboring sites we identified an alignment in the alpha and theta bands. This spike-field coherence hints at an involvement of attention and memory circuits in the perceptual awareness of the stimulus. Taken together, our results suggest that conscious awareness of content emerges when there is alignment between individual neurons' activity and the local field potentials. Our work provides direct neural correlate for the psychological process by which one can look at things directly but fail to perceive them with the "mind's eye".

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.

Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence

Neurophysiological studies have shown that there is a close relationship between spikes and local field potential (LFP), which reflects crucial neural coding information. In this paper, we used a new method to evaluate the synchronization between spikes and LFP. All possible phases of LFP from −π to π were first binned into a freely chosen number of bins; then, the probability of spikes falling in each bin was calculated, and the deviation degree from the uniform distribution based on the Kullback–Leibler divergence was calculated to define the synchronization between spikes and LFP. The simulation results demonstrate that the method is rapid, basically unaffected by the total number of spikes, and can adequately resist the noise of spike trains. We applied this method to the experimental data of patients with intractable epilepsy, and we observed the synchronization between spikes and LFP in the formation of memory. These results show that our proposed method is a powerful tool that can quantitatively measure the synchronization between spikes and LFP.

Concept formation as a computational cognitive process

Hippocampus and entorhinal cortex form cognitive maps that represent relations among memories within a multidimensional space. While these relational maps have long been proposed to contribute to episodic memory, recent work suggests that they also support concept formation by representing relevant features for discriminating among related concepts. Cognitive maps may be refined by medial prefrontal cortex, which selects dimensions to represent based on their behavioral relevance. Hippocampal pattern completion, which is critical for retrieval of episodic memories, may also contribute to generalization of existing concepts to new exemplars. Navigation within hippocampal cognitive maps, which is guided by grid coding in entorhinal cortex, may contribute to imagination through recombination of event elements or concept features.

A novel low-cost electrode for recording the local field potential of freely moving rat's brain

Local field potentials (LFPs) are involved in almost all cognitive activities of animals. Several kinds of recording electrodes are used for recording LFPs in freely moving animals, including commercial and homemade electrodes. However, commercial recording electrodes are expensive, and their relatively fixed size often causes a steric hindrance effect, especially when combining deep brain stimulation (DBS) with LFP recording, which may not always satisfy the aim of researchers. Currently, an increasing number of researchers are designing their own recording electrodes to lower research costs. Nevertheless, there is no simple universal method to produce low-cost recording electrodes with a specific size according to the target brain area. Thus, we developed a simple method for quickly producing low-cost multiple-channel recording electrodes. To inspect the effectiveness of our self-designed electrode, LFPs were recorded in a Parkinson’s disease (PD) rat model, and an electrical stimulation electrode was implanted into the subthalamic nucleus to verify the space-saving ability of the self-designed recording electrode. The results showed that <30 min was needed to prepare an electrode and that the electrode materials cost <5 dollars. Further investigations showed that our electrode successfully recorded the beta oscillations (12–40 Hz) in the PD rat model. Thus, this method will greatly reduce the cost of recording electrodes and save time for researchers. Additionally, the small size of the electrode will further facilitate DBS research.

The Architecture of Human Memory: Insights from Human Single-Neuron Recordings

Deciphering the mechanisms of human memory is a central goal of neuroscience, both from the point of view of the fundamental biology of memory and for its translational relevance. Here, we review some contributions that recordings from neurons in humans implanted with electrodes for clinical purposes have made toward this goal. Recordings from the medial temporal lobe, including the hippocampus, reveal the existence of two classes of cells: those encoding highly selective and invariant representations of abstract concepts, and memory-selective cells whose activity is related to familiarity and episodic retrieval. Insights derived from observing these cells in behaving humans include that semantic representations are activated before episodic representations, that memory content and memory strength are segregated, and that the activity of both types of cells is related to subjective awareness as expected from a substrate for declarative memory. Visually selective cells can remain persistently active for several seconds, thereby revealing a cellular substrate for working memory in humans. An overarching insight is that the neural code of human memory is interpretable at the single-neuron level. Jointly, intracranial recording studies are starting to reveal aspects of the building blocks of human memory at the single-cell level. This work establishes a bridge to cellular-level work in animals on the one hand, and the extensive literature on noninvasive imaging in humans on the other hand. More broadly, this work is a step toward a detailed mechanistic understanding of human memory that is needed to develop therapies for human memory disorders.

Microelectrode recordings in human epilepsy: a case for clinical translation

With their 'all-or-none' action potential responses, single neurons (or units) are accepted as the basic computational unit of the brain. There is extensive animal literature to support the mechanistic importance of studying neuronal firing as a way to understand neuronal microcircuits and brain function. Although most studies have emphasized physiology, there is increasing recognition that studying single units provides novel insight into system-level mechanisms of disease. Microelectrode recordings are becoming more common in humans, paralleling the increasing use of intracranial electroencephalography recordings in the context of presurgical evaluation in focal epilepsy. In addition to single-unit data, microelectrode recordings also record local field potentials and high-frequency oscillations, some of which may be different to that recorded by clinical macroelectrodes. However, microelectrodes are being used almost exclusively in research contexts and there are currently no indications for incorporating microelectrode recordings into routine clinical care. In this review, we summarize the lessons learnt from 65 years of microelectrode recordings in human epilepsy patients. We cover the electrode constructs that can be utilized, principles of how to record and process microelectrode data and insights into ictal dynamics, interictal dynamics and cognition. We end with a critique on the possibilities of incorporating single-unit recordings into clinical care, with a focus on potential clinical indications, each with their specific evidence base and challenges.

Brain computation by assemblies of neurons

Our expanding understanding of the brain at the level of neurons and synapses, and the level of cognitive phenomena such as language, leaves a formidable gap between these two scales. Here we introduce a computational system which promises to bridge this gap: the Assembly Calculus. It encompasses operations on assemblies of neurons, such as project, associate, and merge, which appear to be implicated in cognitive phenomena, and can be shown, analytically as well as through simulations, to be plausibly realizable at the level of neurons and synapses. We demonstrate the reach of this system by proposing a brain architecture for syntactic processing in the production of language, compatible with recent experimental results.

Assemblies are large populations of neurons believed to imprint memories, concepts, words, and other cognitive information. We identify a repertoire of operations on assemblies. These operations correspond to properties of assemblies observed in experiments, and can be shown, analytically and through simulations, to be realizable by generic, randomly connected populations of neurons with Hebbian plasticity and inhibition. Assemblies and their operations constitute a computational model of the brain which we call the Assembly Calculus, occupying a level of detail intermediate between the level of spiking neurons and synapses and that of the whole brain. The resulting computational system can be shown, under assumptions, to be, in principle, capable of carrying out arbitrary computations. We hypothesize that something like it may underlie higher human cognitive functions such as reasoning, planning, and language. In particular, we propose a plausible brain architecture based on assemblies for implementing the syntactic processing of language in cortex, which is consistent with recent experimental results.

Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function

Brain rhythms are associated with a range of physiologic states, and thus, studies have traditionally focused on neuronal origin, temporal dynamics and fundamental role of individual brain rhythms, and more recently on specific pair-wise interactions. Here, we aim to understand integrated physiologic function as an emergent phenomenon of dynamic network interactions among brain rhythms. We hypothesize that brain rhythms continuously coordinate their activations to facilitate physiologic states and functions. We analyze healthy subjects during sleep, and we demonstrate the presence of stable interaction patterns among brain rhythms. Probing transient modulations in brain wave activation, we discover three classes of interaction patterns that form an ensemble representative for each sleep stage, indicating an association of each state with a specific network of brain-rhythm communications. The observations are universal across subjects and identify networks of brain-rhythm interactions as a hallmark of physiologic state and function, providing new insights on neurophysiological regulation with broad clinical implications.

Single Neuron Coding of Identity in the Human Hippocampal Formation

Experimental findings show the ubiquitous presence of graded responses and tuning curves in the neocortex, particularly in visual areas [1-15]. Among these, inferotemporal-cortex (IT) neurons respond to complex visual stimuli, but differences in the neurons' responses can be used to distinguish the stimuli eliciting the responses [8, 9, 16-18]. The IT projects directly to the medial temporal lobe (MTL) [19], where neurons respond selectively to different pictures of specific persons and even to their written and spoken names [20-22]. However, it is not clear whether this is done through a graded coding, as in the neocortex, or a truly invariant code, in which the response-eliciting stimuli cannot be distinguished from each other. To address this issue, we recorded single neurons during the repeated presentation of different stimuli (pictures and written and spoken names) corresponding to the same persons. Using statistical tests and a decoding approach, we found that only in a minority of cases can the different pictures of a given person be distinguished from the neurons' responses and that in a larger proportion of cases, the responses to the pictures were different to the ones to the written and spoken names. We argue that MTL neurons tend to lack a representation of sensory features (particularly within a sensory modality), which can be advantageous for the memory function attributed to this area [23-25], and that a full representation of memories is given by a combination of mostly invariant coding in the MTL with a representation of sensory features in the neocortex.

Modeling of Brain-Like Concept Coding with Adulthood Neurogenesis in the Dentate Gyrus

Mammalian brains respond to new concepts via a type of neural coding termed “concept coding.” During concept coding, the dentate gyrus (DG) plays a vital role in pattern separation and pattern integration of concepts because it is a brain region with substantial neurogenesis in adult mammals. Although concept coding properties of the brain have been extensively studied by experimental work, modeling of the process to guide both further experimental studies and applications such as natural language processing is scarce. To model brain-like concept coding, we built a spiking neural network inspired by adulthood neurogenesis in the DG. Our model suggests that neurogenesis may facilitate integration of closely related concepts and separation of less relevant concepts. Such pattern agrees with the previous experimental observations in classification tasks and place cells in the hippocampus. Therefore, our simulation provides insight for future experimental studies on the neural coding difference between perception and cognition. By presenting 14 contexts each containing 4 concepts to the network, we found that neural responses of the DG changed dynamically as the context repetition time increased and were eventually consistent with the category organization of humans. Thus, our work provides a new framework of word representation for the construction of brain-like knowledge map.

Episodic memory improvements due to noninvasive stimulation targeting the cortical-hippocampal network: A replication and extension experiment

INTRODUCTION

The distributed cortical network of the human hippocampus is important for episodic memory. In a previous experiment, noninvasive stimulation of the hippocampal-cortical network applied for five consecutive days improved paired-associate learning measured after the stimulation regimen via cued recall (Wang et al., Science, 2014, 345, 1054). This finding has not yet been directly replicated. Furthermore, evidence for long-lasting effects of stimulation on paired-associate learning was obtained by analyzing relatively small subsamples (Wang & Voss, Hippocampus, 2015, 25, 877) and requires further evaluation.

METHODS

Sixteen healthy young adults participated in this replication study using the same experimental design as the original study. Participants received 1 week of active stimulation and 1 week of sham stimulation, with memory assessments occurring at the beginning (pre) and end (post) of each week. Assessments included the paired-associate task used in the original study, as well as a long-term episodic memory retention task in order to test the hypothesis that increased paired-associate learning could come at the cost of accelerated long-term forgetting. Change in memory scores was evaluated within (pre vs. post) and across (active vs. sham) weeks.

RESULTS

Similar to Wang et al., paired-associate learning was significantly improved after 1 week of active stimulation but not after 1 week of sham stimulation. We found no evidence that stimulation increased long-term forgetting for either week.

CONCLUSION

These findings confirm the beneficial effects of stimulation on episodic memory that were reported previously and indicate that stimulation-related gains in new learning ability do not come at the price of accelerated long-term forgetting.