Plugging in to Human Memory: Advantages, Challenges, and Insights from Human Single-Neuron Recordings.

We describe single-neuron recordings in the human hippocampal formation, performed in epileptic patients for clinical reasons, and highlight their advantages, challenges, and limitations compared with non-invasive recordings in humans and invasive recordings in animals. We propose a unified framework to explain different findings-responses to novel stimuli, spatial locations, and specific concepts-linking the rodent and human literature regarding the function of the hippocampal formation. Moreover, we propose a model of how memories are encoded in this area, suggesting that the context-independent, invariant coding by concept cells may provide a uniquely human neural mechanism underlying memory representations.

How Do We Recognize a Face?

How individual faces are encoded by neurons in high-level visual areas has been a subject of active debate. An influential model is that neurons encode specific faces. However, Chang and Tsao conclusively show that, instead, these neurons encode features along specific axes, which explains why they were previously found to respond to apparently different faces.

A novel missense mutation in the AIRE gene underlying autoimmune polyglandular syndrome type 1.

The immune regulator gene AIRE plays an essential role in the establishment of immune tolerance and the prevention of autoimmunity. This transcription factor plays a critical role in promoting self-tolerance in the thymus by regulating the expression of a large number of self-antigens that share the common feature of being tissue-restricted in their expression pattern in the periphery. Dysfunction of AIRE in humans causes a rare disease, autoimmune polyglandular syndrome type 1 (APS1), characterized by an autoimmune response against peripheral tissues, particularly endocrine tissues. Although a few dominant mutations have been described, the inactivation of AIRE is usually caused by recessive mutations. Recent data suggests that alterations in AIRE function contribute not only to APS1 but also to more common forms of autoimmune disease. Here, we present a previously unreported missense mutation (NM_000383.2:c.260 T > C) in exon 2 of the AIRE gene, predicted to cause the substitution (p.(Leu87Pro)) in the CARD domain of the AIRE protein. When inherited in conjunction with another dysfunctional AIRE allele, this mutation was associated with immune dysregulation in a pediatric patient. The presence of hypergammaglobulinemia, malabsorption syndrome, ectodermal dysplasia, mucocutaneous candidiasis, vitiligo, and hypothyroidism as well as the presence of multiple autoantibodies allowed us to confirm an APS1 diagnosis.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

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.

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.

Convergent Evolution in SARS-CoV-2 Spike Creates a Variant Soup from Which New COVID-19 Waves Emerge.

The first 2 years of the COVID-19 pandemic were mainly characterized by recurrent mutations of SARS-CoV-2 Spike protein at residues K417, L452, E484, N501 and P681 emerging independently across different variants of concern (Alpha, Beta, Gamma, and Delta). Such homoplasy is a marker of convergent evolution. Since Spring 2022 and the third year of the pandemic, with the advent of Omicron and its sublineages, convergent evolution has led to the observation of different lineages acquiring an additional group of mutations at different amino acid residues, namely R346, K444, N450, N460, F486, F490, Q493, and S494. Mutations at these residues have become increasingly prevalent during Summer and Autumn 2022, with combinations showing increased fitness. The most likely reason for this convergence is the selective pressure exerted by previous infection- or vaccine-elicited immunity. Such accelerated evolution has caused failure of all anti-Spike monoclonal antibodies, including bebtelovimab and cilgavimab. While we are learning how fast coronaviruses can mutate and recombine, we should reconsider opportunities for economically sustainable escape-proof combination therapies, and refocus antibody-mediated therapeutic efforts on polyclonal preparations that are less likely to allow for viral immune escape.

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 cmin of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction cmax 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.

Scene-selective coding by single neurons in the human parahippocampal cortex.

Imaging, electrophysiological, and lesion studies have shown a relationship between the parahippocampal cortex (PHC) and the processing of spatial scenes. Our present knowledge of PHC, however, is restricted to the macroscopic properties and dynamics of bulk tissue; the behavior and selectivity of single parahippocampal neurons remains largely unknown. In this study, we analyzed responses from 630 parahippocampal neurons in 24 neurosurgical patients during visual stimulus presentation. We found a spatially clustered subpopulation of scene-selective units with an associated event-related field potential. These units form a population code that is more distributed for scenes than for other stimulus categories, and less sparse than elsewhere in the medial temporal lobe. Our electrophysiological findings provide insight into how individual units give rise to the population response observed with functional imaging in the parahippocampal place area.

A novel and fully automatic spike-sorting implementation with variable number of features.

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

Concept cells: the building blocks of declarative memory functions.

Intracranial recordings in subjects suffering from intractable epilepsy - made during their evaluation for an eventual surgical removal of the epileptic focus - have allowed the extraordinary opportunity to study the firing of multiple single neurons in awake and behaving human subjects. These studies have shown that neurons in the human medial temporal lobe respond in a remarkably selective and abstract manner to particular persons or objects, such as Jennifer Aniston, Luke Skywalker or the Tower of Pisa. These neurons have been named 'Jennifer Aniston neurons' or, more recently, 'concept cells'. I argue that the sparse, explicit and abstract representation of these neurons is crucial for memory functions, such as the creation of associations and the transition between related concepts that leads to episodic memories and the flow of consciousness.

On-line, voluntary control of human temporal lobe neurons.

Daily life continually confronts us with an exuberance of external, sensory stimuli competing with a rich stream of internal deliberations, plans and ruminations. The brain must select one or more of these for further processing. How this competition is resolved across multiple sensory and cognitive regions is not known; nor is it clear how internal thoughts and attention regulate this competition. Recording from single neurons in patients implanted with intracranial electrodes for clinical reasons, here we demonstrate that humans can regulate the activity of their neurons in the medial temporal lobe (MTL) to alter the outcome of the contest between external images and their internal representation. Subjects looked at a hybrid superposition of two images representing familiar individuals, landmarks, objects or animals and had to enhance one image at the expense of the other, competing one. Simultaneously, the spiking activity of their MTL neurons in different subregions and hemispheres was decoded in real time to control the content of the hybrid. Subjects reliably regulated, often on the first trial, the firing rate of their neurons, increasing the rate of some while simultaneously decreasing the rate of others. They did so by focusing onto one image, which gradually became clearer on the computer screen in front of their eyes, and thereby overriding sensory input. On the basis of the firing of these MTL neurons, the dynamics of the competition between visual images in the subject's mind was visualized on an external display.

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes.

Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

Extracting information in spike time patterns with wavelets and information theory.

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information.

Short transmembrane domains with high-volume exoplasmic halves determine retention of Type II membrane proteins in the Golgi complex.

It is still unclear why some proteins that travel along the secretory pathway are retained in the Golgi complex whereas others make their way to the plasma membrane. Recent bioinformatic analyses on a large number of single-spanning membrane proteins support the hypothesis that specific features of the transmembrane domain (TMD) are relevant to the sorting of these proteins to particular organelles. Here we experimentally test this hypothesis for Golgi and plasma membrane proteins. Using the Golgi SNARE protein Sft1 and the plasma membrane SNARE protein Sso1 from Saccharomyces cerevisiae as model proteins, we modified the length of their TMDs and the volume of their exoplasmic hemi-TMD, and determined their subcellular localization both in yeast and mammalian cells. We found that short TMDs with high-volume exoplasmic hemi-TMDs confer Golgi membrane residence, whereas TMDs with low-volume exoplasmic hemi-TMDs, either short or long, confer plasma membrane residence to these proteins. Results indicate that the shape of the exoplasmic hemi-TMD, in addition to the length of the entire TMD, determine retention in the Golgi or exit to the plasma membrane of Type II membrane proteins.

Single-cell recordings in the human medial temporal lobe.

Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons have opened the possibility to narrow down the gap between neurophysiological studies in animals and non-invasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography) investigations in humans. Here we provide a description of the main procedures for electrode implantation and recordings, the experimental paradigms used and the main steps for processing the data. We also present key characteristics of the so-called 'concept cells', neurons in the human medial temporal lobe with selective and invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative memory. Finally, we present novel results dealing with the stability of the representation given by these neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show that, after an initial decay, the response strength reaches an asymptotic value after approximately 15 presentations that remains above baseline for the whole duration of the experiment.

Bayes optimal template matching for spike sorting - combining fisher discriminant analysis with optimal filtering.

Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy.

Uncovering the mechanisms of conscious face perception: a single-trial study of the n170 responses.

When a face is flashed to an observer, a large negative component is elicited in the occipitotemporal cortex at ∼170 ms from the onset of presentation (N170). Previous studies have shown that the average N170 is correlated with conscious face perception; however, the single-trial mechanisms underlying such modulation remain largely unexplored. Here, we studied in human subjects the average and the single-trial N170 responses to briefly flashed faces, coupled with backward masking and varying degrees of Gaussian noise. In the average evoked responses we observed that, at fixed levels of noise, supraliminal faces exhibited significantly larger N170 amplitudes than subliminal faces. Moreover, the average N170 amplitude decreased with noise level both for the perceived and the nonperceived faces. At the single-trial level, the N170 amplitude was modulated by conscious recognition, which allowed predicting the subjects' perceptual responses above chance. In contrast, the single-trial N170 amplitudes were not modulated by the amount of noise and the effect found in the average responses was due to different latency jitters, as confirmed with latency-corrected averages. Altogether, these results suggest that conscious face perception is correlated with a boost in the activity of face-selective neural assemblies, whereas the stimulus uncertainty introduced by the added noise decreases the timing consistency (but not the amplitude) of this activation.

Looking for a face in the crowd: fixation-related potentials in an eye-movement visual search task.

Despite the compelling contribution of the study of event related potentials (ERPs) and eye movements to cognitive neuroscience, these two approaches have largely evolved independently. We designed an eye-movement visual search paradigm that allowed us to concurrently record EEG and eye movements while subjects were asked to find a hidden target face in a crowded scene with distractor faces. Fixation event-related potentials (fERPs) to target and distractor stimuli showed the emergence of robust sensory components associated with the perception of stimuli and cognitive components associated with the detection of target faces. We compared those components with the ones obtained in a control task at fixation: qualitative similarities as well as differences in terms of scalp topography and latency emerged between the two. By using single trial analyses, fixations to target and distractors could be decoded from the EEG signals above chance level in 11 out of 12 subjects. Our results show that EEG signatures related to cognitive behavior develop across spatially unconstrained exploration of natural scenes and provide a first step towards understanding the mechanisms of target detection during natural search.