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.

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.

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.

A detailed and fast model of extracellular recordings.

We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. To reduce the computational load, the contributions of neurons further away were simulated using previously recorded spikes with their amplitude normalized by the distance to the recording electrode. For making the simulations more realistic, we also considered a model of a finite-size electrode by averaging the potential along the electrode surface and modeling the electrode-tissue interface with a capacitive filter. This model allowed studying the effect of the electrode diameter on the quality of the recordings and how it affects the number of identified neurons after spike sorting. Given that not all neurons are active at a time, we also generated simulations with different ratios of active neurons and estimated the ratio that matches the signal-to-noise values observed in real data. Finally, we used the model to simulate tetrode recordings.

Human medial temporal lobe neurons respond preferentially to personally relevant images.

People with whom one is personally acquainted tend to elicit richer and more vivid memories than people with whom one does not have a personal connection. Recent findings from neurons in the human medial temporal lobe (MTL) have shown that individual cells respond selectively and invariantly to representations of famous people [Quian Quiroga R, Reddy L, Kreiman G, Koch C, Fried I (2005) Nature 435(7045):1102-1107]. Observing these cells, we wondered whether photographs of personally relevant individuals, such as family members, might be more likely to generate such responses. To address this issue, we recorded the activity of 2,330 neurons in the human MTL while patients viewed photographs of varying personal relevance: previously unknown faces and landscapes, familiar but not necessarily personally relevant faces and landscapes, and finally, photographs of the patients themselves, their families, and the experimenters. Our findings indicate that personally relevant photographs are indeed more likely to elicit selective responses in MTL neurons than photographs of individuals with whom the patients have had no personal contact. These findings further suggest that relevant stimuli are encoded by a larger proportion of neurons than less relevant stimuli, given that familiar or personally relevant items are linked to a larger variety of experiences and memories of these experiences.

Fixation-related potentials in visual search: a combined EEG and eye tracking study.

We report a study of concurrent eye movements and electroencephalographic (EEG) recordings while subjects freely explored a search array looking for hidden targets. We describe a sequence of fixation-event related potentials (fERPs) that unfolds during ∼ 400 ms following each fixation. This sequence highly resembles the event-related responses in a replay experiment, in which subjects kept fixation while a sequence of images occurred around the fovea simulating the spatial and temporal patterns during the free viewing experiment. Similar responses were also observed in a second control experiment where the appearance of stimuli was controlled by the experimenters and presented at the center of the screen. We also observed a relatively early component (∼150 ms) that distinguished between targets and distractors only in the freeviewing condition. We present a novel approach to match the critical properties of two conditions (targets/distractors), which can be readily adapted to other paradigms to investigate EEG components during free eye-movements.

Selectivity of pyramidal cells and interneurons in the human medial temporal lobe.

Neurons in the medial temporal lobe (MTL) respond selectively to pictures of specific individuals, objects, and places. However, the underlying mechanisms leading to such degree of stimulus selectivity are largely unknown. A necessary step to move forward in this direction involves the identification and characterization of the different neuron types present in MTL circuitry. We show that putative principal cells recorded in vivo from the human MTL are more selective than putative interneurons. Furthermore, we report that putative hippocampal pyramidal cells exhibit the highest degree of selectivity within the MTL, reflecting the hierarchical processing of visual information. We interpret these differences in selectivity as a plausible mechanism for generating sparse responses.

Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex.

Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized LFPs and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters.

Responses of human medial temporal lobe neurons are modulated by stimulus repetition.

Recent studies have reported the presence of single neurons with strong responses to visual inputs in the human medial temporal lobe. Here we show how repeated stimulus presentation--photos of celebrities and familiar individuals, landmark buildings, animals, and objects--modulates the firing rate of these cells: a consistent decrease in the neural activity was registered as images were repeatedly shown during experimental sessions. The effect of repeated stimulus presentation was not the same for all medial temporal lobe areas. These findings are consistent with the view that medial temporal lobe neurons link visual percepts to declarative memory.

Searching for the neural correlates of human intelligence.

In this My Word, Rodrigo Quian Quiroga discusses neural mechanisms underlying human memory and uniquely human cognitive abilities.

Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe.

Neurons in the temporal lobe of both monkeys and humans show selective responses to classes of visual stimuli and even to specific individuals. In this study, we investigate the latency and selectivity of visually responsive neurons recorded from microelectrodes in the parahippocampal cortex, entorhinal cortex, hippocampus, and amygdala of human subjects during a visual object presentation task. During 96 experimental sessions in 35 subjects, we recorded from a total of 3278 neurons. Of these units, 398 responded selectively to one or more of the presented stimuli. Mean response latencies were substantially larger than those reported in monkeys. We observed a highly significant correlation between the latency and the selectivity of these neurons: the longer the latency the greater the selectivity. Particularly, parahippocampal neurons were found to respond significantly earlier and less selectively than those in the other three regions. Regional analysis showed significant correlations between latency and selectivity within the parahippocampal cortex, entorhinal cortex, and hippocampus, but not within the amygdala. The later and more selective responses tended to be generated by cells with sparse baseline firing rates and vice versa. Our results provide direct evidence for hierarchical processing of sensory information at the interface between the visual pathway and the limbic system, by which increasingly refined and specific representations of stimulus identity are generated over time along the anatomic pathways of the medial temporal lobe.

A single-neuron correlate of change detection and change blindness in the human medial temporal lobe.

Observers are often unaware of changes in their visual environment when attention is not focused at the location of the change . Because of its rather intriguing nature, this phenomenon, known as change blindness, has been extensively studied with psychophysics as well as with fMRI . However, whether change blindness can be tracked in the activity of single cells is not clear. To explore the neural correlates of change detection and change blindness, we recorded from single neurons in the human medial temporal lobe (MTL) during a change-detection paradigm. The preferred pictures of the visually responsive units elicited significantly higher firing rates on the attended trials when subjects correctly identified a change (change detection) compared to the unattended trials when they missed it (change blindness). On correct trials, the firing activity of individual units allowed us to predict the occurrence of a change, on a trial-by-trial basis, with 67% accuracy. In contrast, this prediction was at chance for incorrect, unattended trials. The firing rates of visually selective MTL cells thus constitute a neural correlate of change detection.

Selectivity and invariance for visual object perception.

The sight of an object triggers a complex set of processes in the brain. Although it is already well established that object perception is performed by a hierarchical network, the so-called ventral visual pathway, we are only starting to understand how neurons along this pathway encode visual information at each processing stage. In this review, we discuss basic principles of neural coding for object perception and describe evidence showing that it mainly relies on two principles: selectivity and invariance.

Unmixing concurrent EEG-fMRI with parallel independent component analysis.

Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-by-voxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment.

Compact standalone platform for neural recording with real-time spike sorting and data logging.

OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain-machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. APPROACH: We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. MAIN RESULTS: The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. SIGNIFICANCE: The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

Nonlinear multivariate analysis of neurophysiological signals.

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

Tracking pattern learning with single-trial event-related potentials.

OBJECTIVE: The main aim was to track the dynamics of pattern-learning using single-trial event-related potentials (ERPs). A new 'learning-oddball' paradigm was employed presenting eight random targets (the 'no-pattern') followed by eight regular targets (the 'pattern'). In total, six repetitions of the 'no-pattern' followed by the 'pattern' were presented. METHODS: We traced the dynamics of learning by measuring responses to 16 (eight random-eight regular) targets. Since this alternation of the 'no-pattern' followed by the 'pattern' was repeated six times, we extracted single-trial responses to all 96 targets to determine if learning occurred more rapidly with each repetition of the 'pattern.' RESULTS: Following random targets, ERPs contained a marked P3-N2 component that decreased to regular targets, whereas a contingent negative variation (CNV) appeared. ERP changes could be best described by sigmoid 'learning' curves. Single-trial analyses showed that learning occurred more rapidly over repetitions and suggested that the CNV developed prior to the decay of the N2-P3 component. CONCLUSIONS: We show a new paradigm-analysis methodology to track learning processes directly from brain signals. SIGNIFICANCE: Single-trial ERPs analyses open a wide range of applications. Tracking the dynamic structure of cognitive functions may prove crucial in the understanding of learning and in the study of different pathologies.

Magic and cognitive neuroscience.

In recent years, neuroscientists have shown an increasing interest in magic. One reason for this is the parallels that can be drawn between concepts that have long been discussed in magic theory, particularly misdirection, and those that are routinely studied in cognitive neuroscience, such as attention and, as argued in this essay, different forms of memory. A second and perhaps more attractive justification for this growing interest is that magic tricks offer novel experimental approaches to cognitive neuroscience. In fact, magicians continuously demonstrate in very engaging ways one of the most basic principles of brain function - how the brain constructs a subjective reality using assumptions based on relatively little and ambiguous information.