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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.

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.

While experience is continuous, memories are organized as discrete events. Cognitive boundaries are thought to segment experience and structure memory, but how this process is implemented remains unclear. We recorded the activity of single neurons in the human medial temporal lobe (MTL) during the formation and retrieval of memories with complex narratives. Here, we show that neurons responded to abstract cognitive boundaries between different episodes. Boundary-induced neural state changes during encoding predicted subsequent recognition accuracy but impaired event order memory, mirroring a fundamental behavioral tradeoff between content and time memory. Furthermore, the neural state following boundaries was reinstated during both successful retrieval and false memories. These findings reveal a neuronal substrate for detecting cognitive boundaries that transform experience into mnemonic episodes and structure mental time travel during retrieval.

The ability to integrate 'omics' (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different 'omics' data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/.

Bowers challenged the common view in favor of distributed representations in psychological modeling and the main arguments given against localist and grandmother cell coding schemes. He revisited the results of several single-cell studies, arguing that they do not support distributed representations. We praise the contribution of Bowers (2009) for joining evidence from psychological modeling and neurophysiological recordings, but we disagree with several of his claims. In this comment, we argue that distinctions between distributed, localist, and grandmother cell coding can be troublesome with real data. Moreover, these distinctions seem to be lying within the same continuum, and we argue that it may be sensible to characterize coding schemes with a sparseness measure. We further argue that there may not be a unique coding scheme implemented in all brain areas and for all possible functions. In particular, current evidence suggests that the brain may use distributed codes in primary sensory areas and sparser and invariant representations in higher areas. (Contains 1 figure and 1 footnote.)

Primates constantly explore their surroundings via saccadic eye movements that bring different parts of an image into high resolution. In addition to exploring new regions in the visual field, primates also make frequent return fixations, revisiting previously foveated locations. We systematically studied a total of 44,328 return fixations out of 217,440 fixations. Return fixations were ubiquitous across different behavioral tasks, in monkeys and humans, both when subjects viewed static images and when subjects performed natural behaviors. Return fixations locations were consistent across subjects, tended to occur within short temporal offsets, and typically followed a 180-degree turn in saccadic direction. To understand the origin of return fixations, we propose a proof-of-principle, biologically-inspired and image-computable neural network model. The model combines five key modules: an image feature extractor, bottom-up saliency cues, task-relevant visual features, finite inhibition-of-return, and saccade size constraints. Even though there are no free parameters that are fine-tuned for each specific task, species, or condition, the model produces fixation sequences resembling the universal properties of return fixations. These results provide initial steps towards a mechanistic understanding of the trade-off between rapid foveal recognition and the need to scrutinize previous fixation locations. Author summary: We move our eyes several times a second, bringing the center of gaze into focus and high resolution. While we typically assume that we can rapidly recognize the contents at each fixation, it turns out that we often move our eyes back to previously visited locations. These return fixations are ubiquitous across different tasks, conditions, and across species. A computational model captures these eye movements and return fixations by using four key mechanisms: extraction of salient parts of an image, incorporation of task goals such as the target during visual search, a constraint to avoid making large eye movements, and forgetful memory of previous locations. Neither the extreme of getting stuck at a single location or the extreme of never revisiting previous locations seems adequate for visual processing. Instead, the combination of these four mechanisms allows the visual system to achieve a happy medium during scene understanding. [ABSTRACT FROM AUTHOR]

The current authors reply to a response by Bowers on a comment by the current authors on the original article. A typical problem in any discussion about grandmother cells is that there is not a general consensus about what should be called as such. Here, we discuss possible interpretations in turn and contrast them with what we find in our own data (arguably the closest experimental evidence of grandmother cells so far). A first and naive interpretation of the term grandmother cell is that one and only one neuron encodes for one and only one concept (a face, an object, an animal, etc.). We agree with Bowers (2010) that this is a straw-man version of this idea--although some people still take this view when (incorrectly) arguing that if we would have grandmother cells then the concept of grandma would disappear if her dedicated cell dies--which clearly does not apply to our data.

Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex., Introduction More is known about the functions and deficits of the eye than about the rest of the visual system. However, most visual processing occurs in the cortex of the [...]

We used genome-wide sequencing methods to study stimulus-dependent enhancer function in mouse cortical neurons. We identified ~12,000 neuronal activity-regulated enhancers that are bound by the general transcriptional co-activator CBP in an activity-dependent manner. A function of CBP at enhancers may be to recruit RNA polymerase II (RNAPII), as we also observed activity-regulated RNAPII binding to thousands of enhancers. Notably, RNAPII at enhancers transcribes bi-directionally a novel class of enhancer RNAs (eRNAs) within enhancer domains defined by the presence of histone H3 monomethylated at lysine 4. The level of eRNA expression at neuronal enhancers positively correlates with the level of messenger RNA synthesis at nearby genes, suggesting that eRNA synthesis occurs specifically at enhancers that are actively engaged in promoting mRNA synthesis. These findings reveal that a widespread mechanism of enhancer activation involves RNAPII binding and eRNA synthesis., During development and in mature organisms, cells respond to changes in their environment in part through changes in gene expression. Extracellular factors including growth factors, hormones and neurotransmitters activate programs [...]

Bowers (2009) challenged the common view in favor of distributed representations in psychological modeling and the main arguments given against localist and grandmother cell coding schemes. He revisited the results of several single-cell studies, arguing that they do not support distributed representations. We praise the contribution of Bowers (2009) for joining evidence from psychological modeling and neurophysiological recordings, but we disagree with several of his claims. In this comment, we argue that distinctions between distributed, localist, and grandmother cell coding can be troublesome with real data. Moreover, these distinctions seem to be lying within the same continuum, and we argue that it may be sensible to characterize coding schemes with a sparseness measure. We further argue that there may not be a unique coding scheme implemented in all brain areas and for all possible functions. In particular, current evidence suggests that the brain may use distributed codes in primary sensory areas and sparser and invariant representations in higher areas. Keywords: grandmother cell, visual perception, memory, sparseness, neural coding Supplemental materials: http://dx. doi.org/ 10.1037/a0016917.supp

The ability to distinguish between negative, positive and neutral valence is a key part of emotion perception. Emotional valence has conceptual meaning that supersedes any particular type of stimulus, although it is typically captured experimentally in association with particular tasks. We sought to identify neural encoding for task-invariant emotional valence. We evaluated whether high-gamma responses (HGRs) to visually displayed words conveying emotions could be used to decode emotional valence from HGRs to facial expressions. Intracranial electroencephalography was recorded from 14 individuals while they participated in two tasks, one involving reading words with positive, negative, and neutral valence, and the other involving viewing faces with positive, negative, and neutral facial expressions. Quadratic discriminant analysis was used to identify information in the HGR that differentiates the three emotion conditions. A classifier was trained on the emotional valence labels from one task and was cross-validated on data from the same task (within-task classifier) as well as the other task (between-task classifier). Emotional valence could be decoded in the left medial orbitofrontal cortex and middle temporal gyrus, both using within-task classifiers and between-task classifiers. These observations suggest the presence of task-independent emotional valence information in the signals from these regions. [ABSTRACT FROM AUTHOR]

The primate inferior temporal cortex contains neurons that respond more strongly to faces than to other objects. Termed "face neurons," these neurons are thought to be selective for faces as a semantic category. However, face neurons also partly respond to clocks, fruits, and single eyes, raising the question of whether face neurons are better described as selective for visual features related to faces but dissociable from them. We used a recently described algorithm, XDream, to evolve stimuli that strongly activated face neurons. XDream leverages a generative neural network that is not limited to realistic objects. Human participants assessed images evolved for face neurons and for nonface neurons and natural images depicting faces, cars, fruits, etc. Evolved images were consistently judged to be distinct from real faces. Images evolved for face neurons were rated as slightly more similar to faces than images evolved for nonface neurons. There was a correlation among natural images between face neuron activity and subjective "faceness" ratings, but this relationship did not hold for face neuron-evolved images, which triggered high activity but were rated low in faceness. Our results suggest that so-called face neurons are better described as tuned to visual features rather than semantic categories. [ABSTRACT FROM AUTHOR]

Author(s): Gabriel Kreiman [1]; Christof Koch [1]; Itzhak Fried (corresponding author) [2] Vivid visual images can be voluntarily generated in our minds in the absence of simultaneous visual input. While [...]