Genealogy of the "grandmother cell"

Compositionality in perception: A framework

Perception involves the processing of content or information about the world. In what form is this content represented? I argue that perception is widely compositional. The perceptual system represents many stimulus features (including shape, orientation, and motion) in terms of combinations of other features (such as shape parts, slant and tilt, common and residual motion vectors). But compositionality can take a variety of forms. The ways in which perceptual representations compose are markedly different from the ways in which sentences or thoughts are thought to be composed. I suggest that the thesis that perception is compositional is not itself a concrete hypothesis with specific predictions; rather it affords a productive framework for developing and evaluating specific empirical hypotheses about the form and content of perceptual representations. The question is not just whether perception is compositional, but how. Answering this latter question can provide fundamental insights into perception. This article is categorized under: Philosophy > Representation Philosophy > Foundations of Cognitive Science Psychology > Perception and Psychophysics.

Contrastive learning explains the emergence and function of visual category-selective regions

Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.

Visual stimulus structure, visual system neural activity, and visual behavior in young human infants

In visual perception and information processing, a cascade of associations is hypothesized to flow from the structure of the visual stimulus to neural activity along the retinogeniculostriate visual system to behavior and action. Do visual perception and information processing adhere to this cascade near the beginning of life? To date, this three-stage hypothetical cascade has not been comprehensively tested in infants. In two related experiments, we attempted to expose this cascade in 6-month-old infants. Specifically, we presented infants with two levels of visual stimulus intensity, we measured electrical activity at the infant cortex, and we assessed infants' preferential looking behavior. Chromatic saturation provided a convenient stimulus dimension to test the cascade because greater saturation is known to excite increased activity in the primate visual system and is generally hypothesized to stimulate visual preference. Experiment 1 revealed that infants prefer (look longer) at the more saturated of two colors otherwise matched in hue and brightness. Experiment 2 showed increased aggregate neural cortical excitation in infants (and adults) to the more saturated of the same pair of colors. Thus, experiments 1 and 2 taken together confirm a cascade: Visual stimulation of relatively greater intensity evokes relatively greater levels of bioelectrical cortical activity which in turn is associated with relatively greater visual attention. As this cascade obtains near the beginning of life, it helps to account for early visual preferences and visual information processing.

Electrophysiological Evidence for Interhemispheric Connectivity and Communication in Young Human Infants

Little is known empirically about connectivity and communication between the two hemispheres of the brain in the first year of life, and what theoretical opinion exists appears to be at variance with the meager extant anatomical evidence. To shed initial light on the question of interhemispheric connectivity and communication, this study investigated brain correlates of interhemispheric transmission of information in young human infants. We analyzed EEG data from 12 4-month-olds undergoing a face-related oddball ERP protocol. The activity in the contralateral hemisphere differed between odd-same and odd-difference trials, with the odd-different response being weaker than the response during odd-same trials. The infants' contralateral hemisphere "recognized" the odd familiar stimulus and "discriminated" the odd-different one. These findings demonstrate connectivity and communication between the two hemispheres of the brain in the first year of life and lead to a better understanding of the functional integrity of the developing human infant brain.

Perception of color in primates: A conceptual color neurons hypothesis

Perception of color by humans and other primates is a complex problem, studied by neurophysiology, psychophysiology, psycholinguistics, and even philosophy. Being mostly trichromats, simian primates have three types of opsin proteins, expressed in cone neurons in the eye, which allow for the sensing of color as the physical wavelength of light. Further, in neural networks of the retina, the coding principle changes from three types of sensor proteins to two opponent channels: activity of one type of neuron encode the evolutionarily ancient blue-yellow axis of color stimuli, and another more recent evolutionary channel, encoding the axis of red-green color stimuli. Both color channels are distinctive in neural organization at all levels from the eye to the neocortex, where it is thought that the perception of color (as philosophical qualia) emerges from the activity of some neuron ensembles. Here, using data from neurophysiology as a starting point, we propose a hypothesis on how the perception of color can be encoded in the activity of certain neurons in the neocortex. These conceptual neurons, herein referred to as 'color neurons', code only the hue of the color of visual stimulus, similar to place cells and number neurons, already described in primate brains. A case study with preliminary, but direct, evidence for existing conceptual color neurons in the human brain was published in 2008. We predict that the upcoming studies in non-human primates will be more extensive and provide a more detailed description of conceptual color neurons.

Cortical maps as a fundamental neural substrate for visual representation

Visual perception is the product of serial hierarchical processing, parallel processing, and remapping on a dynamic network involving several topographically organized cortical visual areas. Here, we will focus on the topographical organization of cortical areas and the different kinds of visual maps found in the primate brain. We will interpret our findings in light of a broader representational framework for perception. Based on neurophysiological data, our results do not support the notion that vision can be explained by a strict representational model, where the objective visual world is faithfully represented in our brain. On the contrary, we find strong evidence that vision is an active and constructive process from the very initial stages taking place in the eye and from the very initial stages of our development. A constructive interplay between perceptual and motor systems (e.g., during saccadic eye movements) is actively learnt from early infancy and ultimately provides our fluid stable visual perception of the world.

Maximal Dependence Capturing as a Principle of Sensory Processing

Sensory inputs conveying information about the environment are often noisy and incomplete, yet the brain can achieve remarkable consistency in recognizing objects. Presumably, transforming the varying input patterns into invariant object representations is pivotal for this cognitive robustness. In the classic hierarchical representation framework, early stages of sensory processing utilize independent components of environmental stimuli to ensure efficient information transmission. Representations in subsequent stages are based on increasingly complex receptive fields along a hierarchical network. This framework accurately captures the input structures; however, it is challenging to achieve invariance in representing different appearances of objects. Here we assess theoretical and experimental inconsistencies of the current framework. In its place, we propose that individual neurons encode objects by following the principle of maximal dependence capturing (MDC), which compels each neuron to capture the structural components that contain maximal information about specific objects. We implement the proposition in a computational framework incorporating dimension expansion and sparse coding, which achieves consistent representations of object identities under occlusion, corruption, or high noise conditions. The framework neither requires learning the corrupted forms nor comprises deep network layers. Moreover, it explains various receptive field properties of neurons. Thus, MDC provides a unifying principle for sensory processing.

The Simpsons Neuron: A Case Study Exploring Neuronal Coding and the Scientific Method for Introductory and Advanced Neuroscience Courses

A fictitious patient, Miguel, has been diagnosed with drug-resistant epilepsy and is awaiting neurosurgery. While in the hospital, Miguel agrees to participate in a research study in which depth electrodes are used to record neuronal activity in response to a range of stimuli. Interestingly, a neuron is identified that seems to respond selectively to video clips of the animated satirical TV show The Simpsons. Students are challenged to make observations, formulate and revise hypotheses, and interpret data, excerpted from an authentic dataset derived from actual patients in a 2008 Science paper. Students then consider implications for these data, evaluate their ability to generalize to non-human (rodent) models, and speculate about future directions for this research. Adaptations of this case have been implemented in introductory and advanced neuroscience courses. Students responded positively to the case, and reported gains in science competence and identity, particularly in the introductory courses. Suggestions for implementation and adaptation of this experience are offered. While this case has been implemented in undergraduate neuroscience courses, it might also be used in physiology, psychology, biology, research methods, or clinical courses.

A Biomorphic Model of Cortical Column for Content-Based Image Retrieval

How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of “triggering” a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces.

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

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

Whether Mirror and Conceptual Neurons are Myths? Sparse vs. Distributed Neuronal Representations

Multi-layer neural networks, mirror neurons, and gnostic neurons are concepts that assign neural representations to mental representations of percepts and inner sensations. However, none of these approaches alone can explain the higher mental functions, which we observe in natural minds from the third and first-person perspectives through introspection. Recent concepts of preservation of chemical traces of sensory stimuli and hierarchical structures of postsynaptic associations represented by specifically organized groups of neurons combine these concepts and effectively explain much more complex mental functions. To find an operative model and understand how knowledge in the mind creates conscious sensations, we explain how perceptions, sensory impressions, and environment models gain their neural representations. It was pointed out ways to detect the similarity of structures representing previously remembered patterns to the mental and neuronal representations of new perceptions, ways of their associations, and principles of information processing. Supplemented, presented in earlier works, concepts of competition of representations stimulation and factors stimulating their action explain the mind's complex functions, including speech production and recognition. We postulate that using new methods of modelling the neural network's functions through the parallel physical process allows creating a physical model of natural and artificial, conscious, intelligent minds.

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

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

Brain cell somatic gene recombination and its phylogenetic foundations

A new form of somatic gene recombination (SGR) has been identified in the human brain that affects the Alzheimer's disease gene, amyloid precursor protein (APP). SGR occurs when a gene sequence is cut and recombined within a single cell's genomic DNA, generally independent of DNA replication and the cell cycle. The newly identified brain SGR produces genomic complementary DNAs (gencDNAs) lacking introns, which integrate into locations distinct from germline loci. This brief review will present an overview of likely related recombination mechanisms and genomic cDNA-like sequences that implicate evolutionary origins for brain SGR. Similarities and differences exist between brain SGR and VDJ recombination in the immune system, the first identified SGR form that now has a well-defined enzymatic machinery. Both require gene transcription, but brain SGR uses an RNA intermediate and reverse transcriptase (RT) activity, which are characteristics shared with endogenous retrotransposons. The identified gencDNAs have similarities to other cDNA-like sequences existing throughout phylogeny, including intron-less genes and inactive germline processed pseudogenes, with likely overlapping biosynthetic processes. gencDNAs arise somatically in an individual to produce multiple copies; can be functional; appear most frequently within postmitotic cells; have diverse sequences; change with age; and can change with disease state. Normally occurring brain SGR may represent a mechanism for gene optimization and long-term cellular memory, whereas its dysregulation could underlie multiple brain disorders and, potentially, other diseases like cancer. The involvement of RT activity implicates already Food and Drug Administration-approved RT inhibitors as possible near-term interventions for managing SGR-associated diseases and suggest next-generation therapeutics targeting SGR elements.

Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation

Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate their relationship under common conditions, we proposed a representation invariance analysis approach based on data augmentation technology. Firstly, the original image library was expanded by data augmentation. The representation invariances of CNNs and the ventral visual stream were then studied by comparing the similarities of the corresponding layer features of CNNs and the prediction performance of visual encoding models based on functional magnetic resonance imaging (fMRI) before and after data augmentation. Our experimental results suggest that the architecture of CNNs, combinations of convolutional and fully-connected layers, developed representation invariance of CNNs. Remarkably, we found representation invariance belongs to all successive stages of the ventral visual stream. Hence, the internal correlation between CNNs and the human visual system in representation invariance was revealed. Our study promotes the advancement of invariant representation of computer vision and deeper comprehension of the representation invariance mechanism of human visual information processing.

An Annotated Journey through Modern Visual Neuroscience

Recent advances in microscopy, genetics, physiology, and data processing have expanded the scope and accelerated the pace of discovery in visual neuroscience. However, the pace of discovery and the ever increasing number of published articles can present a serious issue for both trainees and senior scientists alike: with each passing year the fog of progress thickens, making it easy to lose sight of important earlier advances. As part of this special issue of the Journal of Neuroscience commemorating the 50th anniversary of SfN, here, we provide a variation on Stephen Kuffler's Oldies but Goodies classic reading list, with the hope that by looking back at highlights in the field of visual neuroscience we can better define remaining gaps in our knowledge and thus guide future work. We also hope that this article can serve as a resource that will aid those new to the field to find their bearings.

Researchers Keep Rejecting Grandmother Cells after Running the Wrong Experiments: The Issue Is How Familiar Stimuli Are Identified

There is widespread agreement in neuroscience and psychology that the visual system identifies objects and faces based on a pattern of activation over many neurons, each neuron being involved in representing many different categories. The hypothesis that the visual system includes finely tuned neurons for specific objects or faces for the sake of identification, so-called "grandmother cells", is widely rejected. Here it is argued that the rejection of grandmother cells is premature. Grandmother cells constitute a hypothesis of how familiar visual categories are identified, but the primary evidence against this hypothesis comes from studies that have failed to observe neurons that selectively respond to unfamiliar stimuli. These findings are reviewed and it is shown that they are irrelevant. Neuroscientists need to better understand existing models of face and object identification that include grandmother cells and then compare the selectivity of these units with single neurons responding to stimuli that can be identified.

Memory engrams: Recalling the past and imagining the future

In 1904, Richard Semon introduced the term "engram" to describe the neural substrate for storing memories. An experience, Semon proposed, activates a subset of cells that undergo off-line, persistent chemical and/or physical changes to become an engram. Subsequent reactivation of this engram induces memory retrieval. Although Semon's contributions were largely ignored in his lifetime, new technologies that allow researchers to image and manipulate the brain at the level of individual neurons has reinvigorated engram research. We review recent progress in studying engrams, including an evaluation of evidence for the existence of engrams, the importance of intrinsic excitability and synaptic plasticity in engrams, and the lifetime of an engram. Together, these findings are beginning to define an engram as the basic unit of memory.

Deep Self-Evolution Clustering

Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar. Specifically, similarities between pairwise patterns are defined by the dot product between indicator features which are generated by a deep neural network (DNN). To learn informative representations for clustering, clustering constraints are imposed on the indicator features to represent specific concepts with specific representations. Since the ground-truth similarities are unavailable in clustering, an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise patterns and to train the DNN alternately. Consequently, the indicator features tend to be one-hot vectors and the patterns can be clustered by locating the largest response of the learned indicator features. Extensive experiments strongly evidence that DSEC outperforms current models on twelve popular image, text and audio datasets consistently.

The neural mechanisms of face processing: cells, areas, networks, and models

Since its discovery, the face-processing network in the brain of the macaque monkey has emerged as a model system that allowed for major neural mechanisms of face recognition to be identified - with implications for object recognition at large. Populations of face cells encode faces through broad tuning curves, whose shapes change over time. Face representations differ qualitatively across faces areas, and we not only understand the global organization of these specializations, but also some of the transformations between face areas, both feed-forward and feed-back, and the computational principles behind face representations and transformations. Facial information is combined with physical features and mnemonic features in extensions of the core network, which forms an early part of the primate social brain.

Removing a single neuron in a vertebrate brain forever abolishes an essential behavior

The Mauthner cell is the largest neuron known in the vertebrate brain and, in fish, mediates rapid escape behavior. Ablating this neuron has repeatedly failed to eliminate rapid escapes, a survival role of these escapes has not been supported experimentally, and it is unknown which advantage the enormous size and complexity of the cell conveys. By taking care to ensure ablations remove not only the soma but also the giant axon, we find that rapid escapes are lost forever and that this loss directly affects survival in predator–prey assays. The Mauthner cell thus is an example in which a survival-critical function depends on an individual neuron whose axon appears to have unusual capacities to remain functional after severe injury.

The giant Mauthner (M) cell is the largest neuron known in the vertebrate brain. It has enabled major breakthroughs in neuroscience but its ultimate function remains surprisingly unclear: An actual survival value of M cell-mediated escapes has never been supported experimentally and ablating the cell repeatedly failed to eliminate all rapid escapes, suggesting that escapes can equally well be driven by smaller neurons. Here we applied techniques to simultaneously measure escape performance and the state of the giant M axon over an extended period following ablation of its soma. We discovered that the axon survives remarkably long and remains still fully capable of driving rapid escape behavior. By unilaterally removing one of the two M axons and comparing escapes in the same individual that could or could not recruit an M axon, we show that the giant M axon is essential for rapid escapes and that its loss means that rapid escapes are also lost forever. This allowed us to directly test the survival value of the M cell-mediated escapes and to show that the absence of this giant neuron directly affects survival in encounters with a natural predator. These findings not only offer a surprising solution to an old puzzle but demonstrate that even complex brains can trust vital functions to individual neurons. Our findings suggest that mechanisms must have evolved in parallel with the unique significance of these neurons to keep their axons alive and connected.

Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing

Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well-known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target people's positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance-approximately two-thirds of noise ceiling accuracy--indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others' traits and states.

The Value of Failure in Science: The Story of Grandmother Cells in Neuroscience

The annals of science are filled with successes. Only in footnotes do we hear about the failures, the cul-de-sacs, and the forgotten ideas. Failure is how research advances. Yet it hardly features in theoretical perspectives on science. That is a mistake. Failures, whether clear-cut or ambiguous, are heuristically fruitful in their own right. Thinking about failure questions our measures of success, including the conceptual foundations of current practice, that can only be transient in an experimental context. This article advances the heuristics of failure analysis, meaning the explicit treatment of certain ideas or models as failures. The value of failures qua being a failure is illustrated with the example of grandmother cells; the contested idea of a hypothetical neuron that encodes a highly specific but complex stimulus, such as the image of one's grandmother. Repeatedly evoked in popular science and maintained in textbooks, there is sufficient reason to critically review the theoretical and empirical background of this idea.

Associative responses to visual shape stimuli in the mouse auditory cortex

Humans can recall various aspects of a characteristic sound as a whole when they see a visual shape stimulus that has been intimately associated with the sound. In subjects with audio-visual associative memory, auditory responses that code the associated sound may be induced in the auditory cortex in response to presentation of the associated visual shape stimulus. To test this possibility, mice were pre-exposed to a combination of an artificial sound mimicking a cat’s “meow” and a visual shape stimulus of concentric circles or stars for more than two weeks, since such passive exposure is known to be sufficient for inducing audio-visual associative memory in mice. After the exposure, we anesthetized the mice, and presented them with the associated visual shape stimulus. We found that associative responses in the auditory cortex were induced in response to the visual stimulus. The associative auditory responses were observed when complex sounds such as “meow” were used for formation of audio-visual associative memory, but not when a pure tone was used. These results suggest that associative auditory responses in the auditory cortex represent the characteristics of the complex sound stimulus as a whole.

The neuroscience of vision and pain: evolution of two disciplines

Research in the neuroscience of pain perception and visual perception has taken contrasting paths. The contextual and the social aspects of pain judgements predisposed pain researchers to develop computational and functional accounts early, while vision researchers tended to simple localizationist or descriptive approaches first. Evolutionary thought was applied to distinct domains, such as game-theoretic approaches to cheater detection in pain research, versus vision scientists' studies of comparative visual ecologies. Both fields now contemplate current motor or decision-based accounts of perception, particularly predictive coding. Vision researchers do so without the benefit of earlier attention to social and motivational aspects of vision, while pain researchers lack a comparative behavioural ecology of pain, the normal incidence and utility of responses to tissue damage. Hybrid hypotheses arising from predictive coding as used in both domains are applied to some perplexing phenomena in pain perception to suggest future directions. The contingent and predictive interpretation of complex sensations, in such domains as 'runner's high', multiple cosmetic procedures, self-harm and circadian rhythms in pain sensitivity is one example. The second, in an evolutionary time frame, considers enhancement of primary perception and expression of pain in social species, when expressions of pain might reliably elicit useful help. This article is part of the Theo Murphy meeting issue 'Evolution of mechanisms and behaviour important for pain'.

Neuroscience: The Key to Consciousness May Not Be under the Streetlight

Key theories of consciousness predict that the prefrontal cortex (PFC) plays important roles, but there has been relatively little causal evidence showing that manipulation of activity in the region can broadly affect conscious experiences. A new study provides crucial findings to help resolve this issue, showing that direct pharmacological stimulation of PFC restores wakefulness in anesthetized rats.

Locally connected spiking neural networks for unsupervised feature learning

In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via inhibitory interactions to learn features from different locations of the input space. These locally-connected SNNs (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore a biologically inspired n-gram classification approach allowing parallel processing over various patches of the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which respectively match state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large numbers of synapses and neurons. Our results have been obtained using the BindsNET library, which allows efficient machine learning implementations of spiking neural networks.

Minimal Recognizable Configurations Elicit Category-selective Responses in Higher Order Visual Cortex

Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. The rationale is based on previous research showing that a set of representations, termed "minimal recognizable configurations" (MIRCs), which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.

Neuropsychology of Consciousness: Some History and a Few New Trends

Consciousness is a global activity of the nervous system. Its physiological and pathological mechanisms have been studied in relation to the natural sleep-wake cycle and various forms of normal or morbid unconsciousness, mainly in neurophysiology and clinical neurology. Neuropsychology has been more interested in specific higher brain functions, such as perception and memory and their disorders, rather than in consciousness per se. However, neuropsychology has been at the forefront in the identification of conscious and unconscious components in the processing of sensory and mnestic information. The present review describes some historical steps in the formulation of consciousness as a global brain function with arousal and content as principal ingredients, respectively, instantiated in the subcortex and the neocortex. It then reports a few fresh developments in neuropsychology and cognitive neuroscience which emphasize the importance of the hippocampus for thinking and dreaming. Non-neocortical structures may contribute to the contents of consciousness more than previously believed.

The unreasonable effectiveness of small neural ensembles in high-dimensional brain

Complexity is an indisputable, well-known, and broadly accepted feature of the brain. Despite the apparently obvious and widely-spread consensus on the brain complexity, sprouts of the single neuron revolution emerged in neuroscience in the 1970s. They brought many unexpected discoveries, including grandmother or concept cells and sparse coding of information in the brain. In machine learning for a long time, the famous curse of dimensionality seemed to be an unsolvable problem. Nevertheless, the idea of the blessing of dimensionality becomes gradually more and more popular. Ensembles of non-interacting or weakly interacting simple units prove to be an effective tool for solving essentially multidimensional and apparently incomprehensible problems. This approach is especially useful for one-shot (non-iterative) correction of errors in large legacy artificial intelligence systems and when the complete re-training is impossible or too expensive. These simplicity revolutions in the era of complexity have deep fundamental reasons grounded in geometry of multidimensional data spaces. To explore and understand these reasons we revisit the background ideas of statistical physics. In the course of the 20th century they were developed into the concentration of measure theory. The Gibbs equivalence of ensembles with further generalizations shows that the data in high-dimensional spaces are concentrated near shells of smaller dimension. New stochastic separation theorems reveal the fine structure of the data clouds. We review and analyse biological, physical, and mathematical problems at the core of the fundamental question: how can high-dimensional brain organise reliable and fast learning in high-dimensional world of data by simple tools? To meet this challenge, we outline and setup a framework based on statistical physics of data. Two critical applications are reviewed to exemplify the approach: one-shot correction of errors in intellectual systems and emergence of static and associative memories in ensembles of single neurons. Error correctors should be simple; not damage the existing skills of the system; allow fast non-iterative learning and correction of new mistakes without destroying the previous fixes. All these demands can be satisfied by new tools based on the concentration of measure phenomena and stochastic separation theory. We show how a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content of high-dimensional data, ii) simultaneous separation of several uncorrelated informational items from a large set of stimuli, and iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organisation of complex memories in ensembles of single neurons.

Barlow versus Hebb: When is it time to abandon the notion of feature detectors and adopt the cell assembly as the unit of cognition?

Research on how information is encoded by the brain is largely based on studies of feature detector properties of single neurons, but considerable new data shows that single neurons in many brain areas have mixed selectivity for multiple features and change their tuning properties across realistic information processing situations. Here I consider new approaches that explore cell assemblies as the units of information processing and how these approaches are revealing the structure and organization of neural representations in perception and cognition.

Nonsynaptic plasticity model of long-term memory engrams

Using steady-state electrical properties of non-ohmic dendrite based on cable theory, we derive electrotonic potentials that do not change over time and are localized in space. We hypothesize that clusters of such stationary, local and permanent pulses are the electrical signatures of enduring memories which are imprinted through nonsynaptic plasticity, encoded through epigenetic mechanisms, and decoded through electrotonic processing. We further hypothesize how retrieval of an engram is made possible by integration of these permanently imprinted standing pulses in a neural circuit through neurotransmission in the extracellular space as part of conscious recall that acts as a guiding template in the reconsolidation of long-term memories through novelty characterized by uncertainty that arises when new fragments of memories reinstate an engram by way of nonsynaptic plasticity that permits its destabilization. Collectively, these findings seem to reinforce this hypothesis that electrotonic processing in non-ohmic dendrites yield insights into permanent electrical signatures that could reflect upon enduring memories as fragments of long-term memory engrams.

The behavioral and neural binding phenomena during visuomotor integration of angry facial expressions

Different parts of our brain code the perceptual features and actions related to an object, causing a binding problem, in which the brain has to integrate information related to an event without any interference regarding the features and actions involved in other concurrently processed events. Using a paradigm similar to Hommel, who revealed perception-action bindings, we showed that emotion could bind with motor actions when relevant, and in specific conditions, irrelevant for the task. By adapting our protocol to a functional Magnetic Resonance Imaging paradigm we investigated, in the present study, the neural bases of the emotion-action binding with task-relevant angry faces. Our results showed that emotion bound with motor responses. This integration revealed increased activity in distributed brain areas involved in: (i) memory, including the hippocampi; (ii) motor actions with the precentral gyri; (iii) and emotion processing with the insula. Interestingly, increased activations in the cingulate gyri and putamen, highlighted their potential key role in the emotion-action binding, due to their involvement in emotion processing, motor actions, and memory. The present study confirmed our previous results and point out for the first time the functional brain activity related to the emotion-action association.

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons

Vision is a challenging problem because the same object can produce a variety of images on the retina, mixing signals related to its identity with signals related to its viewing attributes, such as size, position, rotation, etc. Precisely how the brain separates these signals to form an efficient representation is unknown. Here, we show that single neurons in high-level visual cortex encode object identity and attribute multiplicatively and that doing so allows for better decoding of each signal.

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively.

Face Processing Systems: From Neurons to Real-World Social Perception

Primate face processing depends on a distributed network of interlinked face-selective areas composed of face-selective neurons. In both humans and macaques, the network is divided into a ventral stream and a dorsal stream, and the functional similarities of the areas in humans and macaques indicate they are homologous. Neural correlates for face detection, holistic processing, face space, and other key properties of human face processing have been identified at the single neuron level, and studies providing causal evidence have established firmly that face-selective brain areas are central to face processing. These mechanisms give rise to our highly accurate familiar face recognition but also to our error-prone performance with unfamiliar faces. This limitation of the face system has important implications for consequential situations such as eyewitness identification and policing.

Neglected but Exciting Concepts in Developmental and Neurobiological Psychology

This review provides an evaluative overview of five concepts specific to developmental and neurobiological psychology that are found to be largely overlooked in current textbooks. A sample of 19 introductory psychology texts was surveyed to develop a list, including glial cell signaling, grandmother cells, memory reconsolidation, brain plasticity, and moral judgements by infants. These topics are relatively new, have proven to be of high impact in their respective fields, but are rarely discussed in psychological textbooks or by instructors in the classroom. In addition to a brief, but detailed background on each of the concepts, potential textbook chapters and classroom topics that would benefit from a discussion of these concepts are identified. Finally, this review briefly addresses possible ways for textbook authors to incorporate these new topics in future editions of texts without drastically increasing the overall length of the text.

The Biology of Forgetting-A Perspective

Pioneering research studies, beginning with those using Drosophila, have identified several molecular and cellular mechanisms for active forgetting. The currently known mechanisms for active forgetting include neurogenesis-based forgetting, interference-based forgetting, and intrinsic forgetting, the latter term describing the brain's chronic signaling systems that function to slowly degrade molecular and cellular memory traces. The best-characterized pathway for intrinsic forgetting includes "forgetting cells" that release dopamine onto engram cells, mobilizing a signaling pathway that terminates in the activation of Rac1/Cofilin to effect changes in the actin cytoskeleton and neuron/synapse structure. Intrinsic forgetting may be the default state of the brain, constantly promoting memory erasure and competing with processes that promote memory stability like consolidation. A better understanding of active forgetting will provide insights into the brain's memory management system and human brain disorders that alter active forgetting mechanisms.

Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding

The vicinity of phase transitions selectively amplifies weak stimuli, yielding optimal sensitivity to distinguish external input. Along with this enhanced sensitivity, enhanced levels of fluctuations at criticality reduce the specificity of the response. Given that the specificity of the response is largely compromised when the sensitivity is maximal, the overall benefit of criticality for signal processing remains questionable. Here, it is shown that this impasse can be solved by heterogeneous systems incorporating functional diversity, in which critical and subcritical components coexist. The subnetwork of critical elements has optimal sensitivity, and the subnetwork of subcritical elements has enhanced specificity. Combining segregated features extracted from the different subgroups, the resulting collective response can maximize the trade-off between sensitivity and specificity measured by the dynamic-range-to-noise ratio. Although numerous benefits can be observed when the entire system is critical, our results highlight that optimal performance is obtained when only a small subset of the system is at criticality.

Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability

A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.

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.

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.