Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception

Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition

Semantic memory

The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions

Cortex, countercurrent context, and dimensional integration of lifetime memory

The correlation between relative neocortex size and longevity in mammals encourages a search for a cortical function specifically related to the life-span. A candidate in the domain of permanent and cumulative memory storage is proposed and explored in relation to basic aspects of cortical organization. The pattern of cortico-cortical connectivity between functionally specialized areas and the laminar organization of that connectivity converges on a globally coherent representational space in which contextual embedding of information emerges as an obligatory feature of cortical function. This brings a powerful mode of inductive knowledge within reach of mammalian adaptations, a mode which combines item specificity with classificatory generality. Its neural implementation is proposed to depend on an obligatory interaction between the oppositely directed feedforward and feedback currents of cortical activity, in countercurrent fashion. Direct interaction of the two streams along their cortex-wide local interface supports a scheme of "contextual capture" for information storage responsible for the lifelong cumulative growth of a uniquely cortical form of memory termed "personal history." This approach to cortical function helps elucidate key features of cortical organization as well as cognitive aspects of mammalian life history strategies

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

Gradient-free activation maximization for identifying effective stimuli

A fundamental question for understanding brain function is what types of stimuli drive neurons to fire. In visual neuroscience, this question has also been posted as characterizing the receptive field of a neuron. The search for effective stimuli has traditionally been based on a combination of insights from previous studies, intuition, and luck. Recently, the same question has emerged in the study of units in convolutional neural networks (ConvNets), and together with this question a family of solutions were developed that are generally referred to as "feature visualization by activation maximization." We sought to bring in tools and techniques developed for studying ConvNets to the study of biological neural networks. However, one key difference that impedes direct translation of tools is that gradients can be obtained from ConvNets using backpropagation, but such gradients are not available from the brain. To circumvent this problem, we developed a method for gradient-free activation maximization by combining a generative neural network with a genetic algorithm. We termed this method XDream (EXtending DeepDream with real-time evolution for activation maximization), and we have shown that this method can reliably create strong stimuli for neurons in the macaque visual cortex (Ponce et al., 2019). In this paper, we describe extensive experiments characterizing the XDream method by using ConvNet units as in silico models of neurons. We show that XDream is applicable across network layers, architectures, and training sets; examine design choices in the algorithm; and provide practical guides for choosing hyperparameters in the algorithm. XDream is an efficient algorithm for uncovering neuronal tuning preferences in black-box networks using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table

The Complementary Brain: From Brain Dynamics To Conscious Experiences

How do our brains so effectively achieve adaptive behavior in a changing world? Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact, and suggests an alternative to the computer metaphor suggesting that brains are organized into independent modules. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are summarized.Defense Advanced Research Projects and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-1-0657

Long-range coupling of prefrontal cortex and visual (MT) or polysensory (STP) cortical areas in motion perception

To investigate how, where and when moving auditory cues interact with the perception of object-motion during self-motion, we conducted psychophysical, MEG, and fMRI experiments in which the subjects viewed nine textured objects during simulated forward self-motion. On each trial, one object was randomly assigned its own looming motion within the scene. Subjects reported which of four labeled objects had independent motion within the scene in two conditions: (1) visual information only and (2) with additional moving- auditory cue. In MEG, comparison of the two conditions showed: (i) MT activity is similar across conditions, (ii) late after the stimulus presentation there is additional activity in the auditory cue condition ventral to MT, (iii) with the auditory cue, the right auditory cortex (AC) shows early activity together with STS, (iv) these two activities have different time courses and the STS signals occur later in the epoch together with frontal activity in the right hemisphere, (v) for the visual-only condition activity in PPC (posterior parietal cortex) is stronger than in the auditory-cue condition. fMRI conducted for visual-only condition reveals activations in a network of parietal and frontal areas and in MT. In addition, Dynamic Granger Causality analysis showed for auditory cues a strong connection of the AC with STP but not with MT suggesting binding of visual and auditory information at STP. Also, while in the visual-only condition PFC is connected with MT, in the auditory-cue condition PFC is connected to STP (superior temporal polysensory) area. These results indicate that PFC allocates attention to the “object” as a whole, in STP to a moving visual-auditory object, and in MT to a moving visual object.Accepted manuscrip

Idealized computational models for auditory receptive fields

This paper presents a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to enable invariance of receptive field responses under natural sound transformations and ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or the combination of a time-causal generalized Gammatone filter over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table