70 research outputs found

    The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons

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    abstract: The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.View the article as published at http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00186/ful

    Neuromorphic Computing Applications in Robotics

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    Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, this work implements associative memory with an Unmanned Ground Vehicle (UGV) and neuromorphic hardware, specifically Intel’s Loihi, for an online learning scenario. This system emulates the classic associative learning in rats using the UGV in place of the rats. In specific, it successfully reproduces the fear conditioning with no pretraining procedure or labeled datasets. The UGV is rendered capable of autonomously learning the cause-and-effect relationship of the light stimulus and vibration stimulus and exhibiting a movement response to demonstrate the memorization. Hebbian learning dynamics are used to update the synaptic weights during the associative learning process. The Intel Loihi chip is integrated with this online learning system for processing visual signals with a specialized neural assembly. While processing, the Loihi’s average power usages for computing logic and memory are 30 mW and 29 mW, respectively

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

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    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
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