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Individual data analysis and Unified Theories of Cognition: A methodological proposal
Unified theories regularly appear in psychology. They also regularly fail to fulfil all of their goals. Newell (1990) called for their revival, using computer modelling as a way to avoid the pitfalls of previous attempts. His call, embodied in the Soar project has so far, however, failed to produce the breakthrough it promised. One of the reasons for the lack of success of Newell’s approach is that the methodology commonly used in psychology, based on controlling potentially confounding variables by using group data, is not the best way forward for developing unified theories of cognition. Instead, we propose an approach where (a) the problems related to group averages are alleviated by analysing subjects individually; (b) there is a close interaction between theory building and experimentation; and (c) computer technology is used to routinely test versions of the theory on a wide range of data. The advantages of this approach heavily outweigh the disadvantages
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Learning long-range spatial dependencies with horizontal gated-recurrent units
Progress in deep learning has spawned great successes in many engineering
applications. As a prime example, convolutional neural networks, a type of
feedforward neural networks, are now approaching -- and sometimes even
surpassing -- human accuracy on a variety of visual recognition tasks. Here,
however, we show that these neural networks and their recent extensions
struggle in recognition tasks where co-dependent visual features must be
detected over long spatial ranges. We introduce the horizontal gated-recurrent
unit (hGRU) to learn intrinsic horizontal connections -- both within and across
feature columns. We demonstrate that a single hGRU layer matches or outperforms
all tested feedforward hierarchical baselines including state-of-the-art
architectures which have orders of magnitude more free parameters. We further
discuss the biological plausibility of the hGRU in comparison to anatomical
data from the visual cortex as well as human behavioral data on a classic
contour detection task.Comment: Published at NeurIPS 2018
https://papers.nips.cc/paper/7300-learning-long-range-spatial-dependencies-with-horizontal-gated-recurrent-unit
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
Attention mechanisms in the CHREST cognitive architecture
In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organises information acquired by direct experience from the world in the form of chunks. These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention mechanism combines bottom-up and top-down heuristics from internal and external sources of information. We describe some experimental evidence demonstrating the correspondence of CHREST’s perceptual mechanisms with those of human subjects. Finally, we discuss how visual attention can play an important role in actions carried out by human experts in domains such as chess
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
Spike-based learning with memristive devices in neuromorphic computing
architectures typically uses learning circuits that require overlapping pulses
from pre- and post-synaptic nodes. This imposes severe constraints on the
length of the pulses transmitted in the network, and on the network's
throughput. Furthermore, most of these circuits do not decouple the currents
flowing through memristive devices from the one stimulating the target neuron.
This can be a problem when using devices with high conductance values, because
of the resulting large currents. In this paper we propose a novel circuit that
decouples the current produced by the memristive device from the one used to
stimulate the post-synaptic neuron, by using a novel differential scheme based
on the Gilbert normalizer circuit. We show how this circuit is useful for
reducing the effect of variability in the memristive devices, and how it is
ideally suited for spike-based learning mechanisms that do not require
overlapping pre- and post-synaptic pulses. We demonstrate the features of the
proposed synapse circuit with SPICE simulations, and validate its learning
properties with high-level behavioral network simulations which use a
stochastic gradient descent learning rule in two classification tasks.Comment: 18 Pages main text, 9 pages of supplementary text, 19 figures.
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