A survey of brain inspired technologies for engineering
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paper_title: SOAR: an architecture for general intelligence

paper_content:

Abstract The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.
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paper_title: Visual Place Recognition: A Survey

paper_content:

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.
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paper_title: The Symbol Grounding Problem

paper_content:

A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.
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paper_title: Connectionist AI, symbolic AI, and the brain

paper_content:

Connectionist AI systems are large networks of extremely simple numerical processors, massively interconnected and running in parallel. There has been great progress in the connectionist approach, and while it is still unclear whether the approach will succeed, it is also unclear exactly what the implications for cognitive science would be if it did succeed. In this paper I present a view of the connectionist approach that implies that the level of analysis at which uniform formal principles of cognition can be found is the subsymbolic level, intermediate between the neural and symbolic levels. Notions such as logical inference, sequential firing of production rules, spreading activation between conceptual units, mental categories, and frames or schemata turn out to provide approximate descriptions of the coarse-grained behaviour of connectionist systems. The implication is that symbol-level structures provide only approximate accounts of cognition, useful for description but not necessarily for constructing detailed formal models.
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paper_title: Cognitive architectures: Research issues and challenges

paper_content:

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area.
---
paper_title: SOAR: an architecture for general intelligence

paper_content:

Abstract The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.
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paper_title: Enhancing Intelligent Agents with Episodic Memory

paper_content:

For a human, episodic memory is a memory of past experiences that one gains over a lifetime. While episodic memory appears critical to human function, researchers have done little to explore the potential benefits for an artificially intelligent agent. In this research, we have added a task-independent, episodic memory to a cognitive architecture. To frame the research, we propose that episodic memory supports a set of cognitive capabilities that improve an agent's ability to sense its environment, reason, and learn. We demonstrate that episodic memory enables agents created with our architecture to employ these cognitive capabilities.
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paper_title: Tensor product variable binding and the representation of symbolic structures in connectionist systems

paper_content:

Abstract A general method, the tensor product representation, is defined for the connectionist representation of value/variable bindings. The technique is a formalization of the idea that a set of value/variable pairs can be represented by accumulating activity in a collection of units each of which computes the product of a feature of a variable and a feature of its value. The method allows the fully distributed representation of bindings and symbolic structures. Fully and partially localized special cases of the tensor product representation reduce to existing cases of connectionist representations of structured data. The representation rests on a principled analysis of structure; it saturates gracefully as larger structures are represented; it permits recursive construction of complex representations from simpler ones; it respects the independence of the capacities to generate and maintain multiple bindings in parallel; it extends naturally to continuous structures and continuous representational patterns; it permits values to also serve as variables; and it enables analysis of the interference of symbolic structures stored in associative memories. It has also served as the basis for working connectionist models of high-level cognitive tasks.
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paper_title: The Symbol Grounding Problem

paper_content:

A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.
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paper_title: Extending the soar cognitive architecture

paper_content:

One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. This paper presents the cognitive architecture approach to general intelligence and the traditional, symbolic Soar architecture. This is followed by major additions to Soar: non-symbolic representations, new learning mechanisms, and long-term memories.
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paper_title: Cognitive architectures: Research issues and challenges

paper_content:

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area.
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paper_title: A cognitive robotic system based on the soar cognitive architecture for mobile robot navigation, search, and mapping missions

paper_content:

Most unmanned vehicles used for civilian and military applications are remotely operated or are designed for specific applications. As these vehicles are used to perform more difficult missions or a larger number of missions in remote environments, there will be a great need for these vehicles to behave intelligently and autonomously. Cognitive architectures, computer programs that define mechanisms that are important for modeling and generating domain-independent intelligent behavior, have the potential for generating intelligent and autonomous behavior in unmanned vehicles. The research described in this presentation explored the use of the Soar cognitive architecture for cognitive robotics. ::: The Cognitive Robotic System (CRS) has been developed to integrate software systems for motor control and sensor processing with Soar for unmanned vehicle control. The CRS has been tested using two mobile robot missions: outdoor navigation and search in an indoor environment. The use of the CRS for the outdoor navigation mission demonstrated that a Soar agent could autonomously navigate to a specified location while avoiding obstacles, including cul-de-sacs, with only a minimal amount of knowledge about the environment. While most systems use information from maps or long-range perceptual capabilities to avoid cul-de-sacs, a Soar agent in the CRS was able to recognize when a simple approach to avoiding obstacles was unsuccessful and switch to a different strategy for avoiding complex obstacles. ::: During the indoor search mission, the CRS autonomously and intelligently searches a building for an object of interest and common intersection types. While searching the building, the Soar agent builds a topological map of the environment using information about the intersections the CRS detects. The agent uses this topological model (along with Soar's reasoning, planning, and learning mechanisms) to make intelligent decisions about how to effectively search the building. Once the object of interest has been detected, the Soar agent uses the topological map to make decisions about how to efficiently return to the location where the mission began. Additionally, the CRS can send an email containing step-by-step directions using the intersections in the environment as landmarks that describe a direct path from the mission's start location to the object of interest. ::: The CRS has displayed several characteristics of intelligent behavior, including reasoning, planning, learning, and communication of learned knowledge, while autonomously performing two missions. The CRS has also demonstrated how Soar can be integrated with common robotic motor and perceptual systems that complement the strengths of Soar for unmanned vehicles and is one of the few systems that use perceptual systems such as occupancy grid, computer vision, and fuzzy logic algorithms with cognitive architectures for robotics. The use of these perceptual systems to generate symbolic information about the environment during the indoor search mission allowed the CRS to use Soar's planning and learning mechanisms, which have rarely been used by agents to control mobile robots in real environments. Additionally, the system developed for the indoor search mission represents the first known use of a topological map with a cognitive architecture on a mobile robot. The ability to learn both a topological map and production rules allowed the Soar agent used during the indoor search mission to make intelligent decisions and behave more efficiently as it learned about its environment. While the CRS has been applied to two different missions, it has been developed with the intention that it be extended in the future so it can be used as a general system for mobile robot control. The CRS can be expanded through the addition of new sensors and sensor processing algorithms, development of Soar agents with more production rules, and the use of new architectural mechanisms in Soar.
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paper_title: Deep Learning in Neural Networks: An Overview

paper_content:

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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paper_title: Long Short-Term Memory

paper_content:

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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paper_title: The Symbol Grounding Problem

paper_content:

A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.
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paper_title: Artificial Intelligence: Connectionist and Symbolic Approaches

paper_content:

This article describes the two competing paradigms of artificial intelligence: connectionist and symbolic approaches. Each paradigm has its strengths and weaknesses. No single existing paradigm currently can fully address all major issues concerning intelligence and cognition. This condition indicates the need to integrate these existing paradigms.
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paper_title: Connectionist AI, symbolic AI, and the brain

paper_content:

Connectionist AI systems are large networks of extremely simple numerical processors, massively interconnected and running in parallel. There has been great progress in the connectionist approach, and while it is still unclear whether the approach will succeed, it is also unclear exactly what the implications for cognitive science would be if it did succeed. In this paper I present a view of the connectionist approach that implies that the level of analysis at which uniform formal principles of cognition can be found is the subsymbolic level, intermediate between the neural and symbolic levels. Notions such as logical inference, sequential firing of production rules, spreading activation between conceptual units, mental categories, and frames or schemata turn out to provide approximate descriptions of the coarse-grained behaviour of connectionist systems. The implication is that symbol-level structures provide only approximate accounts of cognition, useful for description but not necessarily for constructing detailed formal models.
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paper_title: A Hybrid Symbolic and Sub-Symbolic Intelligent System for Mobile Robots

paper_content:

This paper describes our latest efforts to extend the Symbolic and Sub-symbolic Robotics Intelligence Control System (SS-RICS). Our initial development included five design guidelines for a generalized intelligence system and those guidelines are discussed in relationship to the current improvements and extensions. We also discuss recent exercises at the National Institute of Standards and Technology (NIST) robotics test facility as well as the addition of Decision Field Theory (DFT) into the architecture. We conclude that the hybrid nature of SS-RICS allows for some improvements over traditional architectures by allowing for both sub-symbolic and symbolic problems to be addressed. However, the challenges of generalization across problem spaces still remains a topic for further research.
---
paper_title: Tensor product variable binding and the representation of symbolic structures in connectionist systems

paper_content:

Abstract A general method, the tensor product representation, is defined for the connectionist representation of value/variable bindings. The technique is a formalization of the idea that a set of value/variable pairs can be represented by accumulating activity in a collection of units each of which computes the product of a feature of a variable and a feature of its value. The method allows the fully distributed representation of bindings and symbolic structures. Fully and partially localized special cases of the tensor product representation reduce to existing cases of connectionist representations of structured data. The representation rests on a principled analysis of structure; it saturates gracefully as larger structures are represented; it permits recursive construction of complex representations from simpler ones; it respects the independence of the capacities to generate and maintain multiple bindings in parallel; it extends naturally to continuous structures and continuous representational patterns; it permits values to also serve as variables; and it enables analysis of the interference of symbolic structures stored in associative memories. It has also served as the basis for working connectionist models of high-level cognitive tasks.
---
paper_title: The Symbol Grounding Problem

paper_content:

A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.
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paper_title: Cognitive Control

paper_content:

This paper is inspired by how cognitive control manifests itself in the human brain and does so in a remarkable way. It addresses the many facets involved in the control of directed information flow in a dynamic system, culminating in the notion of information gap, defined as the difference between relevant information (useful part of what is extracted from the incoming measurements) and sufficient information representing the information needed for achieving minimal risk. The notion of information gap leads naturally to how cognitive control can itself be defined. Then, another important idea is described, namely the two-state model, in which one is the system's state and the other is the entropic state that provides an essential metric for quantifying the information gap. The entropic state is computed in the perceptual part (i.e., perceptor) of the dynamic system and sent to the controller directly as feedback information. This feedback information provides the cognitive controller the information needed about the environment and the system to bring reinforcement leaning into play; reinforcement learning (RL), incorporating planning as an integral part, is at the very heart of cognitive control. The stage is now set for a computational experiment, involving cognitive radar wherein the cognitive controller is enabled to control the receiver via the environment. The experiment demonstrates how RL provides the mechanism for improved utilization of computational resources, and yet is able to deliver good performance through the use of planning. The paper finishes with concluding remarks.
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paper_title: A cognitive robotic system based on the soar cognitive architecture for mobile robot navigation, search, and mapping missions

paper_content:

Most unmanned vehicles used for civilian and military applications are remotely operated or are designed for specific applications. As these vehicles are used to perform more difficult missions or a larger number of missions in remote environments, there will be a great need for these vehicles to behave intelligently and autonomously. Cognitive architectures, computer programs that define mechanisms that are important for modeling and generating domain-independent intelligent behavior, have the potential for generating intelligent and autonomous behavior in unmanned vehicles. The research described in this presentation explored the use of the Soar cognitive architecture for cognitive robotics. ::: The Cognitive Robotic System (CRS) has been developed to integrate software systems for motor control and sensor processing with Soar for unmanned vehicle control. The CRS has been tested using two mobile robot missions: outdoor navigation and search in an indoor environment. The use of the CRS for the outdoor navigation mission demonstrated that a Soar agent could autonomously navigate to a specified location while avoiding obstacles, including cul-de-sacs, with only a minimal amount of knowledge about the environment. While most systems use information from maps or long-range perceptual capabilities to avoid cul-de-sacs, a Soar agent in the CRS was able to recognize when a simple approach to avoiding obstacles was unsuccessful and switch to a different strategy for avoiding complex obstacles. ::: During the indoor search mission, the CRS autonomously and intelligently searches a building for an object of interest and common intersection types. While searching the building, the Soar agent builds a topological map of the environment using information about the intersections the CRS detects. The agent uses this topological model (along with Soar's reasoning, planning, and learning mechanisms) to make intelligent decisions about how to effectively search the building. Once the object of interest has been detected, the Soar agent uses the topological map to make decisions about how to efficiently return to the location where the mission began. Additionally, the CRS can send an email containing step-by-step directions using the intersections in the environment as landmarks that describe a direct path from the mission's start location to the object of interest. ::: The CRS has displayed several characteristics of intelligent behavior, including reasoning, planning, learning, and communication of learned knowledge, while autonomously performing two missions. The CRS has also demonstrated how Soar can be integrated with common robotic motor and perceptual systems that complement the strengths of Soar for unmanned vehicles and is one of the few systems that use perceptual systems such as occupancy grid, computer vision, and fuzzy logic algorithms with cognitive architectures for robotics. The use of these perceptual systems to generate symbolic information about the environment during the indoor search mission allowed the CRS to use Soar's planning and learning mechanisms, which have rarely been used by agents to control mobile robots in real environments. Additionally, the system developed for the indoor search mission represents the first known use of a topological map with a cognitive architecture on a mobile robot. The ability to learn both a topological map and production rules allowed the Soar agent used during the indoor search mission to make intelligent decisions and behave more efficiently as it learned about its environment. While the CRS has been applied to two different missions, it has been developed with the intention that it be extended in the future so it can be used as a general system for mobile robot control. The CRS can be expanded through the addition of new sensors and sensor processing algorithms, development of Soar agents with more production rules, and the use of new architectural mechanisms in Soar.
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paper_title: A million spiking-neuron integrated circuit with a scalable communication network and interface

paper_content:

): – requirements throughout surface waters of the N-limited North Pacific. Recent isotopic analysis of skeleton material from deep-sea corals near Hawaii also exhibit a decreasing trend over this time period, which has been interpreted as a signal of increasing N inputs from N2 fixation (36). However, because isotopic and stoichiometric signals of denitrification are transported from the anoxic zone into the subtropical gyre (37), the reported coral trends may originate partly from the OMZ. Any remaining signal attributable to N2 fixation would imply that the ecological niche of diazotrophs in the central gyre is uncoupled from the major N loss in the OMZ (38), and that a substantial imbalance of the Pacific N budget has persisted over the 20th century.
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paper_title: The Human Brain Project and neuromorphic computing.

paper_content:

Understanding how the brain manages billions of processing units connected via kilometers of fibers and trillions of synapses, while consuming a few tens of Watts could provide the key to a completely new category of hardware (neuromorphic computing systems). In order to achieve this, a paradigm shift for computing as a whole is needed, which will see it moving away from current “bit precise” computing models and towards new techniques that exploit the ::: stochastic behavior of simple, reliable, very fast, ::: low-power computing devices embedded in intensely recursive architectures. In this paper we summarize how these objectives will be pursued in the Human Brain Project.
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paper_title: A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm

paper_content:

The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of the human brain—within the constraints of existing silicon and post-silicon technologies. To this end, we fabricated a key building block of a modular neuromorphic architecture, a neurosynaptic core, with 256 digital integrate-and-fire neurons and a 1024×256 bit SRAM crossbar memory for synapses using IBM's 45nm SOI process. Our fully digital implementation is able to leverage favorable CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. In contrast to a conventional von Neumann architecture, our core tightly integrates computation (neurons) alongside memory (synapses), which allows us to implement efficient fan-out (communication) in a naturally parallel and event-driven manner, leading to ultra-low active power consumption of 45pJ/spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and is thus amenable to a wide range of applications. As an example, we trained a restricted Boltzmann machine offline to perform a visual digit recognition task, and mapped the learned weights to our chip.
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paper_title: Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

paper_content:

In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize the function of biological neural systems by emulating their structure. Designers of such systems face three major design choices: 1) whether to emulate the four neural elementsVaxonal arbor, synapse, dendritic tree, and somaVwith dedicated or shared electronic circuits; 2) whether to implement these electronic circuits in an analog or digital manner; and 3) whether to interconnect arrays of these silicon neurons with a mesh or a tree network. The choices we made were: 1) we emulated all neural elements except the soma with shared electronic circuits; this choice maximized the number of synaptic connections; 2) we realizedallelectroniccircuitsexcept those foraxonal arbors in an analog manner; this choice maximized energy efficiency; and
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paper_title: Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors

paper_content:

A transistor-free metal-oxide memristor crossbar with low device variability is realised and trained to perform a simple classification task, opening the way to integrated neuromorphic networks of a complexity comparable to that of the human brain, with high operational speed and manageable power dissipation.
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paper_title: Overview of the SpiNNaker System Architecture

paper_content:

SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal is to be able to simulate the behavior of aggregates of up to a billion neurons in real time. It consists of an array of ARM9 cores, communicating via packets carried by a custom interconnect fabric. The packets are small (40 or 72 bits), and their transmission is brokered entirely by hardware, giving the overall engine an extremely high bisection bandwidth of over 5 billion packets/s. Three of the principal axioms of parallel machine design (memory coherence, synchronicity, and determinism) have been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations. A further attribute of the system is the acknowledgment, from the initial design stages, that the sheer size of the implementation will make component failures an inevitable aspect of day-to-day operation, and fault detection and recovery mechanisms have been built into the system at many levels of abstraction. This paper describes the architecture of the machine and outlines the underlying design philosophy; software and applications are to be described in detail elsewhere, and only introduced in passing here as necessary to illuminate the description.
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paper_title: Memristor-based neural networks

paper_content:

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)
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paper_title: The SpiNNaker Project

paper_content:

The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or “spike.” In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world.
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Title: A survey of brain inspired technologies for engineering

Section 1: INTRODUCTION
Description 1: Introduce the historical and scientific intrigue surrounding the brain and its relation to the fields of AI, symbolic, and sub-symbolic approaches.

Section 2: SYMBOLIC APPROACHES
Description 2: Discuss the symbolic AI approaches, including cognitive architectures and their historical context, advantages, and limitations.

Section 3: SUB-SYMBOLIC APPROACHES
Description 3: Explore connectionist AI models like artificial neural networks (ANNs), their adaptive nature, benefits, and challenges.

Section 4: HYBRID APPROACHES
Description 4: Examine the hybrid approaches combining symbolic and connectionist models to leverage strengths and mitigate weaknesses in cognitive tasks.

Section 5: NEUROMORPHIC EMULATION
Description 5: Review specialized hardware designed to emulate neural networks, detailing various approaches and their respective advantages.

Section 6: CONCLUSION
Description 6: Summarize the technologies discussed and their potential impact on enabling the creation of cognitive machines.