Intrinsic and extrinsic implementation of a bio-inspired hardware system

Glackin, Brendan; Maguire, Liam; McGinnity, T.M.

doi:10.1016/j.ins.2003.03.008

Cited by 7 publications

(2 citation statements)

References 36 publications

(33 reference statements)

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“…However this design challenge is now penetrating into non-critical systems where engineers must aim to realize reliable systems using unreliable computing fabrics (Barker et al, 2007 ; Beiu and Ibrahim, 2007 ; DeSyRe, 2012 ). Currently, bio-inspired techniques which utilize FPGAs (e.g., Glackin et al, 2004 ; Negoita and Hintea, 2009 ) provide adaptive repair but the levels of granularity are still insufficient. Furthermore, such systems also depend upon a central controller to make repair decisions thereby rendering the entire repair process ineffective if it develops a fault.…”

Section: Discussionmentioning

confidence: 99%

Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling

Wade¹,

McDaid²,

Harkin³

et al. 2012

Front. Comput. Neurosci.

128

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In this paper we demonstrate that retrograde signaling via astrocytes may underpin self-repair in the brain. Faults manifest themselves in silent or near silent neurons caused by low transmission probability (PR) synapses; the enhancement of the transmission PR of a healthy neighboring synapse by retrograde signaling can enhance the transmission PR of the “faulty” synapse (repair). Our model of self-repair is based on recent research showing that retrograde signaling via astrocytes can increase the PR of neurotransmitter release at damaged or low transmission PR synapses. The model demonstrates that astrocytes are capable of bidirectional communication with neurons which leads to modulation of synaptic activity, and that indirect signaling through retrograde messengers such as endocannabinoids leads to modulation of synaptic transmission PR. Although our model operates at the level of cells, it provides a new research direction on brain-like self-repair which can be extended to networks of astrocytes and neurons. It also provides a biologically inspired basis for developing highly adaptive, distributed computing systems that can, at fine levels of granularity, fault detect, diagnose and self-repair autonomously, without the traditional constraint of a central fault detect/repair unit.

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Section: Discussionmentioning

confidence: 99%

Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling

Wade¹,

McDaid²,

Harkin³

et al. 2012

Front. Comput. Neurosci.

128

View full text Add to dashboard Cite

show abstract

“…By contrast, intrinsic evolution refers to approaches that conduct fitness measurements by implementing each candidate solution on the device and observe its behavior. As this eliminates the need for complex simulation environments, the validity of this method is enhanced [6]. However, both extrinsic and intrinsic evolution show some limitations when it comes to the learning process of an agent's behavior.…”

Section: Introductionmentioning

confidence: 99%

Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution

Hallawa

Schug

Iacca

et al. 2020

Applications of Evolutionary Computation

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Recent developments in the miniaturization of hardware have facilitated the use of robots or mobile sensory agents in many applications such as exploration of GPS-denied, hardly accessible unknown environments. This includes underground resource exploration and water pollution monitoring. One problem in scaling-down robots is that it puts significant emphasis on power consumption due to the limited energy available online. Furthermore, the design of adequate controllers for such agents is challenging as representing the system mathematically is difficult due to complexity. In that regard, Evolutionary Algorithms (EA) is a suitable choice for developing the controllers. However, the solution space for evolving those controllers is relatively large because of the wide range of the possible tunable parameters available on the hardware, in addition to the numerous number of objectives which appear on different design levels. A recently-proposed method, dubbed as Instinct Evolution Scheme (IES), offered a way to limit the solution space in these cases. This scheme uses Behavior Trees (BTs) to represent the robot behaviour in a modular, re-usable and intelligible fashion. In this paper, we improve upon the original IES by using Grammatical evolution (GE) to implement a full BT evolution model integratable with IES. A special emphasis is put on minimizing the complexity of the BT generated by GE. To test the scheme, we consider an environment exploration task on a virtual environment. Results show 85% correct reactions to environment stimuli and a decrease in relative complexity to 4.7%. Finally, the evolved BT is represented in an if-else on-chip compatible format.

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A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware

Glackin

McGinnity

Maguire

et al. 2005

Lecture Notes in Computer Science

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Intrinsic and extrinsic implementation of a bio-inspired hardware system

Cited by 7 publications

References 36 publications

Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling

Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling

Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution

A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware

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