Very small spiking neural networks evolved to recognize a pattern in a continuous input stream

Yaqoob, Muhammad; Wróbel, Borys

doi:10.1109/ssci.2017.8285420

Cited by 7 publications

(11 citation statements)

References 22 publications

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“…We observe that a variety of network topologies resulting from an artificial evolutionary process can perform the same computational task [38][39][40]. This is also the case for biological networks [18,22].…”

Section: Introductionsupporting

confidence: 54%

“…Each network in our model is encoded in a linear genome, and consists of three inputs, three interneurons, and one output neuron [38][39][40]. Inputs are not allowed to connect to the output neuron directly and only interneurons can have self-loops.…”

Section: Methodsmentioning

confidence: 99%

“…A fully connected network with this structure can have up to 21 connections (up to nine connections from inputs to interneurons, six connections between the interneurons, three self-loops, and three connections from the interneurons to the output neuron). Each input is dedicated to one signal (stimulus type), denoted as A, B and C. The interneurons and output neuron are modelled using Euler integration with 1 ms steps of the differential equations for adaptive exponential integrate and fire neurons [20]; we use the same parameter values as in [38][39][40]; these values result in tonic spiking in response to constant input current. Since this study focuses on the effect of network connectivity, the neuronal parameters are kept constant (allowing them to evolve would hugely increase the search space of the artificial evolutionary process).…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we analyse very small spiking neural networks (SNNs) evolved to perform a simple temporal pattern recognition task. We have shown previously that without noise, two interneurons are sufficient for this task, but such networks are fragile to even the slightest variation of the timing of inputs [39]. In contrast, networks with three interneurons can be evolved to recognize patterns consisting of three stimuli in the presence of noise, and they are robust to a change of neuronal parameters or duration of intervals of silence between the stimuli [38,40].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns

Yaqoob

Steuber

Wróbel

2019

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation

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Biological and artificial spiking neural networks process information by changing their states in response to the temporal patterns of input and of the activity of the network itself. Here we analyse very small networks, evolved to recognize three signals in a specific pattern (ABC) in a continuous temporal stream of signals (...CABCACB...). This task can be accomplished by networks with just four neurons (three interneurons and one output). We show that evolving the networks in the presence of noise and variation of the intervals of silence between signals biases the solutions towards networks that can maintain their states (a form of memory), while the majority of networks evolved without variable intervals between signals cannot do so. We demonstrate that in most networks, the evolutionary process leads to the presence of superfluous connections that can be pruned without affecting the ability of the networks to perform the task and, if the unpruned network can maintain memory, so does the pruned network. We then analyse how these small networks can perform their tasks, using a paradigm of finite state transducers. This analysis shows that self-excitatory loops (autapses) in these networks are crucial for both the recognition of the pattern and for memory maintenance.

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Section: Introductionsupporting

confidence: 54%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns

Yaqoob

Steuber

Wróbel

2019

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation

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“…Recently, however, some researchers have attempted to extract state machines from evolved neural networks. For example, Yaqoob and Wróbel [27] automatically generated a state machine with the same properties of an evolved spiking neural network.…”

Section: Related Workmentioning

confidence: 99%

Evolving Controllers for a Transformable Wheel Mobile Robot

2018

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Unmanned ground vehicles (UGVs) are well suited to tasks that are either too dangerous or too monotonous for people. For example, UGVs can traverse arduous terrain in search of disaster victims. However, it is difficult to design these systems so that they perform well in a variety of different environments. In this study, we evolve controllers and physical characteristics of a UGV with transformable wheels to improve its mobility in a simulated environment. The UGV’s mission is to visit a sequence of coordinates while automatically handling obstacles of varying sizes by extending wheel struts radially outward from the center of each wheel. Evolved finite state machines (FSMs) and artificial neural networks (ANNs) are compared, and a set of controller design principles are gathered from analyzing these experiments. Results show similar performance between FSM and ANN controllers but differing strategies. Finally, we show that a UGV’s controller and physical characteristics can be effectively chosen by examining results from evolutionary optimization.

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Very Small Spiking Neural Networks Evolved for Temporal Pattern Recognition and Robust to Perturbed Neuronal Parameters

Yaqoob

Wróbel

2018

Artificial Neural Networks and Machine Learning – ICANN 2018

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Very small spiking neural networks evolved to recognize a pattern in a continuous input stream

Cited by 7 publications

References 22 publications

The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns

The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns

Evolving Controllers for a Transformable Wheel Mobile Robot

Very Small Spiking Neural Networks Evolved for Temporal Pattern Recognition and Robust to Perturbed Neuronal Parameters

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