Robust Very Small Spiking Neural Networks Evolved with Noise to Recognize Temporal Patterns

Yaqoob, Muhammad; Wróbel, Borys

doi:10.1162/isal_a_00121

Cited by 4 publications

(12 citation statements)

References 20 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: 53%

“…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%

“…The task of the networks is to recognize three signals in a particular order (ABC) in a continuous random sequence (...BCACACCABCACBAC...), in which all signals appear with equal probability, and thus the correct patterns take up about 10% of time. To generate a variety of solutions, we use a genetic algorithm with a population of 300 individuals, with 100 independent runs for each of the two settings: in the first setting signals are followed by a constant interval of silence (16 ms), in the second setting the intervals vary, with a uniform distribution between 16 and 32 ms (in previous work, [38,40], we used noise on the membrane potential, but did not vary the interval of silences). In both settings the length of a each signal is 6 ms. We use the same genetic operators as in [40]; they can result in changes of weights, deletion and addition of edges (synapses) and the nodes (neurons) in the network (through deletion and duplication, respectively, of consecutive elements in the linear genomes; however, the maximum size of the network was limited as described above).…”

Section: Methodsmentioning

confidence: 99%

“…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]. In this work, we use the same model of noise (on the membrane voltage) as previously; while its level is biologically realistic, and so including it adds to the biological plausibility of our model, our primary concern is to aid in the evolution of networks that can maintain their states (a form of memory) even as the intervals between stimuli are hugely increased when testing the evolved network.…”

Section: Introductionmentioning

confidence: 99%

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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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