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Spiking neural network

What are spiking neural networks?

Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic states, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. 

When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increases or decreases their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.

What are some advantages of Spiking Neural Networks?

A motivation for studying SNNs is that brains exhibit a remarkable cognitive performance in real-world tasks. With ongoing efforts toward improving our understanding of brain-like computation, there are expectations that models staying closer to biology will also come closer to achieving natural intelligence than more abstract models, or at least will have greater computational efficiency.

SNNs are ideally suited for processing spatio-temporal event-based information from neuromorphic sensors, which are themselves power efficient. The sensors record temporally precise information from the environment and SNNs can utilize efficient temporal codes in their computations as well. This processing of information is also event-driven meaning that whenever there is little or no information recorded the SNN does not compute much, but when sudden bursts of activity are recorded, the SNN will create more spikes. Under the assumption that typically information from the outside world is sparse, this results in a highly power-efficient way of computing. In addition, using time-domain input is additional valuable information compared to frame-driven approaches, where an artificial time step imposed by the sensor is introduced. 

 

What are some applications of spiking neural networks?

SNNs can in principle apply to the same applications as traditional ANNs. In addition, SNNs can model the central nervous system of biological organisms, such as an insect seeking food without prior knowledge of the environment. Due to their relative realism, they can be used to study the operation of biological neural circuits. Starting with a hypothesis about the topology of a biological neuronal circuit and its function, recordings of this circuit can be compared to the output of the corresponding SNN, evaluating the plausibility of the hypothesis. However, there is a lack of effective training mechanisms for SNNs, which can be inhibitory for some applications, including computer vision tasks.

As of 2019, SNNs lag ANNs in terms of accuracy, but the gap is decreasing and has vanished on some tasks

 

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Spiking neural network

October 14, 2020

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What are spiking neural networks?

Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic states, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. 

When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increases or decreases their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.

What are some advantages of Spiking Neural Networks?

A motivation for studying SNNs is that brains exhibit a remarkable cognitive performance in real-world tasks. With ongoing efforts toward improving our understanding of brain-like computation, there are expectations that models staying closer to biology will also come closer to achieving natural intelligence than more abstract models, or at least will have greater computational efficiency.

SNNs are ideally suited for processing spatio-temporal event-based information from neuromorphic sensors, which are themselves power efficient. The sensors record temporally precise information from the environment and SNNs can utilize efficient temporal codes in their computations as well. This processing of information is also event-driven meaning that whenever there is little or no information recorded the SNN does not compute much, but when sudden bursts of activity are recorded, the SNN will create more spikes. Under the assumption that typically information from the outside world is sparse, this results in a highly power-efficient way of computing. In addition, using time-domain input is additional valuable information compared to frame-driven approaches, where an artificial time step imposed by the sensor is introduced. 

 

What are some applications of spiking neural networks?

SNNs can in principle apply to the same applications as traditional ANNs. In addition, SNNs can model the central nervous system of biological organisms, such as an insect seeking food without prior knowledge of the environment. Due to their relative realism, they can be used to study the operation of biological neural circuits. Starting with a hypothesis about the topology of a biological neuronal circuit and its function, recordings of this circuit can be compared to the output of the corresponding SNN, evaluating the plausibility of the hypothesis. However, there is a lack of effective training mechanisms for SNNs, which can be inhibitory for some applications, including computer vision tasks.

As of 2019, SNNs lag ANNs in terms of accuracy, but the gap is decreasing and has vanished on some tasks

 

 Thanks for reading! We hope you found this helpful.

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