Neuromorphic computing entails building architectures inspired by elements of the human brain, such as neural organization and synapses. These architectures have proved to be highly promising and advantageous for a number of applications, as they can have both memory and learning functions.
Most current neuromorphic architectures artificially recreate the plasticity (i.e., ability to be easily shaped over time) of synapses, which are junctions between nerve cells that enable the propagation of impulses across brain regions. Another potentially valuable source of inspiration for neuromorphic computing approaches, however, is the plasticity of the neuronal membrane, the protective barrier that preserves the functioning of neurons.
With this in mind, researchers at TU Dresden and Hemholtz Center have recently designed a neurotransistor that emulates the intrinsic plasticity of the neuronal membrane. The new neurotransistor, presented in a paper published in Nature Electronics, was fabricated by coating a silicon nanowire with an ion-doped sol-gel silicate film.
The new neurotransistor can act as a short-term memory, as the sol-gel film restricts the movement of ions inside it and maintains certain ionic states for a short period of time. Due to this unique quality, the sol-gel film allows the neurotransistor to generate unique nonlinear (i.e., sigmoid) output dynamics, which are governed by the history of the input signal.
The device emulates the functionality and intrinsic plasticity of a neuron cell. In fact, in neurons, a membrane potential induces a sigmoidal change in the ionic current. This nonlinear dynamic property also gives it advanced learning capabilities, making it ideal for machine-learning applications, for instance, learning how to perform well in pattern-classification tasks. The design allows the neurotransistor to use the quantified amount of doped-ions in its cell to attain stable output dynamics.
Read More: https://techxplore.com/news/2020-06-silicon-nanowire-transistors-memory-functions.html
Most current neuromorphic architectures artificially recreate the plasticity (i.e., ability to be easily shaped over time) of synapses, which are junctions between nerve cells that enable the propagation of impulses across brain regions. Another potentially valuable source of inspiration for neuromorphic computing approaches, however, is the plasticity of the neuronal membrane, the protective barrier that preserves the functioning of neurons.
With this in mind, researchers at TU Dresden and Hemholtz Center have recently designed a neurotransistor that emulates the intrinsic plasticity of the neuronal membrane. The new neurotransistor, presented in a paper published in Nature Electronics, was fabricated by coating a silicon nanowire with an ion-doped sol-gel silicate film.
The new neurotransistor can act as a short-term memory, as the sol-gel film restricts the movement of ions inside it and maintains certain ionic states for a short period of time. Due to this unique quality, the sol-gel film allows the neurotransistor to generate unique nonlinear (i.e., sigmoid) output dynamics, which are governed by the history of the input signal.
The device emulates the functionality and intrinsic plasticity of a neuron cell. In fact, in neurons, a membrane potential induces a sigmoidal change in the ionic current. This nonlinear dynamic property also gives it advanced learning capabilities, making it ideal for machine-learning applications, for instance, learning how to perform well in pattern-classification tasks. The design allows the neurotransistor to use the quantified amount of doped-ions in its cell to attain stable output dynamics.
Read More: https://techxplore.com/news/2020-06-silicon-nanowire-transistors-memory-functions.html
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