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neuromorphic computing review

There is no dedicated space in your brain which stores all the memory.

Those are connected to each other in a more random like fashion and there is no direction (it is still a directed graph, but they aren't organized in layers). (I refer here to axo-dendritic synapses). Your memory is stored in the connections of the neurons. Then we still have to wait some years until we can use that kind of system to make something useful. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. We have a current \(I(t)\):and \(u\) is the voltage.

Take a classification task for example, the same classification of the neural network is required every time similar inputs are presented. Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.


We use E-Mail instead of waiting until we visit our friend next year to talk and then E-Mail was too slow so we all use some instant chat system.

Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information.

They all work on their own schedule. Oh yeah so to simulate a so called spiking neural network we need to know when each of them is firing check whether a potential in a neuron is above the firing threshold and then fire but the inputs into one neuron aren't happening all at the same time like in our artificial version.

This firing has a length and an amplitude but these are not used encode any information. If the next neuron fires shortly after the current one the weight is strengthen and if the next one fires if the current doesn't the strength shrinks. I'm currently preparing a seminar talk for this topic and find it quite interesting but also relatively complicated. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated.

That is binary and our computer model uses those complicated floating point numbers.

In general we call \(u_i(t)\) the potential of neuron \(i\) at time \(t\). Why does it exist?

Instead each synapse has a bit code (here 4 bits) which is used to activate the synapse for communication. In this hardware model one neuron can have up to 14,000 connections via synapses which is close to our brain. Maybe you heard about The von Neumann architecture consists of input and output devices and then a separation of processing unit and memory which doesn't exist in the brain in that form. Good question! Okay sorry... What I wanted to point out is that brains evolved for millions of years but okay we might find out how our brains work and jump straight to a human newborn brain.

In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption.

If nothing happens a neuron has a resting potential \(u_{\text{rest}}\) (the chill state). After each firing there is a exhaustion phase (that's just my naming here) which is determined by the so called absolute refractory period.

They work well but they aren't really biologically inspired. The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. There will be a some literature references in this post and if you enjoy such non coding posts and hope I'll enjoy writing it :D then I'll consider writing more of these kind.

Let's have a look at our city block understanding of the brain first before we dive into the hardware stuff.A neuron in the brain has three parts: dendrites, soma and axon whereas the dendrites are the input connections, the soma is the processing unit and the axon is the output.

Additionally artificial neural networks are working with floating point numbers i.e if the input is 0.3 and the weight is 0.2 we get 0.06 as a result input to the next neuron add it up with all the other inputs and then send it over to the next layer.

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