4
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Accepted
Is there research that employs realistic models of neurons?
State of Rosehip Research
The Rosehip neuron is an important discovery, with vast implications to AI and its relationship to the dominant intelligence on earth for at least the last 50,000 years. ...
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Is there research that employs realistic models of neurons?
It looks like you really have two questions here. I'll try to answer the first one, and you should think about making a separate question for the second.
There is research into using simulated models ...
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How to model inhibitory synapses in the artificial neuron?
Principles of Computational Modelling in Neuroscience by David Sterratt, Bruce Graham, Andrew Gillies and David Willshaw discuss it in Chapter 7 (The synapse) and also in Chapter 8 (Simplified models ...
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Is there research that employs realistic models of neurons?
It is true that the current Machine learning is based on treating neurons as a component in the whole complexity , mesh of neurons. The focus is more on the architecture rather than understanding or ...
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2
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Is it possible to build an AGI with neural networks on neuromorphic chips?
No. The reasons include but are not limited to:
lack of understanding of how the brain works
current ANNs are mostly good at pattern recognition and generative tasks, but lack the capacity to ...
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2
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How to model inhibitory synapses in the artificial neuron?
In biology, when the presynaptic releases a neurotransmitter (a positive amount of them, obviously), this neurotransmitter reaches the postsynaptic vesicles causing an excitatory (depolarization) or ...
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2
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How to model inhibitory synapses in the artificial neuron?
The Degree to Which Inhibition is in Common Use
What could loosely be considered inhibitory effect occurs in MLPs (multilayer perceptrons) as they are normally designed and implemented already.
The ...
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1
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Accepted
Modifying the population neurons at runtime - Framework and resources
The challenge you've identified is a notable limitation in most existing neuromorphic computing frameworks, including Nengo. Generally, these frameworks are designed for static network configurations, ...
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Is there any comprehensive book that reviews topics in the area of brain-inspired computing?
The most popular theoretical framework in use currently, in the neuromorphic (brain-inspired) computing community is the Neural Engineering Framework (NEF). Neural Engineering by Chris Eliasmith and ...
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What are examples of machine learning techniques inspired by neuroscience?
There is a category of neural networks that more closely attempt to mimic biological neural networks by incorporating also time (i.e. not all neurons fire at the same time). They are called spiking ...
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Are there examples of neural networks (used for control) implemented on a FPGA or on a neurochip?
Assumption
In this answer it is assumed that with "neurochips" you mean chips made (using neuromorphic engineering) for neuromphic computing.
Related example
From what I currently understand from ...
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Could a large number of interconnected tiny turing-complete computer chips be patterned across a wafer to simulate a programmable neural network?
The building unit of a neural network is called perceptron. It cannot be represented by single transistor because it should hold arbitrary (float) value, over multiple computational iterations. (While ...
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