Deep learning incorporating biologically inspired neural dynamics and in-memory computing
نویسندگان
چکیده
منابع مشابه
Biologically inspired learning in a layered neural net
A feed–forward neural net with adaptable synaptic weights and fixed, zero or non–zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong. It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possib...
متن کاملBiologically Inspired Learning System
............................................................................................................................. v Chapter
متن کاملBiologically Inspired Computing in CMOL CrossNets
This extended abstract outlines my invited keynote presentation of the recent work on neuromorphic networks (“CrossNets”) based on hybrid CMOS/nanoelectronic (“CMOL”) circuits, in the space-saving Q/A format.
متن کاملBiologically Inspired Modular Neural Networks
(ABSTRACT) This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modu-larization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to exp...
متن کاملBiologically Inspired Temporal Sequence Learning
We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons. In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function. The dynamic prope...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nature Machine Intelligence
سال: 2020
ISSN: 2522-5839
DOI: 10.1038/s42256-020-0187-0