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A deeper understanding of the brain's computation will inform design of sensorimotor devices for neural control and allow for more targeted future work on investigating neural function. [7] Using this knowledge, inorganic systems can be better engineered to better interact with the brain's endogenous system of computation.
Neurotechnology encompasses any method or electronic device which interfaces with the nervous system to monitor or modulate neural activity. [1] [2]Common design goals for neurotechnologies include using neural activity readings to control external devices such as neuroprosthetics, altering neural activity via neuromodulation to repair or normalize function affected by neurological disorders ...
In situ [a] is a Latin phrase meaning 'in place' or 'on site', derived from in ('in') and situ (ablative of situs, lit. ' place ' ). [ 3 ] The term refers to the examination or occurrence of a process within its original context, without relocation.
Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, or enhance neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living constructs.
The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held every December. Along with ICLR and ICML , it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.
These maps incorporate individual neural connections in the brain and are often presented as wiring diagrams. [4] Brain mapping techniques are constantly evolving, and rely on the development and refinement of image acquisition, representation, analysis, visualization and interpretation techniques. [5]
Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...
The research achieved great success and aroused the interest of scholars in the study of neural networks. While the architecture of the best performing neural networks today are not the same as that of LeNet, the network was the starting point for a large number of neural network architectures, and also brought inspiration to the field.