Company Filing History:
Years Active: 2023-2025
Title: Alexander Almela Conklin: Innovator in Neural Network Training
Introduction
Alexander Almela Conklin is a prominent inventor based in San Jose, CA. He has made significant contributions to the field of neural networks, holding a total of 3 patents. His work focuses on innovative methods for training neural networks, which have applications in various technological advancements.
Latest Patents
One of Conklin's latest patents is titled "Local Training of Neural Networks." This patent describes a method for performing learning through a learning network that processes input signals corresponding to target output signals. The learning network consists of inputs, neurons, and weights that interconnect the neurons, ultimately producing output signals. The method includes a free inference process that results in output signals based on the energy of the learning network, which incorporates interaction terms for neuron pair interactions. Additionally, a biased inference is performed by providing input signals and bias signals to the outputs, which are based on the target output signals. The weights of the network are updated based on the determined equilibrium states during the biased inference.
Career Highlights
Conklin is currently employed at Rain Neuromorphics Inc., where he continues to develop and refine his innovative approaches to neural network training. His expertise in this area has positioned him as a valuable asset to the company and the broader field of artificial intelligence.
Collaborations
Throughout his career, Conklin has collaborated with notable colleagues, including Suhas Kumar and Jack David Kendall. These partnerships have fostered a collaborative environment that encourages the exchange of ideas and advancements in technology.
Conclusion
Alexander Almela Conklin is a key figure in the development of neural network training methods. His innovative patents and contributions to Rain Neuromorphics Inc. highlight his commitment to advancing technology in this critical field.