It’s Here: Researchers Grow Neurons, Brain Cells, On A Chip – Up Next the Artificial Brain

Researchers have successfully grown a brain cell, neuron, on a chip. While this is not the result that futurists are hoping for, it is a step in that direction. Futurists, the Government, and Tech Giants have theorized creating an artificial Brain through agendas such as the Human Brain Project, and Obama’s BRAIN Initiative.

Darpa in 2008 dove into a project that would essentially create an artificial brain modeled after the human brain, the official project is called; Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE.

From 2008;

“The program will develop a brain inspired electronic ‘chip’ that mimics that function, size, and power consumption of a biological cortex,” DARPA promises us. “If successful, the program will provide the foundations for functional machines to supplement humans in many of the most demanding situations faced by warfighters today” — like getting usable information out of video feeds, and starting tasks.

Fast forward, and Australian researchers have created a brain cell on a chip, literally. The researchers claim that their discovery is for medical research but realistically the discovery will aid scientists in understanding how human thoughts are formed and how connections are formed between neurons in the brain. Thus, leading to furthering the creation of an artificial brain.

According to the Darpa initiative, a computer modeled after the human brain would grant the computer the capacity to dig through video feeds and decipher much more than consumer computers can. Such processing capabilities given to conglomerate corporations would eradicate privacy and safety from each and every American because we would all be tracked and monitored like never before.

The researchers are working on investigating the mechanisms involved in the formation of neural circuits.

The neural scaffold falls far short of any brain-on-a-chip that futurists might imagine. But it does provide a way for scientists to guide the growth of neurons and study their connectivity, says Vini Gautam, a biomaterials engineer at Australian National University who led the study.

That has been a challenge for scientists trying to recreate neural circuitry in the lab. Neurons in the brain connect and communicate in a highly ordered way. But in the lab, the cells tend to reconstruct randomly and suffer from experimental limitations that render the circuitry nothing like the real thing in the brain.

“Understanding how neural circuits form in the brain is one of the fundamental questions in neuroscience,” Gautam says. Those connections form the basis for how we process information, and understanding them is key to developing treatments for mental disorders, she says.

The researchers wanted to create an environment where they can both direct the growth of neurons and allow them to make natural synchronized connections. So they made a nanowire scaffold made of indium phosphide. The semiconductor material is well known for applications in nanoscale electronics such as in the fabrication of LEDs, solar cells. But no one had used it to interface with brain cells, Gautam says.

Such a process could further be mimicked into how computers process information, for example – in the field of computing, the creation of neural networks have already happened.

— From Wikipedia;

Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.

Typically, neurons are connected in layers, and signals travel from the first (input), to the last (output) layer. Modern neural network projects typically have a few thousand to a few million neural units and millions of connections; their computing power is similar to a worm brain, several orders of magnitude simpler than a human brain. The signals and state of artificial neurons are real numbers, typically between 0 and 1. There may be a threshold function or limiting function on each connection and on the unit itself, such that the signal must surpass the limit before propagating. Back propagation is the use of forward stimulation to modify connection weights, and is sometimes done to train the network using known correct outputs. However, the success is unpredictable: after training, some systems are good at solving problems while others are not. Training typically requires several thousand cycles of interaction.

The goal of the neural network is to solve problems in the same way that a human would, although several neural network categories are more abstract. New brain research often stimulates new patterns in neural networks. One new approach is use of connections which span further to connect processing layers rather than adjacent neurons. Other research being explored with the different types of signal over time that axons propagate, such as deep learning, interpolates greater complexity than a set of boolean variables being simply on or off. Newer types of network are more free flowing in terms of stimulation and inhibition, with connections interacting in more chaotic and complex ways. Dynamic neural networks are the most advanced, in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.

The goals of the Human Brain Project and the Brain Initiative have long since been aligned with creating artificial technology that could mimic that of a human brain, and the latest Australian development in the field further paves the way for a true “brain on a chip” technology. Machines with the power to process information similar to that of Humans running autonomously spells a very bad future for humanity. While the cabal enlighten computations, they dumb down the masses, thus creating the extreme dystopia theorized in the novel 1984.