Nanowire Brain Networks For Brain Computer Interface - Hydrogels To Create Tissue Engineered Electronic Nerve Interface For Artificial Memory & Nanotechnological Neuromodulation

ANA MARIA MIHALCEA, MD, PHD
MAY 05, 2024
Nanowire brain networks have been described recently in the literature with huge accomplishments and progress - the question is, would the dual use of such technology be abused for militarized mind control purposes? I have discussed even a couple years ago the parallel processing platform that can be created within the brain and then remote controlled via frequency signsals. Lets review how the nanowire brains have been shown to learn and remember - just like a human brain would. We can also see that the nanowire networks have been developed for brain computer interfaces. Tissue nerve electric interfaces have been created.

Nanowire 'brain' network learns and remembers 'on the fly'

For the first time, a physical neural network has successfully been shown to learn and remember "on the fly," in a way inspired by and similar to how the brain's neurons work. The result opens a pathway for developing efficient and low-energy machine intelligence for more complex, real-world learning and memory tasks.

Published today in Nature Communications, the research is a collaboration between scientists at the University of Sydney and University of California at Los Angeles.

Lead author Ruomin Zhu, a Ph.D. student from the University of Sydney Nano Institute and School of Physics, said, "The findings demonstrate how brain-inspired learning and memory functions using nanowire networks can be harnessed to process dynamic, streaming data."

Nanowire networks are made up of tiny wires that are just billionths of a meter in diameter. The wires arrange themselves into patterns reminiscent of the children's game "Pick Up Sticks," mimicking neural networks, like those in our brains. These networks can be used to perform specific information processing tasks. Memory and learning tasks are achieved using simple algorithms that respond to changes in electronic resistance at junctions where the nanowires overlap. Known as "resistive memory switching," this function is created when electrical inputs encounter changes in conductivity, similar to what happens with synapses in our brain.

Nanowire networks which of course are connected to artificial intelligence computing have been shown to create short and long term memory.

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks.

In conclusion, we have demonstrated how neuromorphic nanowire network devices can be used to perform tasks in an online manner, learning from the rich spatiotemporal dynamics generated by the physical neural-like network. This is fundamentally different from data-driven statistical machine learning using artificial neural network algorithms. Additionally, our results demonstrate how online learning and recall of streamed sequence patterns are linked to the associated memory patterns embedded in the spatiotemporal dynamics.

The self assembly and self organizing nanowire connectomes are used for brain inspired computing. This is a major step in teaching artificial intelligence - designing neural networks that can self learn and have quantum computing capacity is key to evolving AI towards the technocratic Singularity - the ability of AI to be smarter than all human brainpower on earth combined. It also is used for human augmentation, since the technocrats want to modifiy their brains so they can evolve their knowledge base via fusion with AI - and the unlimited download of information as elite Cyborgs and eventual immortal Robots.

Tomography of memory engrams in self-organizing nanowire connectomes

Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite having been shown that the emergent behavior relies on weight plasticity at single junction/synapse level and on wiring plasticity involving topological changes, a shift to multiterminal paradigms is needed to unveil dynamics at the network level. Here, we report on tomographical evidence of memory engrams (or memory traces) in nanowire connectomes, i.e., physicochemical changes in biological neural substrates supposed to endow the representation of experience stored in the brain. An experimental/modeling approach shows that spatially correlated short-term plasticity effects can turn into long-lasting engram memory patterns inherently related to network topology inhomogeneities. The ability to exploit both encoding and consolidation of information on the same physical substrate would open radically new perspectives for in materia computing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge.

Here we see that the real goal is the brain human interface:

Nanowire probes could drive high-resolution brain-machine interfaces

A central challenge in the field of electrophysiology is to achieve intracellular recording of the complex networks of electrogenic cells in tissues. The historical gold-standard of intracellular recording - patch-clamp electrodes - do have limitations in terms of their invasiveness and difficulty to use in large-scale parallel recording. Recent advances in nanowire-based bioelectronics have demonstrated minimally-invasive intracellular interfaces and highly-scalable parallel recording at the network level. Combined with in vivo recording platforms, these advances can enable investigations of dynamics in the brain and drive the development of new brain-machine interfaces with unprecedented resolution and precision.

https://anamihalceamdphd.substack.com/p/nanowire-brain-networks-for-brain?
----------------------------------------------------
Much more at the link and a great deal covered about the towers
and some new pulsing that is taking place, last video speaks of this.

Many Blessings,
CrystalRiver