Creating Substrate-Independent Minds is a nonprofit organisation with a goal of advancing and creating Substrate-Independent Minds (SIM). Through, we reach out to the public (e.g. meetings, Facebook group), to projects and experts, in order to introduce SIM, to explain why we should accomplish SIM, to maintain development roadmaps, as well as to facilitate research and development networks, secure funding and the establishment of new projects to address the complete mosaic of requirements.

Besides Whole Brain Emulation (WBE), they also look at Brain Computer Interfaces (BCI) and Loosely-Coupled Off-Loading (LCOL). LCOL would be re-creations dependent on sources such as self-report, life-logs, video recordings, artificial intelligence that attempts to learn about an individual, etc.

Information presented here is from the FAQ and website. There is article by Randal A. Koene, on Substrate Independent minds

What is Advancing Substrate-Independent Minds (ASIM)?

In the past the transferal of minds into computer-based systems has been rather vaguely referred to as ‘uploading’. However, those hoping to advance this multidisciplinary field of research prefer to use the term Advancing Substrate Independent Minds (ASIM), to emphasize a more scientific, and less science fiction approach to creating emulations of human brains in substrates other than the original biological substrate. The term ASIM captures the fact that there are several ways in which hardware and software may be used to run algorithms which mimic the human brain, and that there are many different approaches that can be used to realize this objective.

Once you implement the functions originally carried out in one substrate in the computational hardware of another substrate you have achieved substrate-independence for those functions.

ASIM depends on developments in many disciplines. From a technical perspective, some of the foremost are neuroinformatics, neuroprosthetics, artificial general intelligence, high-throughput microscopy and brain-computer interfaces. Conceptually, there are also strong associations with applied bioinformatics and life-extension research.

The notion that the human mind is central to the experience of our existence and the realization that the brain can be understood as a biological machine have both been raised many times throughout the history of science. Following the development of computers and serious attempts to create mind-like function in artificial intelligence, there are now multiple high-profile projects directly aimed at reimplementing brain structure and functions of neurophysiology. To name the most obvious current candidates: the Blue-Brain Project, and the DARPA Synapse Project. Finally, converging developments in the areas of neural interfacing, optogenetic techniques and high-throughput microscopy, we arrive at the very real possibility to learn from and re-implement structure and function of specific brain samples.

ASIM is a subset of AGI (artificial general intelligence). It is the technical approach to mind uploading.

Concrete Steps

The term “mind uploading” has been used to describe a transition from the brain’s implementation of mind functions to SIM. Ideally, we would always re-compile functions of mind to make optimal use of a new target substrate. But at present, we do not understand enough about the hierarchy of interacting strategies employed at different cognitive levels of the mind to carry out such optimization. We do understand a great deal more about the principles of the fundamental biophysical components from which functions of mind emerge.

In neuroscience, we have experience identifying mechanistic aspects of neurophysiology, measuring functional responses and determining modulating contributors. While we may not have a complete descriptive catalog of all types of neurons, synaptic channels, and so forth, we do know how to obtain that information in a specific case when we need it. By analogy, it is as if we know how to read out the assembly language instructions of a program from its executable file, even though we do not have an adequate high level description to write an alternative implementation of the same program.

This is why the vast majority of actual research and development towards SIM is focused on the most conservative route, which we call whole brain emulation (WBE). In whole brain emulation, we aim to replicate the functions of neurophysiology and the structure of neuroanatomy that determines the interactions of basic components. The same general method, brain emulation at increasing resolution and scale, is adopted by pioneers on the advanced frontiers of computational neuroscience and neuroinformatics, frequently with previously unimaginable results.

We emphasize once more that the objective of Substrate-Independent Minds may be achieved through a number of different ways. is a-priori technology agnostic, and we have identified several conceptually distinct approaches, although the following focuses on the WBE approach.

Solution Projects

The four requirements for whole brain emulation are very concrete and there are solutions that are feasible by applying the capabilities of science and engineering today. Right now, several projects are in stages of preparation or execution. (For details, see and my upcoming article on “Experimental Research in Whole Brain Emulation” in the 2012 special issue of the International Journal of Machine Consciousness.)

The obvious way to acquire a structural connectome is to look at the spatial morphology of cells and fibers in the brain. Electron microscopy provides the resolution that is needed. Automated sectioning and imaging of a brain gives us the scope. Such volume microscopy is actively developed by several groups (e.g. the ATLUM project at Harvard University).

An entirely different solution to the acquisition of the structural connectome is tagged connection inference. There, biological bar codes (e.g., distinct artificial sequences of DNA or RNA) are used to mark pre- and post-synaptic sites throughout the brain. The tags form bidirectional pointers between neurons. After extracting tags at all sites, the sets of pointers provide the structural connectome in terms of synapses between neurons. This biological tool is being developed in the laboratories of Dr. Anthony Zador and Dr. Ed Callaway.

To satisfy the resolution requirements of in-vivo functional characterization of the elements of the connectome we look primarily to the development of new tools that can take these measurements from within. One strategy to manage scale and resolution is to establish a hierarchy of interfaces, reminiscent of the de-multiplexing of signals. Dr. Suzanne Gildert named this category the Demux-Tree approach. An example was introduced by Dr. Rudolpho Llinas, where the edges between nodes of the tree are formed by nanowires delivered through the vasculature of the brain. Flexible nanowires with a diameter of 500 nanometers have been developed at the New York University School of Medicine. Directing the wires into a Demux-Tree remains to be achieved, and a large number of nanowires still displaces significant brain volume.

Here too, there are projects aimed at developing biological tools. These have the advantage that they readily operate at cellular and sub-cellular resolutions, and can do so in vast numbers throughout the neural tissue. A collaboration of laboratories at MIT, Harvard and Northwestern University, with contributions by affiliates of Halcyon Molecular, is preparing the development of such a tool, a Molecular Ticker-Tape (Kording, K.P., PloS Computational Biology, 2011). Functional events, such as the activation of voltage-dependent receptors, will be recorded on biological media, such as DNA. The recordings may then be retrieved from the cells in which they reside.

Explicitly designing processes and systems in biology, while avoiding undesired interactions and downstream-effects is still difficult. Finding the biophysical components for signal detection, achieving the incorporation of those channels, and introducing reliable strategies for molecular recording are exploratory and time-consumptive efforts. Resolution and scale of these biological tools are extremely promising, although an in-vivo method of read-out is a desirable addition.

If we combine the benefits of both approaches, then we operate at sub-cellular scales, while recording in-vivo, retaining only the nodes and not the physical edges of the Demux-Tree. An optimal implementation of that approach was conceived several decades ago by Dr. Eric Drexler, Dr. Ralph Merkle, Dr. Robert Freitas and others, in the form of nanoscopic robots. Nanotechnology is in its early stages and we are not even very good at building macroscopic robots. What we are good at is developing and implementing integrated circuit technology. Shortly, we will describe a project to develop such a solution, a Micro-Neuro-Interface of sorts.

In the brain, the functions of mind are carried out by a highly parallel network of mostly silent, low-power processors – the neurons. Emulation of those functions will be more efficient on a similar computing substrate. That is why the development of neuromorphic computing platforms is of great interest. Examples are the hardware developed in the DARPA SyNAPSE project, the vastly extensible microchip architectures by Dr. Guy Paillet, and results of the European CAVIAR and FACETS projects.

Putting together Whole Brain Emulation Tools

Clearly, there are beneficial ways to combine technologies developed in different projects. For example, the application of protein-based or microbial rhodopsin-based voltage indicators, as developed by the Cohen lab can be a way for Micro-Neuro-Interfaces to optically register voltage changes. Or, high resolution recordings on Molecular Ticker Tape may be delivered in-vivo through agents.
To combine function and structure measurements, co-registration can be achieved in a number of ways. We may use local agent-to-agent topologies together with samples of morphological mapping carried out in-vivo by agents. We may also leave the Micro-Neuro-Interfaces in place, then carry out a volume microscopy in which the sectioned agents will show up at their locations within the tissue.

All of these concrete projects that can solve the requirements for whole brain emulation are based on the combination of present-day technologies. We can plan phases of development and estimate resources.

Of course, there is more to achieving SIM than the emulation of mind functions. A crucial matter is that the mind, as in its original biological implementation, must have a full and rich experience within its surroundings. This is called embodiment. In a sense, we extend beyond our brains, beyond our bodies and into the universe that communicates with us through sensation and interaction. Those input and output transactions must also be provided, but that is a topic that goes beyond the core steps to SIM that are presented here.

In past years, I [Randal Koene] have made it my responsibility to seek out and bring together the pioneers, the investigators, and to identify the technologies. With,

I [Randal Koene] put together, maintain and update road maps for WBE and SIM. An essential task has been to spot key pieces of the puzzle that require urgent attention. Now, we are directly involved with and provide objective oriented coordination and communication between projects, insuring that results will meet the requirements and will come together to achieve substrate-independent minds. That accomplishment will give our species the adaptability to handle and the ability to benefit directly from our technological advances, which we will need in order to thrive through impending new challenges.

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