I’m an independent researcher who’s developed (over the last 12 years) a new way to define the human mind and its neural correlate. It’s called the MA (memory activation) Theory. I argue it raises the accuracy of mind-to-brain mapping from roughly 30 to 80%. It’s a new cognitive neuroscience paradigm. The mind is defined as a person’s ongoing experience of life: perception, thought, emotion, state of arousal, goals, attention, intention, etc. More precisely, the mind (other than low-level perception) = a set of (weighted, connected) general memories = a set of functional neural network (FNN) ranges. Active mind = an active general memory set = a set of FNN range expression (or “sparse code”).

The MA Method has many practical applications in applied neuroscience. My current focus is in motor neuroprosthetics. Here the method can enhance device design, (brain) signal classification, testing, training, and everyday use.

To see how the MA Method might do this, let’s start with a closer look at motor neuroprosthetics. To operate a device, the user manipulates her mind —  thoughts, movement intentions, emotional state, etc. Her mind ultimately controls (artificial) movement. During a given movement, the mind is comprised of a set of (mostly dynamic) movement goals — principally motor imagery and immediate movement intention. Other signals that affect motor control include perception, emotion, motivation, and attention.

Understanding the mind enables you to define it accurately. This in turn is the foundation for selecting the optimal mind/brain “target” to control a device. And once selected, it (and the rest of the mind) can be used to more accurately classify the brain signal.

For example, consider movement intention. This is a common mind/brain signal used as a controller. But what exactly IS a movement intention? Can it be defined? With precision? And, what about the dozens of other signals active at the same time?

I argue a much more optimal brain signal “signature” can be arrived at. A signal that is stronger, less noisy, more representative of the desired state, easier to repeat, and overall more useful than existing signatures. But to arrive at such a signature the concept of movement intention needs to be broadened and sharpened. For example, movement intention is always accompanied by a state of emotion and arousal (ex: calm and focused). Somatosensation and visual perception are a large part of any movement. Not only do the components of the intention need to be defined, its strongest associations need to be accounted for as well.

In an ideal world, the user’s state of mind (ex: a “I want to pick up my phone” state of mind) will control movement with skill, ease, and reliability. And, it will do so across people, movement disorders, device types, and situations (lab, home, work, devise/object spatial relationships…) and other variables.

However, despite the great potential of motor neuroprosthetics, the technology still performs poorly in the real world (Makeig, Kothe, Mullen, et al., 2012). The primary stumbling block to high-functioning motor neuroprosthetic performance, I argue, is the mind. For one, the psychological mechanisms underlying motor intention are poorly understood (O’Shea & Moran, 2017). More importantly, the (conscious and unconscious) mind remains a mystery to the brain science community. The most basic questions remain unanswered. What exactly IS the mind? What are its main components? How do these vary — across people, time, situations, life circumstances, and measurement conditions? How are the mind’s components connected functionally and structurally? What physical form does the mind take inside the brain?

In short, the exact nature of the mind’s relationship to the brain is poorly understood (Bassett & Gazzaniga, 2011). There are a number of very difficult problems standing in the way of accurately defining the mind, and mapping it to the brain (Poldrack & Yarkoni, 2016).

Because the mind is neither understood nor defined, the brain I argue cannot be properly defined either. This is because the mind is, somehow, expressed or represented within the confines of the brain. Somewhere within the structural neural networks & neural substrate, the mind’s potential to express itself is stored. And within these physical constraints, functional neural network activity  — the most dominant activity of the brain — is connected to the mind in action. How could the neural network(s) representing the intention “pick up a glass” possibly be defined without first listing the mind components involved: perception and intention of “arm & hand & fingers, reach toward a glass, move hand to the right, fingers in grasp position, grasp” etc.? Accurate brain mapping depends upon accurate mind mapping.

The reasons for the lack of mind understanding are many. One is the blind spot of the neuroscience community regarding the mind. The solution to human consciousness —  how the brain manifests the conscious and unconscious mind — is viewed as a remote goal to be achieved (possibly) in the distant future. Therefore neuroscience has put the issue on the back burner to focus on the brain. The decision to forego a serious investigation of the mind is entirely rational. Why pursue a problem where little if any progress is being made? Not only that, the current efforts to study and understand the brain ARE making (very, very slow but steady) progress. The hope is the continuing collection of brain data will some day add up to a brain theory.

Though it sounds far-fetched, I argue the solution to the problem already exists — in the form of the MA Method. The core idea is active mind = a set of active general memories = a set of active functional neural network ranges. This concept in turn rests on a more expansive view of the human mind. Cognitive science holds the brain computes or processes (external and internal) events, to create the mind. Mind Brain Insights broadens this narrow view of mind to include all of human experience. A person’s state of mind includes all of the (most salient) INFORMATION played out within his visual field, including his body at its center. This is how most people throughout the world (who are not brain scientists) view the contents of the mind. The brain continually makes copies of this field (ex: those apples), which form and shape general memories (ex: “an apple,” “fruit” etc.). The idea that (both episodic and general) memories are formed by copies of this “field of experience” is also common knowledge.

That the mind is filled with general memory is the foundation for a more clear and precise understanding of neural networks. For example, whenever an apple is seen, a particular set of memories is triggered: “apple, apple taste, apple smell, grasp an apple, take a bite” etc. These memories of experience can not only be listed, but connected, weighted, and labeled excitatory/inhibitory. At the same time, apple recognition, meaning, related thoughts, emotions, goals, attention, intention, etc. involving that apple can also be defined as a general memory set. The mind during everyday activity is filled with general memory (with an assist from the afferent signal and low-level perception).

As a general memory set is activated, in the brain, external and internal information is “processed.” I argue the main processing mechanism of the brain is (you guessed it) general memory activation. For example, a perception of hot coffee will trigger a set of memories such as “hot liquid, spill, burn, pain, attend to, steady my hand,” etc. General memory not only forms the meaning of experience, but directs subsequent action in relation to it.

The most obvious candidate for the neural correlate of an (active) general memory is a functional neural network. A FNN is a coordinated, local & often global, population-level neural firing event. Active state general memories and FNNs share the same basic characteristics. Both are extremely associative. Think of a memory and many other associated memories come to mind; trigger a FNN and many more come to life. Both memories and FNNs act, simultaneously, as a receiver of similar signals AND as a transmitter of its own signal. And each is arguably the dominant feature of their respective (mind/brain) systems.

A similar group of memory/FNN activity (ex: apple taste), repeated over time, will form a range of expression. As a FNN range forms, so does a corresponding structural neural network (SNN). The latter stores the former when dormant, and works to recall & support it when active.

Any new brain theory will take time to understand, let alone evaluate. But let’s assume the MA Method and supporting Theory are correct. This would be great news — not only for applied neuroscience generally but for motor neuroprosthetics. The practical applications are many. The lowest-hanging fruit regarding motor neuroprosthetics and BCI technology is brain signal classification. All aspects of mind — perception, recognition, meaning, thought, emotion, motivation, the self, attention, goals, intention, motor cortex signals etc. — are manifest in the brain. (If an aspect of mind wasn’t, it wouldn’t affect the motor cortex, and motor control signal.) When and only when the mind is classified accurately can corresponding brain signals be accurately classified. Accurate mind/brain defining and subsequent classification enhances the use of brain signals as neuroprosthetic targets — for devise design, user selection, and devise control in lab and everyday situations.

The method is I argue a practical tool with many real-world applications. Yet I realize a new brain theory is difficult to fully understand let alone have confidence in. Instead my main aim is not to explain the theory, but DEMONSTRATE its value. Toward that end, I offer brain science professionals and executives the “Mind Mapping Challenge.” First, the challenger selects any aspect of the mind or behavior he or she wishes; or any combination of aspects. Mind Brain Consulting will then offer a 2 page analysis of how to convert this to a set of memory networks, and map these to neural networks. If both parties are interested, Mind Brain Insights can then provide a more in-depth analysis and mapping. The analysis would be tailored to the specific (real or hypothetical) project: its purpose, patient population, movement disorder, clinical goals, technology involved, etc.

The main aim of this challenge is to demonstrate the method’s value in the real world — specifically its ability to add value to a specific neuroprosthetic or other applied neuroscience project. In addition, the analysis given by Mind Brain Insights can be compared with other mind/brain mapping approaches.


Bassett, D.S., & Gazzaniga, M.S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15, 200-209.

Makeig, S., Kothe, C., Mullen, T., Bigdely-Shamlo, N., Zhang, Z., Kreutz-Delgado, K. (2012). Evolving signal processing for brain-computer interfaces. Proceedings of the IEEE, 100, 1567.

O’Shea, H., & Moran, A. (2017). Does motor simulation theory explain the cognitive mechanisms underlying motor imagery? Frontiers in Human Neuroscience, 17, 1.

Poldrack, R.A., Yarkoni, T. (2016). From brain maps to cognitive ontologies: informatics and the search for mental structure. Annual Review of Psychology, 67, 587.