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Mind Brain Insights, LLC is a small consulting company with a big claim: that we can add significant value to most applied neuroscience projects. Fields enhanced include CNS medicine — biomarkers, therapeutic targets, and clinical treatment — natural language processing, AGI, and bio-inspired robotics. The current focus is on neuroprosthetics and BCI technology.

The company is based on a new cognitive neuroscience paradigm, called the MA (Memory Activation) Framework. It’s a new way to understand, and define, the human mind, and map it to the brain. Mind & behavior are first defined as a set of memory networks. These are listed, weighted, and connected. This set is then mapped to its (neural network) correlate in the brain: a set of functional neural network (FNN) ranges. The method demonstrably improves the accuracy of mind-to-brain mapping: from roughly 30% to 80%.

To understand the brain I argue requires a clear understanding of the mind. Both the mind, and the memory thereof, are constructed from a person’s ongoing, everyday, most powerful and influential experience of life. This features perception, thought, emotion, state of arousal, goals, the self, attention, intention, and other awareness. Mind is continually expressed in the brain — mostly in the form of general memories. One’s state of mind = (mostly) an active general memory set = a set of FNN range expression (or “sparse code”).

This method of defining the mind and mapping it to the brain has many practical applications. The company’s current focus is to find innovative new ways to add value to motor neuroprosthetics. Here the method can enhance device design, signal classification, testing, training, user choice, and everyday use.

The MA Framework and Motor Neuroprosthetics

To see how the MA cognitive neuroscience Framework adds value to real-world projects, let’s take a closer look at motor neuroprosthetics. To operate a device, the user will need to manipulate her own state of mind — her ongoing thoughts, intentions, emotional state, etc. Her mind controls her (artificial) movement. During a given movement, the mind features a set of (static and dynamic) movement goals — principally motor imagery and immediate movement intention. But other signals affect motor control as well; including perception, emotion, motivation, and attention.

What if you clearly understood the contents of the mind, during a given movement? What if the mind was — mostly — a memory set based on past experience? What if you could define (or summarize) this memory set with accuracy and precision? You could then use the set as the basis for creating an optimal mind (and brain) “target” to control a device.

Imagine how improved state of mind targets could empower the user! A target could be selected that is easy to activate consistently, strong or powerful, unique, and desirable. The user and her team could research optimal mental states, select the most useful & desirable ones, and modify these based on mood, environment (spatial, social…), task, and other variables.

Generally speaking, upgrading to a better mind/brain target enhances device design, performance, learning, and most other aspects of motor neuroprosthetics.

Once defined, this target can be used to better classify future brain signals.

The mind and movement intention

Let’s delve a little deeper into the mind. Consider an intention to perform a given movement. Intention (to a particular movement task) is a common mind/brain signal used as a device controller. But what exactly IS a movement intention? Can it be defined with precision? As it fluctuates across people, situations, time, and other variables? And, what about the dozens of other signals (noise, signal, or both) active at the same time?

Mind Brain Insights, LLC can optimize the definition of any movement intention, and corresponding brain signal. A signature can be defined that is less noisy and more representative of the target state. It can also be easier to repeat, more desirable, and overall more useful than existing (task-based) signatures. But to do this, the concept of movement intention needs to be broadened and sharpened. For instance, movement intention is accompanied by a state of emotion & arousal (ex: “calm and focused”). Somatosensation and visual perception are also part of movement intention. To characterize these states of mind as signal, or noise, you first need to define them.

In an ideal world, the user’s state of mind (ex: “I want to pick up my phone — now”) will control the device with skill, ease, and reliability. The key is to define the mind — its contents and function — across people, movement disorders, device types, and situations (lab, home, work, devise/object spatial relationships…) and other variables.

The good news is this is do-able! The main components of a movement intention can be listed, weighted, and connected. And, its strongest associations can also be accounted for (listed, weighted, and connected).

Mind and Brain are Poorly Understood

However, despite the great potential of motor neuroprosthetics, the technology performs poorly in the real world (Makeig, Kothe, Mullen, et al., 2012). The primary obstacle to high-functioning performance is the mind. The psychological mechanisms which underlie motor intention are poorly understood (O’Shea & Moran, 2017). Even more fundamentally, the mind as a whole — consciousness and unconscious awareness — remains a mystery. The brain science community (through no fault of their own) cannot answer the most basic questions. 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? 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 defining the mind and mapping it to the brain (Poldrack & Yarkoni, 2016).

Since the mind is not understand, the brain cannot be properly understood either. This is because the mind is represented (structurally) and expressed (functionally) within the confines of the brain. Somewhere within the structural neural networks & neural substrate, the mind’s potential to express itself is stored. And operating within these physical constraints is functional neural network activity — the most dominant process of the brain. Due to their constant widespread activity, FNNs are widely assumed to be strongly connected to ongoing state of mind.

Logically speaking, how could the neural networks that represent the intention to “pick up a glass” be defined without first listing the mind components involved: the perception and intention of “arm & hand & fingers, reach toward a glass, move hand to the right, fingers in grasp position, grasp” etc.?

The reasons for the lack of mind understanding are many. One is the blind spot of the brain science community. It almost totally ignores the topic of the mind. The answer to the question “how does the mind i.e. human consciousness manifest inside the brain?” is seen as a remote goal to be achieved in the distant future. Therefore neuroscience has put the issue on the back burner, and turned its full attention to 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? Also, the current efforts to understand the brain ARE making (very slow but steady) progress. The hope is the continuing collection of brain data will some day add up to a brain theory.

The MA Method

Though it sounds far-fetched, Mind Brain Insights claims the solution to the problem already exists — in the form of the MA Method for mapping the mind to the brain. The core idea is active mind = a set of active general memories = a set of active functional neural network ranges. This formula 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. The simple fact is a person’s state of mind includes all of the (most salient & attended-to) events in one’s awareness. These occur within his visual field, including within his body at its center. This is how most people throughout the world (who are not brain scientists) see the mind and its contents.

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 (episodic and general) memories are formed by copies of our “field of experience” is also common knowledge.

The idea the mind is filled with general memory, built from everyday experience, is the foundation for a more clear, precise and accurate understanding of neural networks. For example, whenever an apple is seen, a particular set of memories is triggered: “apple, apple taste, apple smell, food, grasp an apple, take a bite” etc. These 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 — again as a general memory set. The mind during daily activity is filled with memory (with an assist from the afferent signal and low-level perception).

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 highly associative. Think of a memory and many associated ones spring to mind. Trigger a FNN and many associated FNNs come to life. Also, 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.

Similar FNN activity (ex: “the taste of an apple”), 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.

A Practical Demonstration

For a moment let’s assume the MA cognitive neuroscience framework is correct. This would be great news! Not only would applied neuroscience be dramatically enhanced, but motor neuroprosthetics in particular. The paradigm would be a practical tool with many real-world applications.

However new paradigms in science are traditionally difficult to understand, let alone have confidence in. Therefore the main goal is not to explain the theory, but demonstrate its value. Toward that end, Mind Brain Insights, LLC offers 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 Insights will offer a 1-2 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 goal of this challenge is to demonstrate the method’s value in the real world — in particular its ability to enhance specific neuroprosthetic or other applied neuroscience projects. Our analysis can be compared with other methods of mapping mind-to-brain (or task-to-brain). I argue our insights are not only unique, but prove much more useful than conventional analysis.

References

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.