Individual Neuromorphic Emulation

Reconstructing the Individual Mind

We are developing a framework for reconstructing the mind from the ground up — a method we call Individual Neuromorphic Emulation (INE). This approach combines high-resolution brain monitoring, advanced anatomical modeling, and machine learning to create a personalized, dynamic model of an individual's neural and cognitive function. The result is more than data — it’s a computational system that reflects the individual’s identity at the level of information processing itself.

Computational Modeling of Individual Brains

With the anatomy and connectivity of an individual characterized, a key question is how and at what scale should function, as it is relevant and practical to emulate the individual, of the brain be modeled? Certainly, neuronal function can be described and modeled at many scales, from molecular mechanisms to large-scale hemodynamic fluctuations. In determining the scale, we are guided by modern neuroscience findings, and particularly relevant are findings on the organization of the cortex. The cortex is a layered “sheet” of interconnected neurons and can be parcellated into areas based on function and neuronal architecture. At a fundamental unit, the cortex can be thought of as being composed of multiple mini-columns (~40 µm), the so-called hyper-columns (~200-800 µm). Across the layers of a mini-column, the organization of neuronal connectivity is canonical and has been described as a microcircuit that is dynamically configurable based on inputs to the circuit (de Costa & Martin, 2010). The activity of the canonical microcircuit (CMC) can be detected with local field potentials (LFP) recordings proximal to the neurons, and the spatial decay of the LFP suggests that its resolution is approximately over several hyper-columns (3-5 mm). LFPs are seen at the scalp as the electroencephalogram (EEG). Because the EEG is recorded at the scalp, inferring the source of the EEG requires advanced modeling and analytic tools. However, if done correctly, dense-array EEG can be used to analyze brain activity at a spatially resolved scale of less than one centimeter.

Linking the Model to Actual Electrophysiology

For an Individual Neuromorphic Model (IN Model) once we have constructed the geometric and connectivity structure of the brain, we compute an electrical current flow model through the entire head volume. This model enables the IN Model to be used to analyze each person’s EEG activity with temporally and spatially resolved scales of electrical activity recorded at the cortex. However, to fully realize the IN Model, an additional property must be added, and that is the IN Model’s ability to generate LFP/EEG. Neuronal population models have been developed for such a purpose. For IN Models, we propose that any computation model of LFP/EEG generation should closely mirror the CMC, with tunable plasticity parameters inherent to the CMC. This will in turn, allow machine learning to tune the IN Model to match the observed brain activity during two-way communication, with the result being an IN Model that not only reflects current brain function but perhaps predictive of future functions.

Transition to Metaform: Reconstructing the Mind in Digital Form

The mind is the brain, a literal reflection of the connectionist function of neural networks.

• As a result, if we can reconstruct the brain -- in its essential information processing -- we can reconstruct the individual's mind.

• Advances in computational neuroscience theory are showing that the essential information operations of the brain can be described mathematically, and therefore may be computable.

• Advances in neuropsychology are showing that the emotional as well as cognitive capacities of the mind can be related to specific features of brain architecture. This progress provides increasing confidence that we can formulate an adequate computational replication, a Personal Neuromorphic Emulation (PNE) (Tucker & Luu, 2024). Even without an exact molecular reconstruction of the brain, a high resolution computational reconstruction should be able to achieve the full qualities of a complete personality.

From Breakthroughs to Buildout: NADA’s Milestones

Our journey blends deep neuroscience with cutting-edge AI, backed by SBIR grants, FDA-clearance pathways, and proprietary technology. These milestones mark our progress toward building the world’s first functional, whole-brain interface—and position NADA for accelerated growth as we approach Series A.