Mind Uploading & Digital Consciousness - Research

Abstract

Mind uploading, or whole-brain emulation, remains one of the most profound and speculative goals of neuroscience and AI. The central hypothesis is that a faithful digital replica of a brain's neural network—its connectome—would be sufficient to instantiate its consciousness. This Masters in Computer Science proposal sidesteps the philosophical debate and focuses on a critical, unanswered computational question: what is the minimal level of biological detail required for a functionally accurate brain simulation? The research will develop a multi-scale modeling framework to simulate a small, well-understood neural circuit (e.g., from the C. elegans worm or a rodent hippocampus) at different levels of abstraction and quantify the "functional distance" between these simulations.

Key Research Questions in Whole-Brain Emulation

The Granularity of Simulation: Is a point-neuron model sufficient, or do we need to simulate detailed dendritic branching, ion channel dynamics, and glial cell interactions to capture the essential computations of a neural circuit?
The Role of Neuromodulation: How critical is it to model the slow, diffusive action of neuromodulators (like dopamine or serotonin) versus the fast, synaptic communication between neurons? How much does this "chemical context" alter the network's function?
Quantifying Functional Equivalence: What is a robust metric for determining if two different simulations (e.g., a simple one and a complex one) are "functionally equivalent"? This involves comparing their output spike trains, information processing capacity, and their ability to learn a benchmark task.
Computational Tractability: What is the computational cost (in terms of processing power and memory) associated with each level of simulation detail? This will help map out the hardware requirements for scaling up from small circuits to whole brains.

Proposed Masters in Computer Science Research: A Multi-Scale Model of the Hippocampal Place Cell Network

This thesis will focus on simulating the network of "place cells" in the hippocampus, a circuit critical for spatial memory and one that is relatively well-understood.

Develop a "Ground Truth" Model: Create a highly detailed, biophysically realistic simulation of a small network of hippocampal neurons using a platform like NEURON. This model will incorporate detailed cell morphologies and ion channel dynamics based on experimental data.
Create Abstracted Models: Develop a series of simpler models of the same network, ranging from a basic leaky integrate-and-fire (LIF) point-neuron model to a more complex model with simplified dendritic compartments.
Functional Benchmarking: Subject all models to the same simulated input, representing an animal moving through a virtual environment. Compare the outputs of the abstracted models to the "ground truth" model. The key metric will be how well each model can reproduce the precise spike-timing and spatial firing patterns of the ground-truth simulation.
Information-Theoretic Analysis: Use tools from information theory to measure how much information about the "animal's" location is encoded in the neural activity of each model. This provides a rigorous way to quantify the functional cost of abstraction.

Long-Term Implications for Ghana and Africa

While this is fundamental, "blue-sky" research, its implications are enormous. Understanding the principles of neural computation is the bedrock of next-generation AI. The highly efficient, low-power learning algorithms discovered through this research could be deployed on neuromorphic hardware to solve real-world problems in Ghana, from medical diagnosis to climate modeling, without needing massive data centers. Furthermore, a deeper understanding of brain function will be essential for developing treatments for neurological and mental health disorders, which are a growing burden in Africa. By participating in this foundational research, Ghanaian scientists can contribute to a global effort that may ultimately unlock the secrets of the mind itself and lead to artificial intelligence that is as efficient and flexible as the human brain.