24-07-2012, 11:08 AM
Whole Brain Emulation A Roadmap
[attachment=28611]
Introduction
Whole brain emulation (WBE), the possible future one‐to‐one modelling of the function of the
human brain, is academically interesting and important for several reasons:
• Research
o Brain emulation is the logical endpoint of computational neuroscience’s
attempts to accurately model neurons and brain systems.
o Brain emulation would help us to understand the brain, both in the lead‐up
to successful emulation and afterwards by providing an ideal test bed for
neuroscientific experimentation and study.
o Neuromorphic engineering based on partial results would be useful in a
number of applications such as pattern recognition, AI and brain‐computer
interfaces.
o As a long‐term research goal it might be a strong vision to stimulate
computational neuroscience.
o As a case of future studies it represents a case where a radical future
possibility can be examined in the light of current knowledge.
The concept of brain emulation
Whole brain emulation, often informally called “uploading” or “downloading”, has been the
subject of much science fiction and also some preliminary studies (see Appendix D for history
and previous work). The basic idea is to take a particular brain, scan its structure in detail,
and construct a software model of it that is so faithful to the original that, when run on
appropriate hardware, it will behave in essentially the same way as the original brain.
Emulation and simulation
The term emulation originates in computer science, where it denotes mimicking the function
of a program or computer hardware by having its low‐level functions simulated by another
program. While a simulation mimics the outward results, an emulation mimics the internal
causal dynamics (at some suitable level of description). The emulation is regarded as
successful if the emulated system produces the same outward behaviour and results as the
original (possibly with a speed difference). This is somewhat softer than a strict mathematical
definition1.
Little need for whole-system understanding
An important hypothesis for WBE is that in order to emulate the brain we do not need to
understand the whole system, but rather we just need a database containing all necessary
low‐level information about the brain and knowledge of the local update rules that change
brain states from moment to moment. A functional understanding (why is a particular piece
of cortex organized in a certain way) is logically separate from detail knowledge (how is it
organised, and how does this structure respond to signals). Functional understanding may be
a possible result from detail knowlede and it may help gather only the relevant information
for WBE, but it is entirely possible that we could acquire full knowledge of the component
parts and interactions of the brain without gaining an insight into how these produce (say)
consciousness or intelligence.
Scale separation
At first it may appear unlikely that a complex system with many degrees of freedom like the
brain could be modelled with the right causal dynamics, but without taking into account the
smallest parts. Microstimulation of individual neurons can influence sensory decisions
(Houweling and Brecht, 2008), showing that very small disturbances can – under the right
circumstances – scale up to behavioural divergences.
[attachment=28611]
Introduction
Whole brain emulation (WBE), the possible future one‐to‐one modelling of the function of the
human brain, is academically interesting and important for several reasons:
• Research
o Brain emulation is the logical endpoint of computational neuroscience’s
attempts to accurately model neurons and brain systems.
o Brain emulation would help us to understand the brain, both in the lead‐up
to successful emulation and afterwards by providing an ideal test bed for
neuroscientific experimentation and study.
o Neuromorphic engineering based on partial results would be useful in a
number of applications such as pattern recognition, AI and brain‐computer
interfaces.
o As a long‐term research goal it might be a strong vision to stimulate
computational neuroscience.
o As a case of future studies it represents a case where a radical future
possibility can be examined in the light of current knowledge.
The concept of brain emulation
Whole brain emulation, often informally called “uploading” or “downloading”, has been the
subject of much science fiction and also some preliminary studies (see Appendix D for history
and previous work). The basic idea is to take a particular brain, scan its structure in detail,
and construct a software model of it that is so faithful to the original that, when run on
appropriate hardware, it will behave in essentially the same way as the original brain.
Emulation and simulation
The term emulation originates in computer science, where it denotes mimicking the function
of a program or computer hardware by having its low‐level functions simulated by another
program. While a simulation mimics the outward results, an emulation mimics the internal
causal dynamics (at some suitable level of description). The emulation is regarded as
successful if the emulated system produces the same outward behaviour and results as the
original (possibly with a speed difference). This is somewhat softer than a strict mathematical
definition1.
Little need for whole-system understanding
An important hypothesis for WBE is that in order to emulate the brain we do not need to
understand the whole system, but rather we just need a database containing all necessary
low‐level information about the brain and knowledge of the local update rules that change
brain states from moment to moment. A functional understanding (why is a particular piece
of cortex organized in a certain way) is logically separate from detail knowledge (how is it
organised, and how does this structure respond to signals). Functional understanding may be
a possible result from detail knowlede and it may help gather only the relevant information
for WBE, but it is entirely possible that we could acquire full knowledge of the component
parts and interactions of the brain without gaining an insight into how these produce (say)
consciousness or intelligence.
Scale separation
At first it may appear unlikely that a complex system with many degrees of freedom like the
brain could be modelled with the right causal dynamics, but without taking into account the
smallest parts. Microstimulation of individual neurons can influence sensory decisions
(Houweling and Brecht, 2008), showing that very small disturbances can – under the right
circumstances – scale up to behavioural divergences.