Limits...
On the precarious path of reverse neuro-engineering.

Marom S, Meir R, Braun E, Gal A, Kermany E, Eytan D - Front Comput Neurosci (2009)

Bottom Line: We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle.If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined?Implications to the way we do Biology are discussed.

View Article: PubMed Central - PubMed

Affiliation: Network Biology Research Laboratories, Technion - Israel Institute of Technology Haifa, Israel. marom@technion.ac.il

ABSTRACT
In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle. If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined? Implications to the way we do Biology are discussed.

No MeSH data available.


The upper panel shows the spikes (blue dots) emitted by ∼60 neurons in response to one stimulus to the network. The stimulus was triggered by the right eye of the agent. Note that in this case there were no spontaneous spikes in the network immediately preceding the stimulus, although this need not be generally true. Black circles depict the first spikes (of each neuron) following the stimulus. The actual design principle is based on the rank-order of these first spikes, resulting in a unique “time-less” neuronal recruitment order; the recruitment order in the top panel example is 24, 17, 26, 25, 48, 1, 13,…. The bottom panel shows the population count histogram (“network spike”).
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Figure 2: The upper panel shows the spikes (blue dots) emitted by ∼60 neurons in response to one stimulus to the network. The stimulus was triggered by the right eye of the agent. Note that in this case there were no spontaneous spikes in the network immediately preceding the stimulus, although this need not be generally true. Black circles depict the first spikes (of each neuron) following the stimulus. The actual design principle is based on the rank-order of these first spikes, resulting in a unique “time-less” neuronal recruitment order; the recruitment order in the top panel example is 24, 17, 26, 25, 48, 1, 13,…. The bottom panel shows the population count histogram (“network spike”).

Mentions: To prove our point about the precariousness of reverse engineering in biology, let us test the validity of an interpretation that is “orthogonal” to the actual design principle (algorithm) of the above toy. The actual design principle of representation, as explained above, relies on the rank order of first spikes in a subset of identified broadly-tuned neurons. Now, suppose that a neurophysiologist wishes to test an hypothesis, according to which representation of the visual field is embedded in a population response rate. This idea of population rate differs from the actual design principle in several key aspects: Neuronal identities are ignored and temporal relations between spikes are ignored; only the temporal profile of total spike counts throughout the network, following input, is considered. Note that thus defined, there is practically no relation between this population-based representation scheme and the original (rank-order) scheme that drives the agent1. Figure 2 shows the process of data reduction.


On the precarious path of reverse neuro-engineering.

Marom S, Meir R, Braun E, Gal A, Kermany E, Eytan D - Front Comput Neurosci (2009)

The upper panel shows the spikes (blue dots) emitted by ∼60 neurons in response to one stimulus to the network. The stimulus was triggered by the right eye of the agent. Note that in this case there were no spontaneous spikes in the network immediately preceding the stimulus, although this need not be generally true. Black circles depict the first spikes (of each neuron) following the stimulus. The actual design principle is based on the rank-order of these first spikes, resulting in a unique “time-less” neuronal recruitment order; the recruitment order in the top panel example is 24, 17, 26, 25, 48, 1, 13,…. The bottom panel shows the population count histogram (“network spike”).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691154&req=5

Figure 2: The upper panel shows the spikes (blue dots) emitted by ∼60 neurons in response to one stimulus to the network. The stimulus was triggered by the right eye of the agent. Note that in this case there were no spontaneous spikes in the network immediately preceding the stimulus, although this need not be generally true. Black circles depict the first spikes (of each neuron) following the stimulus. The actual design principle is based on the rank-order of these first spikes, resulting in a unique “time-less” neuronal recruitment order; the recruitment order in the top panel example is 24, 17, 26, 25, 48, 1, 13,…. The bottom panel shows the population count histogram (“network spike”).
Mentions: To prove our point about the precariousness of reverse engineering in biology, let us test the validity of an interpretation that is “orthogonal” to the actual design principle (algorithm) of the above toy. The actual design principle of representation, as explained above, relies on the rank order of first spikes in a subset of identified broadly-tuned neurons. Now, suppose that a neurophysiologist wishes to test an hypothesis, according to which representation of the visual field is embedded in a population response rate. This idea of population rate differs from the actual design principle in several key aspects: Neuronal identities are ignored and temporal relations between spikes are ignored; only the temporal profile of total spike counts throughout the network, following input, is considered. Note that thus defined, there is practically no relation between this population-based representation scheme and the original (rank-order) scheme that drives the agent1. Figure 2 shows the process of data reduction.

Bottom Line: We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle.If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined?Implications to the way we do Biology are discussed.

View Article: PubMed Central - PubMed

Affiliation: Network Biology Research Laboratories, Technion - Israel Institute of Technology Haifa, Israel. marom@technion.ac.il

ABSTRACT
In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle. If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined? Implications to the way we do Biology are discussed.

No MeSH data available.