Selectionist and evolutionary approaches to brain function: a critical appraisal - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Apr 26:6:24.
doi: 10.3389/fncom.2012.00024. eCollection 2012.

Selectionist and evolutionary approaches to brain function: a critical appraisal

Chrisantha Fernando  1 , Eörs Szathmáry, Phil Husbands
Affiliations

Selectionist and evolutionary approaches to brain function: a critical appraisal

Chrisantha Fernando et al. Front Comput Neurosci. .

Abstract

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman's theory of neuronal group selection, Changeux's theory of synaptic selection and selective stabilization of pre-representations, Seung's Darwinian synapse, Loewenstein's synaptic melioration, Adam's selfish synapse, and Calvin's replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price's covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.

Keywords: Darwinian neurodynamics; Izhikevich spiking networks; causal inference; hill-climbers; neural Darwinism; neuronal group selection; neuronal replicator hypothesis; price equation.

PubMed Disclaimer

Figures

Figure 1
Figure 1
The general selection model of price (left) and its application to neuronal groups (right).
Figure 2
Figure 2
Growth and stabilization of synapses, adapted from Changeux (1985).
Figure 3
Figure 3
Synaptic mutation replication (left) and synaptic mutations (right), adapted from Adams (1998).
Figure 4
Figure 4
Crossover operation for Bayesian networks. Adapted from Myers et al. (1999).
Figure 5
Figure 5
A selection amplifier topology from Lieberman et al. (2005). Vertices that change often, due to replacement from the neighbors, are colored in orange. In the present context each vertex can be a neuron or neuronal group that can inherit its state from its upstream neighbors and pass on its state to the downstream neighbors. Neuronal evolution would be evolution on graphs.
Figure 6
Figure 6
(Left): the gantry robot. A CCD camera head moves at the end of a gantry arm. In the study referred to in the text 2D movement was used, equivalent to a wheeled robot with a fixed forward pointing camera. A validated simulation was used: controllers developed in the simulation work at least as well on the real robot. (Right): the simulated arena and robot. The bottom right view shows the robot position in the arena with the triangle and rectangle. Fitness is evaluated on how close the robot approaches the triangle. The top right view shows what the robot “sees,” along with the pixel positions selected by evolution for visual input. The bottom left view shows how the genetically set pixels are connected into the control network whose gas levels are illustrated. The top left view shows current activity of nodes in the GasNet.
Figure 7
Figure 7
Outline of a mechanism for copying patterns of synaptic connections between neuronal groups. The pattern of connectivity from the lower layer is copied to the upper layer. See text.
Figure A1
Figure A1
A basic GasNet showing excitatory (solid) and inhibitory (dashed) “electrical” connections and a diffusing virtual gas creating a “chemical” gradient.
Figure A2
Figure A2
Overall structure of a two-cause causal network (above) and its inputs that represent two event types (below). The bias of causal network neurons is set to 9.5. As shown on the graph below, this means that external input alone (at fixed synaptic weight 5.25) causes a neuron in the causal network to fire with probability only 0.014. However, if external input is simultaneous with internal delay line input from another causal network neuron, then the neuron will fire with probability 0.15 (given the initial internal delay line synaptic weight of 2.5). If a causal delay line has been potentiated to its maximum (ACh depressed) weight of 4.0, then simultaneous external and internal inputs to a neuron will cause it to fire with probability 0.43. However, internal delay line activation alone (without simultaneous external input) is insufficient to make a neuron fire with any greater than probability 0.004 (even at the maximum internal ACh depressed weight of 4.0). This arrangement insures that simultaneous input from external events and internal delay lines is an order of magnitude more likely to cause a neuron to fire than unsynchronized inputs from either source alone. This non-linearity is essential in training of the causal network because it means that only connections that mirror the delays between received events are potentially strengthened.
Figure A3
Figure A3
Successful copying of common-cause, common-effect, and causal chain networks with all delay combinations from 1 to 4 ms. The “parental” network is stimulated randomly and the resulting spikes are passed to the “offspring” network by a topographic map.
Figure A4
Figure A4
One-, two-, and three-neuron cycles. Only if the cycle has a period greater than the refractory period of the neurons within it can the cycle be inferred. For example three-cycles with 4 ms delays can be inferred when the refractory period is 10 ms.
Figure A5
Figure A5
Feedforward loops (FFLs) with a sample of delays. Some regions in delay space cannot be properly inferred. Light red circles mark synapse sets that were not strengthened when they should have been, i.e., false-negatives.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Adams P. (1998). Hebb and Darwin. J. Theor. Biol. 195, 419–43810.1006/jtbi.1997.0620 - DOI - PubMed
    1. Arnold D., Beyer H. (2003). A comparison of evolution strategies with other direct search methods in the presence of noise. Comput. Optim. Appl. 24, 135–15910.1023/A:1021810301763 - DOI
    1. Aunger R. (2002). The Electric Meme: A New Theory of How We Think. New York: The Free Press
    1. Barnett L. (2001). “Netcrawling-optimal evolutionary search with neutral networks,” in Congress on Evolutionary Computation CEC’01 (Seoul: IEEE Press; ), 30–37
    1. Bi G.-Q., Poo M.-M. (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 15, 10464–10472 - PMC - PubMed

LinkOut - more resources