How metabolic constraints shape neuronal adaptation: A unifying objective function for synaptic plasticity
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1
Frankfurt Institute for Advanced Studies, Germany
The brain is always facing limited metabolic resources and thus it should adapt itself to function in an energy-efficient manner. The consideration of an energy-efficient coding leads to the requirement of a sparse output firing distribution. This idea resulted in the formulation of sparse coding, which has shown how such a constraint can shape the neuronal circuit and its representations.
But not only the action potentials them self are metabolically expensive, but also their transmission and postsynaptic effects. A presynaptic spike will increase the postsynaptic potential (EPSP) and reestablishing the resting potential consumes energy. Thus, if the EPSP does not contribute on average to the firing of the postsynaptic cell, this synapse wastes energy and should be weakened. If it otherwise does repeatedly help to fire the cell it should be strengthened.
So, basically Hebb's postulate can be seen as a consequence of energy efficient signaling. Putting it in statistical terms, we want the membrane potential to be either near the resting level or above threshold, but spent less time in the subthreshold regime. Therefore, we require a sparse distribution of the membrane potential.
In this work, we formulate a differential-Hebbian learning rule by applying a gradient descent on a sparseness measure of the membrane potential. We conduct compartmental simulations in NEURON and compare the results to experimental data. Our learning rule reproduces many different protocols and experimental setups for long-term synaptic plasticity. These not only include timing-dependent plasticity (STDP) for pairing and frequency protocols and their location dependence along the dendritic tree, but also rate-based metaplasticity similar to the BCM rule. Therefore, we show that one can construct a unifying objective function for synaptic long-term plasticity and that energy efficiency is a major part of it. Thus, we cover two of Marr's three levels of analysis: the computational level in terms of energy efficiency and the algorithmic level based on a biophysical differential-Hebbian learning rule from which one could establish the link to the actual biological implementation.
Acknowledgements
This work was supported by the BMBF Project 'Bernstein Fokus: Neurotechnologie Frankfurt', FKZ 01GQ0840
Keywords:
Learning and plasticity
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
learning and plasticity (please use "learning and plasticity" as keyword)
Citation:
Krieg
D and
Triesch
J
(2011). How metabolic constraints shape neuronal adaptation: A unifying objective function for synaptic plasticity.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00090
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Mr. Daniel Krieg, Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60438, Germany, krieg@fias.uni-frankfurt.de