An inverse approach for elucidating dendritic function.
We outline an inverse approach for investigating dendritic function-structure relationships by optimizing dendritic trees for a priori chosen computational functions. The inverse approach can be applied in two different ways. First, we can use it as a "hypothesis generator" in which we optimize dendrites for a function of general interest. The optimization yields an artificial dendrite that is subsequently compared to real neurons. This comparison potentially allows us to propose hypotheses about the function of real neurons. In this way, we investigated dendrites that optimally perform input-order detection. Second, we can use it as a "function confirmation" by optimizing dendrites for functions hypothesized to be performed by classes of neurons. If the optimized, artificial, dendrites resemble the dendrites of real neurons the artificial dendrites corroborate the hypothesized function of the real neuron. Moreover, properties of the artificial dendrites can lead to predictions about yet unmeasured properties. In this way, we investigated wide-field motion integration performed by the VS cells of the fly visual system. In outlining the inverse approach and two applications, we also elaborate on the nature of dendritic function. We furthermore discuss the role of optimality in assigning functions to dendrites and point out interesting future directions.
Theoretical and Experimental Neurobiology Unit, Okinawa Institute of Science and Technology Onna-Son, Okinawa, Japan.
Benjamin
B
Torben-Nielsen
Klaus M
KM
Stiefel
J Comput Neurosci. 1999 Nov-Dec;7(3):213-34
10596834
Neuron. 2001 Oct 11;32(1):141-9
11604145
Nat Neurosci. 1999 Sep;2(9):848
10461231
Theor Biol Med Model. 2007 Jun 08;4:21
17559645
J Physiol. 1995 May 15;485 ( Pt 1):1-20
7658365
Network. 2009;20(2):69-105
19568982
Neuron. 1999 Feb;22(2):383-94
10069343
Nat Rev Neurosci. 2002 May;3(5):362-70
11988775
J Neurophysiol. 2007 Jul;98(1):513-26
17428904
J Comput Neurosci. 1996 Dec;3(4):313-36
9001975
J Comput Neurosci. 1997 Nov;4(4):349-69
9427120
J Neurosci. 1992 Jun;12(6):2403-16
1607948
Nature. 1996 Jul 25;382(6589):363-6
8684467
PLoS Comput Biol. 2010 Sep 30;6(9):e1000932
20957028
Proc Natl Acad Sci U S A. 1994 May 24;91(11):4736-40
8197127
J Neurosci. 2007 Aug 29;27(35):9247-51
17728438
Nature. 1997 Jun 26;387(6636):869-75
9202119
Science. 2000 Oct 27;290(5492):744-50
11052930
Cereb Cortex. 2002 Apr;12(4):395-410
11884355
J Comp Neurol. 2007 Dec 10;505(5):493-510
17924570
Proc Natl Acad Sci U S A. 2006 Mar 21;103(12):4723-8
16537428
Network. 2002 Aug;13(3):311-25
12222816
Nature. 2000 Mar 16;404(6775):285-9
10749211
Biol Cybern. 2000 Sep;83(3):185-97
11007295
J Neurophysiol. 1997 Jul;78(1):335-50
9242284
J Neurosci. 1999 Nov 15;19(22):10082-97
10559416
Nat Neurosci. 2000 Nov;3 Suppl:1171-7
11127834
Neuroinformatics. 2008 Winter;6(4):257-77
18797828