Optimized Dendritic Morphologies for Noisy Inputs

Dendrites are the cellular protrusions of neurons receiving the majority of synaptic inputs. We investigated the structure–function relationship of the dendrites of model neurons optimized for input order detection of stochastic inputs. For this purpose, we used an inverse method based on a genetic algorithm. In this method, via iterative test and selection steps, the genetic algorithm finds a dendritic structure as good as possible for a user-selected neural computation. In a previous study, we generated model neurons optimized for reacting strongly to two groups of synaptic inputs occurring in one but not the reverse temporal order. In the current study, we added both temporal noise (synapse activation times) and spatial noise (synapse placement) to this computational task. We observed that the model neurons which were exposed to a more noisy input generally had smaller dendritic trees. We explain this finding by the fact that for input–order detection, sampling from more varied responses is advantageous and that positive outliers in a population are selected for. We conclude with a general discussion of signal integration in neurons, dendrites, and noise.

[1]  Henry Markram,et al.  Preserving axosomatic spiking features despite diverse dendritic morphology. , 2013, Journal of neurophysiology.

[2]  A. Destexhe,et al.  Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. , 2000, Journal of neurophysiology.

[3]  A. Destexhe,et al.  The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.

[4]  G. Shepherd,et al.  Emerging rules for the distributions of active dendritic conductances , 2002, Nature Reviews Neuroscience.

[5]  Walter Senn,et al.  Hyperpolarization-activated current Ih disconnects somatic and dendritic spike initiation zones in layer V pyramidal neurons. , 2003, Journal of neurophysiology.

[6]  J. Schiller,et al.  NMDA spikes in basal dendrites of cortical pyramidal neurons , 2000, Nature.

[7]  Benjamin Torben-Nielsen,et al.  An Inverse Approach for Elucidating Dendritic Function , 2010, Front. Comput. Neurosci..

[8]  Benjamin Torben-Nielsen,et al.  Systematic mapping between dendritic function and structure , 2009, Network.

[9]  M. Kennedy,et al.  Signal-processing machines at the postsynaptic density. , 2000, Science.

[10]  T. Sejnowski,et al.  Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons , 2001, Neuroscience.

[11]  F. Helmchen,et al.  Background Synaptic Activity Is Sparse in Neocortex , 2006, The Journal of Neuroscience.

[12]  I Segev,et al.  Untangling dendrites with quantitative models. , 2000, Science.

[13]  N. Spruston,et al.  Action potential initiation and backpropagation in neurons of the mammalian CNS , 1997, Trends in Neurosciences.

[14]  P. O. Bishop,et al.  Orientation specificity of cells in cat striate cortex. , 1974, Journal of neurophysiology.

[15]  Jackie Schiller,et al.  Spatiotemporally graded NMDA spike/plateau potentials in basal dendrites of neocortical pyramidal neurons. , 2008, Journal of neurophysiology.

[16]  M. Martina,et al.  Properties and Functional Role of Voltage-Dependent Potassium Channels in Dendrites of Rat Cerebellar Purkinje Neurons , 2003, The Journal of Neuroscience.

[17]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. , 1993, Journal of neurophysiology.

[18]  André van Schaik,et al.  Temporal Order Detection and Coding in Nervous Systems , 2013, Neural Computation.

[19]  Idan Segev,et al.  The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells , 1992, Neural Computation.

[20]  M. Häusser,et al.  Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons , 2010, Science.

[21]  T. Sejnowski,et al.  Mapping function onto neuronal morphology. , 2007, Journal of neurophysiology.

[22]  Benjamin Torben-Nielsen,et al.  Wide-Field Motion Integration in Fly VS Cells: Insights from an Inverse Approach , 2010, PLoS Comput. Biol..

[23]  Idan Segev,et al.  Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing , 1998, Neural Computation.

[24]  Hermann Cuntz.  The Dendritic Density Field of a Cortical Pyramidal Cell , 2012, Front. Neuroanat..

[25]  Alexander Borst,et al.  The Morphological Identity of Insect Dendrites , 2008, PLoS Comput. Biol..

[26]  Bartlett W. Mel,et al.  Computational subunits in thin dendrites of pyramidal cells , 2004, Nature Neuroscience.

[27]  R. C Cannon,et al.  An on-line archive of reconstructed hippocampal neurons , 1998, Journal of Neuroscience Methods.

[28]  James M. Bower,et al.  Prolonged responses in rat cerebellar Purkinje cells following activation of the granule cell layer: an intracellular in vitro and in vivo investigation , 2004, Experimental Brain Research.

[29]  B. Sakmann,et al.  A new cellular mechanism for coupling inputs arriving at different cortical layers , 1999, Nature.

[30]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.