Cerebral Cortex, 11(12):1144-9, 2001
Dendritic Inhibition Enhances Neural Coding Properties
M. W. Spratling and M. H. Johnson
Centre for Brain and Cognitive Development, Birkbeck College, London. UK.
Abstract
The presence of a large number of inhibitory contacts at the soma and axon initial segment of
cortical pyramidal cells has inspired a large and influential class of neural network model which use
post-integration lateral inhibition as a mechanism for competition between nodes. However, inhibitory
synapses also target the dendrites of pyramidal cells. The role of this dendritic inhibition in competi-
tion between neurons has not previously been addressed. We demonstrate, using a simple computational
model, that such pre-integration lateral inhibition provides networks of neurons with useful representa-
tional and computational properties which are not provided by post-integration inhibition.
Introduction
Lateral inhibition between cortical excitatory cells plays an important role in determining the receptive
field properties of those cells. Such lateral inhibition provides a mechanism through which cells compete
to respond to the current pattern of stimulation. Inhibitory inputs are concentrated on the soma and axon
initial segment of pyramidal cells (Mountcastle, 1998; Somogyi and Martin, 1985) where they can be
equally effective at inhibiting responses to excitatory inputs stimulating any part of the dendritic tree.
This observation has formed the basis for many theories of receptive field formation, and is an es-
sential feature of many computational (neural network) models of cortical function (Foldiak, 1989, 1990,
1991; Grossberg, 1987; Hertz et al., 1991; Kohonen, 1997; Marshall, 1995; Oja, 1989; O’Reilly, 1998;
Ritter et al., 1992; Rumelhart and Zipser, 1985; Sanger, 1989; Sirosh and Miikkulainen, 1994; Swindale,
1996; von der Malsburg, 1973; Wallis, 1996). Such neural network algorithms have also found application
beyond the neurosciences as a means of data analysis, classification and visualization in a huge variety
of fields. These algorithms vary greatly in the details of their implementation. In some, competition is
achieved explicitly by using lateral connections between the nodes of the network (Foldiak, 1989, 1990;
Marshall, 1995; Oja, 1989; O’Reilly, 1998; Sanger, 1989; Sirosh and Miikkulainen, 1994; Swindale, 1996;
von der Malsburg, 1973), while in others competition is implemented implicitly through a selection pro-
cess which chooses the ‘winning’ node(s) (Foldiak, 1991; Grossberg, 1987; Hertz et al., 1991; Kohonen,
1997; Ritter et al., 1992; Rumelhart and Zipser, 1985; Wallis, 1996). However, in all of these algorithms
nodes compete for the right to generate a response to the current pattern of input activity. A node’s suc-
cess in this competition is dependent on the total strength of the stimulation it receives and nodes which
compete unsuccessfully have their output activity suppressed. This class of models can thus be described
as implementing ‘post-integration inhibition’.
Inhibitory contacts also occur on the dendrites of cortical pyramidal cells (Kim et al., 1995; Rockland,
1998) and certain classes of interneuron (e.g., double bouquet cells) specifically target dendritic spines
and shafts (Mountcastle, 1998; Tamas et al., 1997). Such contacts would have relatively little impact
on excitatory inputs more proximal to the cell body or on the action of synapses on other branches of
the dendritic tree. Thus these synapses do not appear to contribute to post-integration inhibition. However,
such synapses are likely to have strong inhibitory effects on inputs within the same dendritic branch that are
more distal to the site of inhibition (Borg-Graham et al., 1998; Koch et al., 1983; Koch and Segev, 2000;
Rall, 1964; Segev, 1995). Hence, they could potentially selectively inhibit specific groups of excitatory
inputs. Related synapses cluster together within the dendritic tree so that local operations are performed by
multiple, functionally distinct, dendritic subunits before integration at the soma (Hausser, 2001; Hausser
et al., 2000; Koch and Segev, 2000; Mel, 1994, 1999; Segev, 1995; Segev and Rall, 1998). Dendritic
inhibition could thus act to ‘block’ the output from individual functional compartments. It has long been
recognized that a dendrite composed of multiple subunits would provide a significant enhancement to the
computational powers ofan individual neuron (Mel, 1993, 1994, 1999) and that dendritic inhibition could