Correlated Neuronal Activity: High- and Low-Level Views - Computational Neuroscience: A Comprehensive Approach - page 350

12.5.1 Coincidence detection

The classic mechanism underlying a neuron's sensitivity to temporal patterns is co-
incidence detection [2-50]. Neurons can certainly be sensitive to the arrival of spikes
from two or more inputs within a short time window; the most notable examples are
from the auditory system [5, 21]. The question is, however, whether this mechanism
is commonly used throughout the cortex.
In the traditional view, coincidence detection is based on a very short membrane
time constant [2-50]. However, it may be greatly enhanced by the spatial arrange-
ment of synapses and by nonlinear processes. For instance, nearby synapses may
interact strongly, forming clusters in which synaptic responses to simultaneous acti-
vation are much stronger than the sum of individual, asynchronous responses [58].
A neuron could operate with many such clusters which, if located on electrotonically
distant parts of the dendritic tree, could act independently of each other. Voltage-
dependent channels in the dendrites may mediate or boost such nonlinear interac-
tions between synapses [58-64]. These nonlinearities could in principle increase
the capacity for coincidence detection to the point of making the neuron selective for
specific temporal sequences of input spikes, and the very idea of characterizing those
inputs statistically would be questionable. However, the degree to which the cortex
exploits such nonlinearities is uncertain.
The coincidence detection problem can also be posed in terms of the capacity of
a network to preserve the identity of a volley of spikes fired by multiple neurons
within a short time window [18, 31]. Suppose a neuron receives a volley of input
spikes; what is the likelihood of evoking a response (reliability), and what will its
timing be relative to the center of mass of the input volley (precision)? Theoretical
studies suggest that the temporal precision of the response spikes is not limited by
the membrane time constant, but rather by the up-slope of excitatory synaptic events.
Thus, under the right conditions a volley of synchronized action potentials may prop-
agate in a stable way through many layers [31]. Whether areas of the cortex actually
exchange information in this way is still unclear, and other modes of information
transmission are possible [88].

12.5.2 Fluctuations and integrator models

The flip side of coincidence detection is integration. Neurons may also sum or av-
erage many inputs to generate an action potential [2, 50, 73]. Earlier theoretical
arguments suggested that neurons acting as integrators would not be sensitive to
temporal correlations [74], or that these would only matter at high firing rates, where
refractory effects become important [13, 60]. However, later results [67, 69] show
that neurons may still be highly sensitive to weak correlations in their inputs even if
there is no spatial segregation along the dendritic tree and no synaptic interactions
beyond the expected temporal summation of postsynaptic currents.
A key quantity in this case is the balance of the neuron, which refers to the relative
strength between inhibitory and excitatory inputs [67, 82, 73]. When the neuron
is not balanced, excitation is on average stronger than inhibition, such that the net