### abstract ###
Selective attention is an important filter for complex environments where distractions compete with signals.
Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object.
However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood.
We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations.
Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics.
These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance.
Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.
### introduction ###
Past work with both human and animal subjects has focused on neural correlates of attention.
Attention raises the firing rate and the input output gain of orientation-selective neurons in the visual cortex CITATION CITATION, and shifts response curves so that physiologically relevant stimuli fall in the high-gain region CITATION, CITATION.
Also, when attended stimuli overlap with a recorded receptive field, gamma-band frequency components of local field potentials and single-unit spike responses increase CITATION CITATION.
Gamma oscillations in the field potential likely reflect correlated network activity CITATION, CITATION, as supported by simulations of spiking neurons with inhibitory or recurrent excitatory inhibitory coupling CITATION.
Attention is thought to influence cholingergic neuromodulation CITATION, which presumably affects synchrony of interneuron networks involved in gamma oscillations CITATION, CITATION, CITATION.
It is well-known that correlated network discharge effectively drives postsynaptic cells CITATION, making gamma-band activity a signature of efficient signal propagation.
This would allow attended objects to increase downstream responses, as compared to nonattended objects.
In contrast, we assess the role of gamma oscillations in the signal coding of neural populations participating in gamma oscillatory dynamics.
Tasks where attention improves performance typically involve discrimination between different signals, such as visual cues with different colors, shapes, or orientations CITATION, CITATION CITATION.
Although there are a large number of studies exploring how gamma rhythms are generated in networks of spiking neurons, the mechanisms by which gamma oscillations modify signal discrimination are elusive in three aspects.
First, the relation between gain modulation and gamma oscillations, both of which are attention-dependent, is unclear.
Second, the temporal relation between a network gamma rhythm and the time course of a driving signal is often unclear.
Third, gamma-induced synchronous firing may be deleterious for coding due to increased variability of population activity CITATION .
A popular framework for neural coding is that the number of spikes produced by a single neuron or a population of neurons carries information about a driving signal.
However, in vivo spike trains often show a spike count Fano factor that is close to or even exceeds unity CITATION CITATION.
This trial-to-trial variability is deleterious to the code performance and degrades putative spike-count based signal-discrimination schemes.
In certain situations, Fano factors much less than 1 are observed in the visual cortex CITATION, CITATION, the auditory cortex CITATION, and the salamander retina CITATION.
In an extreme case, if a neuron fires with high probability in response to a relevant input signal and rarely fires otherwise CITATION, then the signal can be estimated from the spike count with small error.
In addition, spike-timing reliability, for which a neuron robustly emits just a single spike during a steep upstroke of the input and seldom fires elsewhere CITATION, CITATION, is also supportive of such binary spiking.
In this study we model the essence of a gamma frequency modulation as a simple rhythmic forcing of a population of uncoupled spiking neurons.
We show that gamma oscillations endow population spike counts with binomial-like statistics, which improve signal discrimination over a range of stimuli through reduced spike-count variability.
In this way, we propose a connection between gamma oscillations and enhanced task performance found in behavioral experiments.
Our results are both distinct and complementary to previously described influences of rhythmic network behavior in temporal coding schemes by improving spike precision CITATION, CITATION or by providing a clock for a phase-based code CITATION CITATION .
