4 research outputs found
Synchrony in Neuronal Communications: An Energy Efficient Scheme
We are interested in understanding the neural correlates of attentional
processes using first principles. Here we apply a recently developed first
principles approach that uses transmitted information in bits per joule to
quantify the energy efficiency of information transmission for an
inter-spike-interval (ISI) code that can be modulated by means of the synchrony
in the presynaptic population. We simulate a single compartment
conductance-based model neuron driven by excitatory and inhibitory spikes from
a presynaptic population, where the rate and synchrony in the presynaptic
excitatory population may vary independently from the average rate. We find
that for a fixed input rate, the ISI distribution of the post synaptic neuron
depends on the level of synchrony and is well-described by a Gamma distribution
for synchrony levels less than 50%. For levels of synchrony between 15% and 50%
(restricted for technical reasons), we compute the optimum input distribution
that maximizes the mutual information per unit energy. This optimum
distribution shows that an increased level of synchrony, as it has been
reported experimentally in attention-demanding conditions, reduces the mode of
the input distribution and the excitability threshold of post synaptic neuron.
This facilitates a more energy efficient neuronal communication.Comment: 6 pages, 5 figures, Accepted for publication to IWCIT 201
Neuronal Synchronization Can Control the Energy Efficiency of Inter-Spike Interval Coding
The role of synchronous firing in sensory coding and cognition remains
controversial. While studies, focusing on its mechanistic consequences in
attentional tasks, suggest that synchronization dynamically boosts sensory
processing, others failed to find significant synchronization levels in such
tasks. We attempt to understand both lines of evidence within a coherent
theoretical framework. We conceptualize synchronization as an independent
control parameter to study how the postsynaptic neuron transmits the average
firing activity of a presynaptic population, in the presence of
synchronization. We apply the Berger-Levy theory of energy efficient
information transmission to interpret simulations of a Hodgkin-Huxley-type
postsynaptic neuron model, where we varied the firing rate and synchronization
level in the presynaptic population independently. We find that for a fixed
presynaptic firing rate the simulated postsynaptic interspike interval
distribution depends on the synchronization level and is well-described by a
generalized extreme value distribution. For synchronization levels of 15% to
50%, we find that the optimal distribution of presynaptic firing rate,
maximizing the mutual information per unit cost, is maximized at ~30%
synchronization level. These results suggest that the statistics and energy
efficiency of neuronal communication channels, through which the input rate is
communicated, can be dynamically adapted by the synchronization level.Comment: 47 pages, 14 figures, 2 Table