Optimal energy-efficient coding in sensory neurons
#Neuron
#modeling
#sensory
#neurons
#energy-efficient
#neural
#coding
Evolutionary pressure suggests that the spike-based code in the sensory nervous system
should satisfy two opposing constraints:
1) minimize signal distortion in the encoding process (i.e., maintain fidelity)
by keeping the average spike rate as high as possible, and
2) minimize the metabolic load on the neuron by keeping the average spike
rate as low as possible.
We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy
these conflicting demands. An energy-fidelity trade-off can be obtained through a
constrained optimization process that achieves the lowest signal distortion for a given
constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained
neuronal source code and show that it leads to a dynamic threshold that functions as an
internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes
are emitted only when the coding error reaches this threshold. A stochastic extension is
obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold.
We show that the source-coding neuron model i) reproduces experimentally observed
spike-times in response to a stimulus, and ii) reproduces the serial correlations in the
observed sequence of inter-spike intervals, using data from a peripheral sensory neuron
and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code,
although a temporal code, is in the limit of high firing rates an instantaneous rate code and
accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting
possible biophysical (ionic) mechanisms for this coding scheme.
Date and Time
Location
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Registration
- Date: 21 Feb 2018
- Time: 04:00 PM to 05:00 PM
- All times are (GMT-06:00) US/Central
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Rice University
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6100 Main Street
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Houston, Texas
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United States
77005
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Building:
Duncan Hall
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Room Number:
1049
Speakers
Prof. Douglas L. Jones, Univ. of Illinois at Urbana-Champaign of University of Illinois at Urbana-Champaign
Topic:
Optimal energy-efficient coding in sensory neurons
Evolutionary pressure suggests that the spike-based code in the sensory nervous system
should satisfy two opposing constraints:
1) minimize signal distortion in the encoding process (i.e., maintain fidelity)
by keeping the average spike rate as high as possible, and
2) minimize the metabolic load on the neuron by keeping the average spike
rate as low as possible.
We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy
these conflicting demands. An energy-fidelity trade-off can be obtained through a
constrained optimization process that achieves the lowest signal distortion for a given
constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained
neuronal source code and show that it leads to a dynamic threshold that functions as an
internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes
are emitted only when the coding error reaches this threshold. A stochastic extension is
obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold.
We show that the source-coding neuron model i) reproduces experimentally observed
spike-times in response to a stimulus, and ii) reproduces the serial correlations in the
observed sequence of inter-spike intervals, using data from a peripheral sensory neuron
and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code,
although a temporal code, is in the limit of high firing rates an instantaneous rate code and
accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting
possible biophysical (ionic) mechanisms for this coding scheme.
Biography:
Douglas L. Jones received the BSEE, MSEE, and Ph.D. degrees from Rice University
in 1983, 1985, and 1987, respectively. During the 1987-1988 academic year, he was
at the University of Erlangen-Nuremberg in Germany on a Fulbright postdoctoral
fellowship. Since 1988, he has been with the University of Illinois at Urbana-Champaign,
where he is currently the William L. Everitt Distinguished Professor in Electrical and
Computer Engineering, the Neuroscience Program, the Coordinated Science Laboratory,
and the Beckman Institute. He was the Director of Illinois' Advanced Digital Sciences
Center in Singapore from 2013-2017. In the Spring semester of 1999 he served as the
Texas Instruments Visiting Professor at Rice University. He is a Fellow of the IEEE and
has served as an at-large member of the Board of Governors of the IEEE Signal Processing
Society. He is an author of the laboratory textbook "A Digital Signal Processing Laboratory
Using the TMS32010." His research interests are in signal processing systems, including
time-frequency signal analysis, adaptive acoustic array processing, energy-efficient
systems, novel sensing technologies, neuro-engineering, and various applications
such as advanced hearing aids.
