Noise Exploitation and Adaptation in Neuromorphic Sensors - Engineered Biomimicry - page 43

The corresponding rate metric, also known as the
population rate R ( t ) , is computed as

enhance the SNR of a population code by a factor
of N 2 [16] through the use of noise shaping.
We complete the discussion of neural encoding
by describing other forms of codes: time-to-first
spike, phase encoding, and neural correlations
and synchrony. We do not describe the mathe-
matical models for these codes but illustrate the
codes using Figure 2.4 d.
The time-to-spike is defined as the time
difference between the onset of the stimuli and
the time when a neuron produces the first spike.
The time difference is inversely proportional to
the strength of the stimulus and can efficiently
encode the real-time stimuli compared to the rate-
based code. Time-to-spike code is efficient since

N

R ( t ) = 1
N

R i ( t ) ,

(2.4)

i = 1

where N denotes the number of neurons in the
population. By using the population rate, the
stimuli can now be effectively encoded at a
signal-to-noise ratio that is N 1/2 times higher
than that of a single neuron [15] .
Unfortunately, even an improvement by a fac-
tor of N is not efficient enough to encode fast-
varying sensory stimuli in real time. Later, in
Section 2.4 , we show that lateral inhibition between
the neurons would potentially be beneficial to

FIGURE 2.4 Different types of neural coding: (a) rate, (b) population rate, (c) burst coding, (d) time-to-spike pulse code,
(e) phase pulse code, and (f) correlation and synchrony-based code. Adapted from Ref. 13 .