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coordinations. The brain can thus be reconceptualised, from the connectionist im-
age of a massive switchboard or telegraph network to something more like a radio
broadcast network or even an internet (John
1972
).
Neurophysiological evidence exists for temporal coding in virtually every sen-
sory system, and in many diverse parts of the brain (Cariani
1995
;
2001c
, Miller
2000
, Perkell and Bullock
1968
, Mountcastle
1967
), and at many time scales
(Thatcher and John
1977
). We have investigated temporal codes for pitch in the early
stages of the auditory system (Cariani and Delgutte
1996
, Ando and Cariani
2009
,
Cariani
1999
). The neural representation that best accounts for pitch perception, in-
cluding the missing fundamental and many other assorted pitch-related phenomena,
is based on interspike intervals, which are the time durations between spikes in a
spike train. Periodic sounds impress their repeating time structure on the timings
of spikes, such that distributions of the interspike intervals produced in auditory
neurons reflect stimulus periodicities. Peaks in the global distribution of interspike
intervals amongst the tens of thousands of neurons that make up the auditory nerve
robustly and precisely predict the pitches that will be heard. In this kind of code,
timing is everything, and it is irrelevant which particular neurons are activated the
most. The existence of such population-based statistical, and purely temporal repre-
sentations begs the question of whether information in other parts of the brain could
be represented this way as well (Cariani and Micheyl
2012
).
Temporal patterns of neural spiking are said to be stimulus-driven in they reflect
the time structure of the stimulus or stimulus-triggered if they produce response
patterns that are unrelated to that time structure. The presence of stimulus-driven
patterns of spikes convey to the rest of the system that a particular stimulus has
been presented. Further, neural assemblies can be electrically conditioned to emit
characteristic stimulus-triggered endogenous patterns that provide readouts that a
given combination of rewarded attributes has been recognised (John
1967
, Morrell
1967
).
The neuronal evidence for temporal coding also provokes the question of what
kinds of neuronal processing architectures might conceivably make use of infor-
mation in this form. Accordingly several types of neural processing architectures
capable of multiplexing temporal patterns have been conceived (Izhikevich
2006
,
Cariani
2004
, Chung et al.
1970
, Raymond and Lettvin
1978
,Pratt
1990
, Wasser-
man
1992
, Emmers
1981
, Singer
1999
).
We have proposed neural timing nets that can separate out temporal pattern com-
ponents even if they are interleaved with other patterns. They differ from neural
networks that use spike synchronies amongst dedicated neural channels, which is a
kind of time-division multiplexing. Instead, signal types are encoded by characteris-
tic temporal patterns rather than by “which neurons were active when”. Neural tim-
ing nets can support multiplexing and demultiplexing of complex temporal pattern
signals in much more flexible ways that do not require precise regulations of neural
interconnections, synaptic efficacies, or spike arrival times (Cariani
2001a
;
2004
).
The potential importance of temporal-pattern-based multiplexing for neural net-
works is fairly obvious. If one can get beyond scalar signals (e.g. spike counts or
firing rates), then what kind of information a given spike train signal contains can
