Supplementary Information to accompany:
Transformation from temporal to rate coding in a somatosensory
thalamocortical pathway
Ehud Ahissar, Ronen Sosnik, Sebastian Haidarliu
Temporal-to-rate code transformation and the thalamocortical phase-locked
loop model.
Our findings of temporal-to-rate code transformations in the paralemniscal
system are consistent with a closed-loop operation of thalamocortical circuits that
include cortical inhibition, cortical oscillatory neurons and thalamic gating (Fig. S1).
Based on in-vitro data1,2 it had been suggested that following a quiescent period,
thalamocortical neurons shift to a hyperpolarized sensitive mode (e.g., Fig. 7C in Ref.
1) in which they faithfully relay sensory activations1. This “relay” mode is consistent
with the short-latency thalamic responses observed herein to the first stimulus cycle.
However, during subsequent stimulus cycles thalamic neurons are expected to shift
into a gating mode (e.g., Fig. 7B in Ref. 1), in which the same sensory activation is
not sufficient for activating the thalamic neurons, i.e. additional depolarization is
required (e.g., Fig. 7A in Ref. 1). Such a depolarization can be induced by the other
major input to these thalamic cells – the cortical feedback. Thus, during on-going
stimulations POm neurons, like other thalamic “relay” neurons, are assumed to
function as AND gates, i.e. they should be active only when their two major inputs,
from the brainstem and cortex, are co-active. When in this mode, the entire
thalamocortical loop depicted in Fig. S1 (right) should function as a phase-locked
loop3.
Within each stimulus cycle (Fig. S1 left, 1-4), the onset latency of the coactivation of the thalamic inputs is the onset latency of the cortical feedback within
that cycle (Fig. S1, blue rectangles), since the cortical feedback lags the brainstem
onset. In contrast, the offset latency of the co-activation is the offset latency of the
brainstem, since brainstem offset leads the offset of the cortical feedback. Previous
experimental results show that the latency of cortical oscillatory neurons, which are
assumed to drive the cortical feedback (Fig. S1, right), increases with the frequency4.
Since the offset latency of brainstem activity is constant (Figs. 1 and 2 of the paper),
the onset latency of the POm neurons is expected to increase, while the offset latency
would remain constant, just as we observed. This reduces the duration and thus also
the spike-count of the POm responses at each stimulus cycle.
Thus, according to this model, the modulation in spike counts as a function of
input frequency results from the modulation of the latency of the cortical oscillatory
neurons. The mechanism that generates this modulation is at the heart of the temporalto-rate code transformation performed by the phase-locked loop. The onset time of the
cortical feedback, driven by the oscillatory neurons, can be viewed as an expectation
with respect to the onset time of the current vibrissal input. This expectation is based
on previous input times (see below) and is not affected by the current input. Thus, if
the input frequency suddenly increases (Fig. S1, T1 to T2), the delay between the
now-early input and the cortical feedback also increases. The POm latency will
therefore increase and the spike-count will decrease. The reduced POm output will
induce less cortical inhibition (Fig. S1), which should result in an increased frequency
of the cortical oscillatory neurons4,5. This re-tunes the cortical oscillators to the new
input frequency, and updates the temporal expectation for the next cycle. Thus,
transient increments in frequency induce reduced spike counts and increased latencies
along the loop. The same dependencies are expected during steady-states3; in order to
maintain a higher frequency during steady-states, the level of inhibition should be
lower, and thus, the latency should be longer (to produce a weaker thalamic output,
Fig. S1, bottom), just as observed herein (Fig. 2 of the paper).
Thus, the simple circuit depicted in Figure S1 can provide the essentials of a
thalamocortical mechanism
for temporal decoding (or recoding6). However, real
thalamocortical circuits contain more than that. Some additional thalamocortical
mechanisms may, and probably do, contribute to the efficiency of the temporal
decoding process. For example, non-lemniscal thalamocortical circuits display
“augmenting responses,” in which the response to the second stimulus pulse is larger
than the response to the first pulse7 (see, responses in the POm and layer 5a in Figure
1 of the paper). Following stimulus (or whisking) onset, the mechanism underlying
the augmenting response8 probably forces the thalamocortical loop into a
predetermined stabilization process. This should be particularly efficient if the
frequency of the cortical oscillations is set, by some preperatory signal, to the
expected whisking frequency prior to the initiation of whisker movement (e.g., Ref.
9).
References
1.
Sherman, S. M. & Guillery, R. W. Functional organization of thalamocortical
relays. J.Neurophysiol. 76, 1367-1395 (1996).
2.
McCormick, D. A. & von Krosigk, M. Corticothalamic activation modulates
thalamic
firing
through
glutamate
"metabotropic"
receptors.
Proc.Natl.Acad.Sci.U.S.A 89, 2774-2778 (1992).
3.
Ahissar, E. Temporal-code to rate-code conversion by neuronal phase-locked
loops. Neural Computation 10 (3), 597-650 (1998).
4.
Ahissar, E., Haidarliu, S. & Zacksenhouse, M. Decoding temporally encoded
sensory input by cortical oscillations and thalamic phase comparators.
Proc.Natl.Acad.Sci.USA 94, 11633-11638 (1997).
5.
Silva, L. R., Amitai, Y. & Connors, B. W. Intrinsic oscillations of neocortex
generated by layer 5 pyramidal neurons. Science 251, 432-435 (1991).
6.
Perkel, D. H. & Bullock, T. H. Neural coding. Neuroscience Research Program
Bulletin 6, 221-248 (1968).
7.
Morison, R. S. & Dempsey, E. W. A study of thalamocortical relations. American
Journal of Physiology 135, 281-292 (1942).
8.
Castro-Alamancos, M. A. Short-term plasticity in thalamocortical pathways:
cellular mechanisms and functional roles. Rev Neurosci 8, 95-116 (1997).
9.
Nicolelis, M. A. L., Baccala, L. A., Lin, R. C. S. & Chapin, J. K. Sensorimotor
Encoding by Synchronous Neural Ensemble Activity at Multiple Levels of the
Somatosensory System. Science 268, 1353-1358 (1995).