Supplementary Information

Ross S. Williamson1,2,7, Misha B. Ahrens3,4, Jennifer F. Linden5,6, and Maneesh Sahani1

1Gatsby Computational Neuroscience Unit, University College London2Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University

College London3Department of Molecular and Cellular Biology, Harvard University

4Computational and Biological Learning Lab, University of Cambridge5Ear Institute, University College London

6Department of Neuroscience, Physiology and Pharmacology, University College London7Current Affiliation: Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary & Centre for Computational

Neuroscience and Neural Technology, Boston University

February 5, 2014

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1 Supplementary Material

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1.1 Uniformity of Contextual Interactions

In equation (??) the CGF is invariant with respect to different time-frequency positions within the PRF. Specifically,contextual effects are equal at all frequencies and time lags, i.e. a single CGF operates over the entire domainof the PRF. To test this assumption, we augmented the context model, to allow contextual effects for differentcombinations of frequency and time-lag to be different.

Equation (??) is augmented as follows

r̂(i) = c1 +∑{j,k}∈ρ1

wtfjks(i− j + 1, k)

(1 +

∑mn

w τφ1 mns(i− j + 1−m, k + n)

)+

∑{j,k}∈ρ2

wtfjks(i− j + 1, k)

(1 +

∑mn

w τφ2 mns(i− j + 1−m, k + n)

)+

...∑{j,k}∈ρs

wtfjks(i− j + 1, k)

(1 +

∑mn

w τφS mns(i− j + 1−m, k + n)

)(1)

where ρ1 · · · ρs are sets that contain different time-frequency ({j, k}) pairs of interest.Here, the original stimulus-response function has been split into a sum of S terms, where S dictates the number

of CGFs present within the model. We chose to examine a number of simpler split models (to avoid the problemof overfitting; incorrect “explanation” of noise), where we segregated the PRF into two divisions, and allowed aCGF to be associated with each of them. Even though such a split is a clearly a large distance away (in modellingterms) from associating a separate CGF with every time-frequency element, this model still allows us to test specifichypotheses about whether the CGFs might differ when associated with different parts of the PRF. Importantlyhowever, the parameter count becomes far more manageable.

We fit a variety of different two-CGF context models to both our cortical and thalamic populations of data. Forthe vast majority of the models that we fit, we noticed a remarkable similarity in the structure present betweenpairs of CGFs. To illustrate this, figure 1(a) and 1(b) show cortical population averages of two two-CGF models(thalamic results were qualitatively similar, not shown). The first of these (in 1(a)) is a model where one CGF (toprow) has been applied to purely the excitatory portion of the underlying PRF, while the second CGF acts upon theentire range of the PRF. In this example, aside from a change in magnitude, the structure seems to be qualitativelysimilar, with this population average exhibiting a large delayed suppressive region, and two noticeable regions ofnear-simultaneous (τ = 0) enhancement. This qualitative similarity also seems to persist in another split that wastried, shown in 1(b). Here, the top CGF was applied only to the low-frequency half of the PRF, and the bottomCGF only to the high-frequency half. Again, both a delayed suppressive region, and a region of near-simultaneousenhancement can be observed.

We also directly tested whether contextual effects that act upon the excitatory and inhibitory components of thePRF are similar. This was one of the only splits that we tried in which the similarity was not as obvious (illustratedin figure 1(c)). There is a delayed suppressive region present in both CGFs for example, although the timescaleis somewhat different in the CGF fit to the inhibitory part of the PRF (bottom row). The near-simultaneousenhancement clearly observable in the excitatory CGF (top row) is also not clearly present within the inhibitoryCGF. Further analyses showed that these differences were only present at the population level, and there were asignificant proportion of individual cells that showed similarity (supplementary figure 2).

Having established a certain amount of similarity in the structure of the CGF pairs, we were curious to establishhow predictive these models were, especially when compared to a single CGF model, with its assumption ofcontextual uniformity.

To investigate this, we replicated the analysis of ? (also ?) and extrapolated cross-validated (test-set) predictivepowers and training-set predictive powers to zero noise power (see methods).These predictive powers can be treatedas lower and upper bounds on what can possibly be achieved by each model (for details, see ?).

From the perspective of overfitting, as the number of parameters increases in a statistical model, the predictivepower on the training set alone should increase (due to a reduction in training error attributable to the parameterincrease). This is certainly the case here, as the extrapolated training-set predictive power is at its lowest for

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−1

0

1

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−0.05

0

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0.1

τ(ms)

φ(o

cta

ves)

240 160 80 0−1

0

1

(a) Excitatory/Backgroundsplit.

−1

0

1

−0.2

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0

0.1

0.2

τ(ms)

φ(o

cta

ves)

240 160 80 0−1

0

1

(b) Low/high frequency split.

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1

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τ(ms)

φ(o

cta

ves)

240 160 80 0−1

0

1

(c) Excitatory/inhibitory split.

−0.2 0 0.2 0.4 0.6−0.2

0

0.2

0.4

0.6

0.8

Single−CGF Predictive Power

Tw

o−

CG

F P

redic

tive P

ow

er

EI Split

EB Split

LH Split

(d) Predictive power comparison for the split models.

0.1

0.5

0.9

Extr

ap

ola

ted

no

rma

lise

dp

red

ictive

po

we

r

STRF

One−CGF

Exc/Inh Exc/BG Lo/Hi

(e) Extrapolated normalised predictive power ranges.

Figure 1: Two-CGF cortical model analysis. This figure illustrates the qualitative similarity between the popula-tion averages of some two-CGF model fits.(a). A model where CGF1 (top) acts upon the excitatory portion of the PRF and CGF2 (bottom) acts on the entirePRF.(b). A model where CGF1 (top) acts upon the low-frequency half of the PRF and CGF2 (bottom) acts upon thehigh-frequency half. Notice that in both (a) and (b), the structure between CGFs is qualitatively similar (up to achange in magnitude, noticeable due to the same scale being used on both pairs).(c). A model where CGF1 (top) acts on the excitatory part of the PRF, and CGF2 (bottom) acts on the inhibitorypart. These population averages share a similar suppressive region (with a temporal difference), but the enhance-ment present at τ = 0 in CGF1 is not present in CGF2.(d). A direct comparison of the cross-validated predictive powers achieved through fitting a single-CGF contextmodel, or a two-CGF context model. Colours denote the different splits. Red: Excitatory/inhibitory. Green:Excitatory/background. Blue: Low frequency/high frequency. For a large number of cells within the population,the predictive power lies either on or below the y = x line, indicating that the increase in complexity provided byan additional CGF does not improve the quality of the model fit.(e). The upper bounds here are provided by extrapolation of the training-set predictive powers, whilst the lowerbounds are provided by extrapolation of the cross-validated (test-set) predictive powers. The upper bounds increaseas a function of parameter count, as expected. The lower bounds for the four context models are greater than thatof an STRF model, indicating that they all provide an increase in quality over a simple linear fit. The lower boundsof all three multiple-CGF models are lower than that of the single-CGF model, further emphasising the point thatan additional CGF does not provide an increase in model quality.

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A1 AAFSTRF Peak Latency (ms) 43.85± 1.03 24.88± 0.50STRF Receptive Field Duration (ms) 159.49± 2.94 125.12± 2.10STRF Excitatory Subfield Width (ms) 51.28± 1.20 36.74± 1.35STRF Inhibitory Subfield Width (ms) 94.87± 1.64 82.79± 0.92STRF Excitatory Bandwidth (kHz) 20.92± 0.47 17.18± 0.74STRF Inhibitory Bandwidth (kHz) 16.90± 0.36 15.25± 0.46STRF Normalised Excitatory Bandwidth 1.19± 0.03 0.92± 0.02STRF Normalised Inhibitory Bandwidth 1.02± 0.02 0.91± 0.02PRF Peak Latency (ms) 40.77± 0.77 29.07± 0.91PRF Receptive Field Duration (ms) 163.08± 3.28 146.98± 2.28PRF Excitatory Subfield Width (ms) 50.77± 1.07 39.53± 1.24PRF Inhibitory Subfield Width (ms) 105.64± 2.24 100.47± 1.43PRF Excitatory Bandwidth (kHz) 19.75± 0.40 16.38± 0.65PRF Inhibitory Bandwidth (kHz) 15.38± 0.45 15.11± 0.47PRF Normalised Excitatory Bandwidth 1.18± 0.03 0.91± 0.02PRF Normalised Inhibitory Bandwidth 0.95± 0.03 0.91± 0.02

Table 1: Spectral/temporal profile differences between A1 and AAF.

an STRF model, and gradually increases as one, and then two, CGFs are added. Perhaps unsurprisingly, theextrapolated training-set predictive powers are almost identical for all three two-CGF models (not shown). Eventhough the models are specified differently, they all contain an identical number of parameters.

Conversely, cross-validated predictive power should only increase with the number of model parameters, if theparameters are actually useful (since cross-validation can be thought of as a measure of how well the model iscapable of generalising). Thus, both single, and two, CGF context models provide a (perhaps subtle) increase overa simple STRF model. Of particular interest however, is the fact that the extrapolated cross-validated predictivepower actually goes down very slightly when a second CGF is added (in all three cases). This indicates that thesignificant increase in complexity does not add much to the quality of the overall model fit. This is probably due,in large part, to the amount of similarity between both CGFs in this split formulation of the context model. Figure1(d) serves to emphasise this point by directly plotting the cross-validated predictive powers of the different contextmodels against one another. For all three split models, a large number of the points lie either on or beneath they = x dotted line, indicating that, for most cells, the addition of a second CGF does not provide an increase inpredictive capability.

Ultimately, these analyses have all been carried out in order to determine whether or not the assumption ofcontextual uniformity within the single CGF context model is a valid one. Can we use a model where we have theassumption that contextual interactions are identical over multiple frequencies and time-lags? For the most part,this certainly seems to be the case, and this particular structural limitation on second-order interactions is a validmodelling assumption to make. As a result of this, all of the following analyses will focus on the single-CGF model,as denoted by equation (??).

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−1 −0.5 0 0.5 1

Uncentered Correlation Coefficient

Random Pairs

True Pairs

(a) Distributions of uncenteredcorrelation coefficients.

0 0.5 1−1

−0.5

0

0.5

1

Predictive Power

Un

cente

red C

orr

ela

tion

Coeff

icie

nt

(b) Predictive power trend.

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τ(ms)

φ(o

cta

ves)

240 160 80 0−1

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1

τ(ms)

240 160 80 0

τ(ms)

240 160 80 0

(c) CGF pairs with positive predictive powers.

τ(ms)

240 160 80 0

τ(ms)

240 160 80 0

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τ(ms)

240 160 80 0

(d) CGF pairs with low predictive powers.

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φ(octaves)

240 160 80 0

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τ(ms)

Co

nte

xt

Str

en

gth

Exc

Inh

−1 0 1φ(octaves)

240 160 80 0τ(ms)

−1 0 1φ(octaves)

240 160 80 0τ(ms)

−1 0 1φ(octaves)

240 160 80 0τ(ms)

−1 0 1φ(octaves)

240 160 80 0τ(ms)

−1 0 1φ(octaves)

240 160 80 0τ(ms)

(e) Temporal/spectral slices of the above CGF pairs.

Figure 2: Excitatory/inhibitory split model - single cell examples.(a). Distributions of correlation coefficients indicating the level of similarity between two CGFs chosen at randomfrom the population (in dotted black), and the level of similarity between two CGFs belonging to a single cell (inblack). Clearly, the black distribution is skewed in the positive direction indicating that CGF pairs from the samecells are likely to be highly correlated, more so than a random pair from the population.(b). CGFs are more likely to be similar if the predictive power is high.(c). Three CGF pairs for model fits yielding high predictive powers (0.69, 0.40, 0.47, from left-right).(d). Three CGF pairs for model fits yielding low predictive powers (0.0005, 0.0199, 0.0153, from left-right).(e). Characteristic structure extracted from the CGFs located above. Top row: A spectral strip at τ = 0, for all φ,the near-simultaneous region corresponding to a time-lag of 0-20 ms. Bottom row: A temporal strip at φ = 0, forall τ . Notice that these features are remarkably consistent within the high predictive power pairs, but significantlyless so in the negative predictive power pairs.

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LGN

vMGB

mMGB

Electrode Tracks

dMGB

Figure 3: An example of thalamic histology. This representative 50 µm slice shows a cytochrome oxidase stainedMGB, with the relevant subdivisions clearly marked. In addition to the auditory part of the thalamus, the lateralgeniculate nucleus (LGN), part of the visual thalamus, can also be observed. At the top of the slice, damage leftby the electrode array as it was pushed through cortex can be observed.

vMGB mMGB dMGBSTRF Peak Latency (ms) 13.14± 0.29 14.71± 0.51 15.45± 1.70STRF Receptive Field Duration (ms) 129.41± 2.37 114.12± 3.19 110.91± 4.98STRF Excitatory Subfield Width (ms) 34.51± 0.74 35.29± 0.96 23.64± 2.19STRF Inhibitory Subfield Width (ms) 81.57± 1.12 73.53± 1.58 69.09± 2.98STRF Excitatory Bandwidth (kHz) 12.84± 0.42 11.27± 0.57 5.96± 0.78STRF Inhibitory Bandwidth (kHz) 13.85± 0.33 12.31± 0.45 11.03± 0.36STRF Normalised Excitatory Bandwidth 1.17± 0.05 0.78± 0.04 0.41± 0.05STRF Normalised Inhibitory Bandwidth 1.09± 0.02 0.89± 0.03 0.83± 0.05PRF Peak Latency (ms) 13.14± 0.29 14.71± 0.51 15.45± 1.70PRF Receptive Field Duration (ms) 140.78± 2.37 125.29± 3.70 156.36± 11.13PRF Excitatory Subfield Width (ms) 33.73± 0.53 37.65± 1.29 23.64± 2.19PRF Inhibitory Subfield Width (ms) 84.71± 1.43 76.47± 1.89 89.09± 3.40PRF Excitatory Bandwidth (kHz) 13.35± 0.41 12.41± 0.56 5.96± 0.78PRF Inhibitory Bandwidth (kHz) 14.72± 0.32 13.31± 0.42 15.37± 1.04PRF Normalised Excitatory Bandwidth 1.22± 0.04 0.83± 0.04 0.41± 0.05PRF Normalised Inhibitory Bandwidth 1.23± 0.03 0.93± 0.03 1.18± 0.10

Table 2: Spectral/temporal profile differences between thalamic subdivsions.

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Figure 4: Receptive field statistics (adapted from ?). The peak latency was the time to the center of the peakin the first subfield of the receptive field (this was usually an excitatory subfield, but occasionally an inhibitorysubfield). Receptive field duration was defined to be the time from the beginning of the first subfield to the endof the last subfield. Excitatory subfield width was defined to be the width at half-maximum of the positive peakin the temporal profile of the receptive field, whilst the inhibitory subfield width was defined to be the width athalf-minimum of the negative peak in the temporal profile. The excitatory and inhibitory bandwidth was definedto be the width at half-maximum (or half-minimum) of the positive (or negative) peak in the spectral profile. Thenormalised bandwidth was achieved by normalising either excitatory or inhibitory bandwidths by the best frequencyof the receptive field (defined to be the frequency corresponding to the maximum in the spectral profile).

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