# Hierarchical Predictive Information Is Channeled by Asymmetric Oscillatory Activity

Anne-Lise Giraud, Luc H Arnal

► To cite this version:

Anne-Lise Giraud, Luc H Arnal. Hierarchical Predictive Information Is Channeled by Asymmetric Oscillatory Activity. Neuron, 2018, 100 (5), pp.1022-1024. 10.1016/j.neuron.2018.11.020. hal-03994236

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# Hierarchical Predictive Information Is Channeled by Asymmetric Oscillatory Activity

Anne-Lise Giraud<fnref n="1" />,* and Luc H. Arnal<fnref n="1" />

Predictive coding and neural oscillations are two descriptive levels of brain functioning whose overlap is not yet understood. Chao et al. (2018) now show that hierarchical predictive coding is instantiated by asymmetric information channeling in the $\gamma$ and $\alpha/\beta$ oscillatory ranges.

Predictive coding is a prolific general theory of the brain assuming that any cerebral process, whatever its scale and purpose, tends to prevent surprise—or, in thermodynamic terms, entropy. The theory entails that the sources of sensory changes are neutralized by predicting their likely causes and that eventually only what cannot be accounted for by anticipation is processed further. In this view, the coincidental effect of prediction and external input *is* the neural representation. Along Marr's classical model (Marr and Poggio, 1976), this level of brain description could be argued to be computational-to-algorithmic (Figure 1A), as it depicts a general neural principle, disregarding its scale or specific physical instantiation. Although connections have been made to the neurotransmitter level in brain pathologies (e.g., Sterzer et al., 2018), predictive coding still requires concrete overlap with the algorithmic-to-implementational level, i.e., how predictions and prediction errors are (bio)physically generated and deployed, notably in the temporal domain (Friston et al., 2018).

By contrast, many if not all cortical operations involve neural oscillations, electrical phenomena that capture composite neural activity at a spatio-temporal integration scale that can easily be related to complex cognitive processes. The generation of neural oscillations is fairly well understood at the biophysical level: it follows from rhythmic neuronal membrane potential fluctuations, extending to basic and complex neural networks through excitatory-excitatory, inhibitory-inhibitory, and excitatory-inhibitory synaptic connections as well as through ion-mediated coupling. Different families of neural oscillations involving different frequency ranges

are associated with different cognitive functions, and the neural architecture underlying their coordinated action, i.e., their nesting, accounts for specific functions (Hyafil et al., 2015), including the maintenance of processing hierarchy and its deployment in the time domain. This function is particularly important in fields of cognition requiring temporal integration of sensory stimuli and on-line action planning, such as language and spatial navigation. In language, neural oscillations at different scales, and their nesting, could be at the heart of a stream of critical interdependent operations, including sequence timing signaling and prediction, chunking and grouping, maintenance of order within sequences, detection of pattern violations, and encoding of hierarchies (Chao et al., 2018; Dehaene et al., 2015; Ding et al., 2015; Hyafil et al., 2015).

Distinct ranges of neural oscillations have also been implicated in general principles of interregional information propagation. In particular, a relatively recent stream of converging findings indicates that feedforward and feedback information is associated, respectively, with $\gamma$- and low-$\beta$-range oscillations (Michalareas et al., 2016). This asymmetry could reflect the independence of information channeling for distinct types of information, namely predictions and prediction errors, and could constitute an interesting connecting point between the theory of predictive coding and the way it is instantiated at the neuronal population level. Interestingly, Bastos and colleagues (Bastos et al., 2012) even argued that frequency asymmetry between up- and down-going format of information flow directly ensues from the predictive coding mathematical formalism. This intriguing observation

opens a whole new field of research at the interface of two distinct description levels of dynamic sensory processing and action planning (Figure 1A, blue area).

In this issue of Neuron, Chao et al. (Chao et al., 2018) elegantly connect these two levels of description by using a now-classical experimental paradigm, the local-global oddball paradigm, that allows probing of hierarchical predictive coding by violating expectations simultaneously at two distinct processing levels and timescales. At a low, local level, expectation violations result from the perception of an oddball sound deviating from a sequence of standard sounds. Local sequences, whether normal or oddball, are repetitively presented to form 2^nd^-order sequences (of sequences), thereby generating expectations at a higher-order global level. Importantly, in their experimental design the authors ensured that standard and deviant local sequences were equally presented within global sequences in a balanced manner. This clever paradigmatic trick enables separation of local from global effects, all sensory inputs being equal, in the factorial analysis design. Leveraging the intrinsically nested organization between these two levels of expectations, the authors could derive specific hypotheses about the amount and location of predictions and prediction errors at distinct anatomical and functional levels (Figure 1B). Importantly, while previous attempts at singling out predictions and errors focused on a unique hierarchical level, the local-global approach measures predictive processing at one level along with the conditional propagation of prediction errors to the next, depending on the predictive context. This aspect is crucial, as it allows exploration of an

<footnote n="1">¹Department of Basic Neurosciences, University of Geneva, Biotech Campus, 9 chemin des Mine 1211 Geneva, Switzerland</footnote>

<footnote>*Correspondence: anne-lise.giraud@unige.ch</footnote>

<footnote>https://doi.org/10.1016/j.neuron.2018.11.020</footnote>
A

Tri-level model

<figure><img src="image_2.png" /></figure>

OSI-like model

<figure><img src="image_3.png" /></figure>

B

<figure><img src="image_4.png" /><figcaption>Figure 1. Predictive Coding, Neural Oscillations, and Hierarchical Processing Depth</figcaption></figure>

(A) Predictive coding and neural oscillations are complementary descriptive tools of neural functioning that, according to the tri-level hypothesis, focus on the computational-to-algorithmic and algorithmic-to-implementational levels, respectively. To unpack these three levels, we use an analogy to the Open Systems Interconnection (OSI) model, which was developed to standardize communication functions across computing systems. The seven postulated layers (from hard, physical [green] to soft, computational [pink] layers) detail the different levels at which neural oscillations could intervene in neural information transmission. An overlap between predictive coding and neural oscillation descriptive levels is in the blue-purple domain (layers 4/5 from bottom). This is the domain that is targeted by the experimental design of Chao et al. (B) The experimental design presents simple auditory stimuli in a local or global sequential context. Response to the last stimulus of a sequence elicits either a local validation of the prediction (if similar to immediately preceding stimuli, xx(X)) or a local prediction error (if different, xx(Y)). If the local sequence is embedded in a global sequence, the last stimulus of a sequence elicits either a global validation of the prediction (if similar to the immediately preceding sequences, xxx - xxX or xxy - xx(Y)) or a global prediction error (if different from the preceding sequences, xxx - xxY or xxy - xx(X)). Using large-scale and dense ECoG brain surface coverage in monkeys, three levels of processing could be identified: level 1 (green area) corresponds to the auditory cortex where local prediction error is generated, level 2 (yellow area) is the anterior temporal cortex where local prediction and global prediction error are produced, and level 3 (brown area) is the prefrontal cortex where global prediction is generated, as well as a hypothetical up-going prediction error to Level N (not characterized here). By dissociating the level at which predictions and prediction errors are generated, the design allows relation of each process to a specific type of neural activity, respectively, β and γ oscillations (full and dotted arrows). Thick dotted arrows represent enhanced prediction error due to expectation violations. *CTC, communication-through-coherence.

essential feature of predictive coding, the
notion of hierarchical depth.

By using large-coverage ECoG record-
ings in two monkeys, Chao et al. managed
to obtain sufficient spatio-temporal reso-
lution to address large- and small-scale
network dynamics of prediction and
prediction errors and to disentangle

feedforward from feedback information
flows. Using a careful incremental analyt-
ical approach, they could experimentally
assemble in one single study many of the
pieces of a pre-existing puzzle. First,
using a spectrally resolved factorial anal-
ysis, they identified a series of anatom-
ical and functional patterns related to

each novelty response. They delineated
two distinct and remarkably consistent
anatomical and functional response pat-
terns in the γ oscillation range. While the
perception of local deviants prompted
an early γ-band response in auditory cor-
tex, global violations induced a distinct,
delayed, γ-band increase in the anterior
temporal cortex. Interestingly, the latter
pattern of prediction error responses
was accompanied by concurrent changes
of neural responses in the β range. Using
an original analytic approach to reduce
the dimensionality of the data, the authors
further circumscribed anatomical and
functional clusters of electrodes consti-
tuting the putative hierarchical nodes
involved in the computation of prediction
errors and update. Finally, using directed
connectivity in the time-frequency domain,
they formally established that prediction
errors and prediction updates propagate
in a feedforward and feedback manner
in the γ and β ranges, respectively.

All together, these findings intimate a coherent view of previous experimental observations, validating on the one hand the notion of hierarchical predictive coding across auditory levels, and confirming on the other hand that β-γ-multiplexed models constitute a plausible biological instantiation of information channeling in the predictive brain. In particular, the backward propagation of β signals following the generation of prediction errors provides a clear-cut demonstration that such *postdictive* signals index the updating of the internal model, when expectations are invalidated by incoming sensory inputs (Arnal et al., 2011). Chao et al.'s article also opens up novel theoretical problematics regarding the depth of hierarchical predictive processing in the monkey and human brain. Is cognition limited in its capacity to extract N-order regularities and generate a set of on-line hypothetic rules to account for past events (Figure 1B)? And are neural oscillations, their nesting properties, and the asymmetric information channeling principle adequate (but also potentially limiting) implementational tools for representing and deploying hierarchical depth?

REFERENCES

Arnal, L.H., Wyart, V., and Giraud, A.L. (2011).
*Transitions in neural oscillations reflect prediction*
errors generated in audiovisual speech. Nat. Neurosci. 14, 797-801.

Bastos, A.M., Usrey, W.M., Adams, R.A., Mangun, G.R., Fries, P., and Friston, K.J. (2012). Canonical microcircuits for predictive coding. Neuron 76, 695-711.

Chao, Z.C., Takaura, K., Wang, L., Fujii, N., and Dehaene, S. (2018). Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain. Neuron 100, this issue, 1252-1266.

Dehaene, S., Meyniel, F., Waongne, C., Wang, L., and Pallier, C. (2015). The Neural Representation of Sequences: From Transition Probabilities to Alge-

braic Patterns and Linguistic Trees. Neuron 88, 2-19.

Ding, N., Melloni, L., Zhang, H., Tian, X., and Poeppel, D. (2015). Cortical tracking of hierarchical linguistic structures in connected speech. Nat. Neurosci. 19, 158-164.

Friston, K.J., Rosch, R., Parr, T., Price, C., and Bowman, H. (2018). Deep temporal models and active inference. Neurosci. Biobehav. Rev. 90, 486-501.

Hyafil, A., Giraud, A.L., Fontolan, L., and Gutkin, B. (2015). Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. Trends Neurosci. 38, 725-740.

Marr, D., and Poggio, T. (1976). From Understanding Computation to Understanding Neural Circuitry. AI Memos (1959-2004). http://hdl.handle.net/1721.1/5782.

Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., and Fries, P. (2016). Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas. Neuron 89, 384-397.

Sterzer, P., Adams, R.A., Fletcher, P., Frith, C., Lawrie, S.M., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., and Corlett, P.R. (2018). The Predictive Coding Account of Psychosis. Biol. Psychiatry 84, 634-643.