Why the neural ingredients for a language of thought are not like spatial cells (commentary on Kazanina & Poeppel, 2023)

No abstract available

Uncovering neural patterns of cognition by aligning oscillatory dynamics

A primary aim of cognitive neuroscience is to explain how cognition is physically realized by the brain. Toward this end, neuroscientists study consistent activity patterns produced across ensembles of neurons. Importantly, such ensembles are subject to excitability fluctuations imposed by neural oscillations, which are used in a self-organized way to realize windows of effective communication, coding schemes, a switch between memory processes, interareal information exchange, and other functions. Given the intimate link between oscillations and neural processing, this thesis explores the power of studying activity patterns of cognition with reference to brain dynamics. Specifically, I submit that spectral information like phase and oscillatory cycles offer a brain-intrinsic coordinate system by which the readout of neurocognitive patterns can be assisted. From this vantage point, I explore two methodological advances: (1) brain time warping, which incorporates oscillatory dynamics post-hoc after brain data has been acquired, and (2) visual perturbation or “pings”, which artificially regularize oscillations as memory retrieval is ongoing. We demonstrate that brain time warping can reveal activity patterns otherwise left undetected, and we introduce a comprehensive toolbox to apply the algorithm and test its effects. On the other hand, we found no evidence that pings enhance the readout of memory representations from electroencephalography data. Together, these empirical and theoretical points underscore the need for a neurally inspired methodology in which scientists are cast as spectators with privileged access to external world variables

Sustained neural rhythms reveal endogenous oscillations supporting speech perception.

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or "entrained") to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research-including neural entrainment and tACS-and reveal endogenous neural oscillations as a key underlying principle for speech perception

Phase separation of competing memories along the human hippocampal theta rhythm

Competition between overlapping memories is considered one of the major causes of forgetting, and it is still unknown how the human brain resolves such mnemonic conflict. In the present magnetoencephalography (MEG) study, we empirically tested a computational model that leverages an oscillating inhibition algorithm to minimise overlap between memories. We used a proactive interference task, where a reminder word could be associated with either a single image (non-competitive condition) or two competing images, and participants were asked to always recall the most recently learned word–image association. Time-resolved pattern classifiers were trained to detect the reactivated content of target and competitor memories from MEG sensor patterns, and the timing of these neural reactivations was analysed relative to the phase of the dominant hippocampal 3 Hz theta oscillation. In line with our pre-registered hypotheses, target and competitor reactivations locked to different phases of the hippocampal theta rhythm after several repeated recalls. Participants who behaviourally experienced lower levels of interference also showed larger phase separation between the two overlapping memories. The findings provide evidence that the temporal segregation of memories, orchestrated by slow oscillations, plays a functional role in resolving mnemonic competition by separating and prioritising relevant memories under conditions of high interference

Oscillation or not – why we can and need to know (commentary on Doelling and Assaneo, 2021)

International audienceNeural oscillations are pivotal to brain function and cognition, but they can be difficult to identify. Researchers engage in careful experimentation to identify their presence, ruling out non-oscillatory processes that could give rise to a similar response. Recently, Doelling and Assaneo have argued against these efforts on the basis that oscillators are heterogeneous, which makes the line to non-oscillators blurred and thereby meaningless to draw. Here, we offer our opposing viewpoint, arguing that we can know whether oscillations are involved, and that we need to know. First, we can know because there are unique properties that only oscillators have, which can be reliably used to identify them – the line is not blurred. These unique properties include eigenfrequency, Arnold Tongue, convergence, and independence. Second, we need to know because there are shared properties, which all oscillators or all oscillators within a subclass have. These shared properties comprise all the information we get once we know there is an oscillator, including neurophysiological, functional, and methodological properties – the fruits of decades of research. We argue that identifying oscillators is crucial for the advancement of research fields as it constrains the possible neural dynamics involved and allows us to make informed predictions on a variety of levels. While neural oscillations are the start and not the end, we have to reach that start

Decoupling Measurements and Processes: On the Epiphenomenon Debate Surrounding Brain Oscillations in Field Potentials

Various theories in neuroscience maintain that brain oscillations have an important role in neuronal computation, but opposing views claim that these macroscale dynamics are “exhaust fumes” of more relevant processes. Here, we argue that the question of whether oscillations are epiphenomenal is ill-defined and cannot be productively resolved without further refinement. Toward that end, we outline a conceptual framework that clarifies the dispute along two axes: first, we introduce a distinction between measurement and process to categorize the theoretical status of electrophysiology terms such as local field potentials and oscillations. Second, we consider the relationships between these disambiguated terms, evaluating based on experimental and computational evidence whether there exist causal or inferentially useful links between them. This decomposes the question of epiphenomenalism into a set of empirically tractable alternatives. Finally, we demarcate oscillations as a conceptually distinct entity where either processes or measurements exhibit periodic behavior, and we suggest that oscillatory processes orchestrate neural computation by implementing a temporal, spatial, and frequency syntax. Overall, our reframed evaluation supports the view that electric fields—oscillating or not—are causally relevant, and that their associated signals are informative. More broadly, we offer a vocabulary and starting point for scientific exchanges on the role and utility of brain signals and the biological processes they capture