Roving oddball paradigm elicits sensory gating, frequency sensitivity, and long-latency response in common marmosets

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.

An Electric Circuit Model of Central Auditory Processing that Replicates Low-level Features of the Mouse Mismatch Response

Neurophysiology research using animals is often necessary to further our understanding of particular areas of medical interest. Human mismatch negativity (MMN) is one such area, where animal models are used to explore underlying mechanisms more invasively and with greater precision than typically possible with human subjects. Computational models can supplement these efforts by providing abstractions that lead to new insights and drive hypotheses. This study aims to establish whether a mouse mismatch response (MMR) analogous to human MMN can be modelled using electric circuit theory. Input to the auditory cortex was modelled as a step function multiplied by a frequency-dependent weighting designed to reflect spectral hearing sensitivity. Afferent sensory responses were modelled using a resistor-capacitor (RC) network, while bidirectional (bottom-up and top-down) responses were modelled using a resistor-inductor-capacitor (RLC) network. Synthetic EEG was combined with RC and RLC circuit currents in response to simulated sequences of auditory input, which comprised duration and frequency oddball paradigms. Two different states of awareness were considered: i) anaesthetized, including only the RC circuit, and ii) conscious, including both RC and RLC circuits. Event-related potential waveforms were obtained from ten simulated experiments for each oddball paradigm and state. These were qualitatively and quantitatively compared with data from a previous in-vivo study, and the model was deemed to successfully replicate low-level features of the mouse central auditory response. Clinical Relevance - Abnormal MMN is believed to reflect pathological changes associated with psychiatric disease. Maximizing the effectiveness of this biomarker will require a greater understanding of the specific cause(s) of these abnormalities. This study presents a computational model that can account for differences between responses to duration and frequency oddball paradigms, which is particularly significant for clinical MMN research.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory‐evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time‐wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound‐level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross‐validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand‐average auditory‐evoked potential waveforms (r² > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative ‘safety’ and ‘danger’ units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.