Chien, V.

Knösche, T.

Maess, B.

MPG.PuRe

Introduction Methods• Deviance detection is a pre-attentive mechanismthat causes extra neural activities in response tounexcpected temporal stimulus patterns.• Deviance-related responses include:1. On and Off responses– elicited by the onsets and offsets of stimuli2. Omitted-stimulus response (OSR)– elicited by an unexpected omitted stimulus3. Mismatch negativity (MMN)– elicited by an infrequent stimulus (or pattern)among regular ones• Aim: a unifying view of cortical deviance detection.Network modelCortical columns• Each column consists of an excitatory (E) and aninhibitory (I) population.• The intra-column connections are fixed.• Constant background current is fed to the Epopulation.Inter-column connections• The inter-column connections W (free parameters)give rise to various behaviors of the changedetectors.Inputs• The inputs (in firing rate) represent the intensities ofstimuli (or features).• The inputs are fed to the E populations (weight=1)and the I populations (weight=0.5).Deviance Detection: On and Off Responses, Omission Response, and Mismatch NegativityShih-Cheng Chien, Burkhard Maess, Thomas R. KnöscheMax Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germanyvchien@cbs.mpg.deResultsHypothesis 2. Features (summed activities of the whitenodes) and change detectors (gray nodes) coexist in theperceptual hierarchy. The change detectors can contributeto other features as well (links not shown here for clarity).Hypothesis 1. The stimuli (e.g. prolonged or periodic) leadto steady feature representations (i.e., regularities). Thedeviance-related responses are the activities of changedetectors.DiscussionMMN Confernce, 2018Summary• The cortical deviance-related responses (e.g. On/Offresponses, OSR, and MMN) can be generated by thesame mechanism, where (1) The regularities ofstimuli are represented by feature nodes in ahierarchical fashion, (2) The neighboring nodes arepotential change detectors that detect suddenchanges of the regularities, and (3) The reciprocalconnections give rise to various types of behaviors ofthe change detectors.Prediction• The NMDA-r antagonists dampen the excitatory-to-excitatory (Wee) and excitatory-to-inhibitory (Wei)connections, and therefore influence the profiles ofall cortical deviance-related responses (not justMMN).2Hypothesis 3.A group ofexcitatory andinhibitory neurons(e.g., a corticalcolumn)  canpotentially serve asa change detectorto its neighboringfeature neurons viareciprocalconnections.3Fig. 4. (A)The OSR peak latency is a constant delay plus theSOA. (B)The feature columns have different time constants τi sothat the periodicity (i.e. SOA of stimuli)  is kept in sustainedresonance. The change detector responds to a sudden changein periodicity. (C) Simulated temporal responses. (D) SimulatedOSR latencies show temporal expectancy.4Fig. 5. (A) MEG responses to theemergence and violation of a regularpattern [2]. (B) Columns 1 & 2 receiveprolonged inputs that represent objectidentities of random (RAND) andregular (REG) sequences.(C)Simulatedresponses mimic the experimentalobservations in (A).51Fig. 1. Connectionsettings. The intra-column connections(black) are fixedthroughout thesimulations. The inter-column connections W(Green) are theparameters of interest.Simulations1. Temporal profiles of On/Off responses (Fig. 2)2. Onset- and offset-frequency receptive fields (Fig. 3)3. Omitted-stimulus response (Fig. 4)4. Mismatch negativity (Fig. 5)Fig. 3. (A) The onset- and offset-FRFs ofexamplary auditory neurons in [1]. (B)The continuum of tonal stimuli in the y-axis of FRFs is modeled ascombinations of two pure tones withdifferent proportions for simplicity. (C)The simulated FRFs are generated bychoosing proper W and input ratios.Fig. 2. (A)The prolonged tonal inputis fed to column 1. By varying the W (8parameters), various types of On/Offresponses can be observed in column2 as shown in (B). (C)  The possibleconnection Ws that give rise to theeight On/Off types are projected to2D for visualization (Matlab function:tsne). Neighboring dots have similarW settings. Temporal behavior of thechange detector is sensitive to W.References[1] Qin, L., Chimoto, S., Sakai, M., Wang, J., Sato, Y. (2007) Comparison Between Offset and Onset Responses of Primary Auditory Cortex ON-OFFNeurons in Awake Cats. Journal of Neurophysiology, 97:3421.[2] Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016). Brain responses in humans reveal ideal observer-like sensitivity tocomplex acoustic patterns. Proceedings of the National Academy of Sciences, 113(5), E616-E625.Hypotheses(B) Model setting(C) Simulation(A) Experiment(A) Model setting(B) Simulation 1(C) Simulation 2(B) Model setting(C) Simulation 1(A) Illustration (C) SimulationSimulated On/Off responses are sensitive to theinter-column connectionsW.Temporal expectancy: simulated OSR peaklatencies are linear to the periodicity.(B) Model settingSimulated MMN at the transition between regularand random sequences.Distinct onset- and offset-frequency receptivefields (FRFs) can be reproduced.(A) Experiment(D) Simulation 2

Deviance detection: On and off responses, omission response, and mismatch negativity

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