Dynamics of the equatorial undercurrent and its termination

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution January 1988This study focuses on the zonal weakening, eastern termination and seasonal variations of the Atlantic equatorial undercurrent (EUC). The main and most original contribution of the dissertation is a detailed analysis of the Atlantic EUC simulated by Philander and Pacanowski's (1986) general circulation model (GCM), which provides a novel description of the dynamical regimes governing various regions of a nonlinear stratified undercurrent. Only in a narrow deep western region of the simulation does one find an approximately inertial regime corresponding to zonal acceleration. Elsewhere frictional processes cannot be ignored. The bulk of the mid-basin model EUC terminates in the overlying westward surface flow while only a small fraction (the deeper more inertial layers) terminates at the eastern coast. In agreement with observations, a robust feature of the GCM not present in simpler models is the apparent migration of the EUC core from above the thermocline in the west to below it in the east. In the GCM, this happens because the eastward flow is eroded more efficiently by vertical friction above the base of the thermocline than by lateral friction at greater depths. This mechanism is a plausible one for the observed EUC. A scale analysis using a depth scale which decreases with distance eastwards predicts the model zonal transition between western inertial and eastern inertio-frictional regimes. Historical and recent observations and simple models of the equatorial and coastal eastern undercurrents are reviewed, and a new analysis of current measurements in the eastern equatorial Atlantic is presented. Although the measurements are inadequate for definitive conclusions, they suggest that Lukas' (1981) claim of a spring surge of the Pacific EUC to the eastern coast and a seasonal branching of the EUC into a coastal southeastward undercurrent may also hold for the Atlantic Ocean. To improve the agreement between observed and modelled strength of the eastern undercurrent, it is suggested that the eddy coefficient of horizontal mixing should be reduced in future GCM simulations.This work was supported by NSF grants OCE82-14771, OCE82-08744 and OCE85-14885

What controls gain in gain control? Mismatch negativity (MMN), priors and system biases

Repetitious patterns enable the auditory system to form prediction models specifying the most likely characteristics of subsequent sounds. Pattern deviations elicit mismatch negativity (MMN), the amplitude of which is modulated by the size of the deviation and confidence in the model. Todd et al. (2001; 2013) demonstrated that a multi-timescale sequence reveals a bias that profoundly distorts the impact of local sound statistics on the MMN amplitude. Two sounds alternate roles as repetitious “standard” and rare “deviant” rapidly (every 0.8 minutes) or slowly (every 2.4 minutes). The bias manifests as larger MMN to the sound first encountered as deviant in slow compared to fast changing sequences, but no difference for the sound first encountered as a standard. We propose that the bias is due to how Bayesian priors shape filters of sound relevance. By examining the time-course of change in MMN amplitude we show that the bias manifests immediately after roles change but rapidly disappears thereafter. The bias was reflected in the response to deviant sounds only (not in response to standards), consistent with precision estimates extracted from second order patterns modulating gain differentially for the two sounds.. Evoked responses to deviants suggest that pattern extraction and reactivation of priors can operate over tens of minutes or longer. Both MMN and deviant responses establish that: (1) priors are defined by the most proximally encountered probability distribution when one exists but; (2) when no prior exists, one is instantiated by sequence onset characteristics; and (3) priors require context interruption to be updated

Predictive regularity representations in deviance detection and auditory stream segregation: from conceptual to computational models

Predictive accounts of perception have received increasing attention in the past twenty years. Detecting violations of auditory regularities, as reflected by the Mismatch Negativity (MMN) auditory event-related potential, is amongst the phenomena seamlessly fitting this approach. Largely based on the MMN literature, we propose a psychological conceptual framework called the Auditory Event Representation System (AERS), which is based on the assumption that auditory regularity violation detection and the formation of auditory perceptual objects are based on the same predictive regularity representations. Based on this notion, a computational model of auditory stream segregation, called CHAINS, has been developed. In CHAINS, the auditory sensory event representation of each incoming sound is considered for being the continuation of likely combinations of the preceding sounds in the sequence, thus providing alternative interpretations of the auditory input. Detecting repeating patterns allows predicting upcoming sound events, thus providing a test and potential support for the corresponding interpretation. Alternative interpretations continuously compete for perceptual dominance. In this paper, we briefly describe AERS and deduce some general constraints from this conceptual model. We then go on to illustrate how these constraints are computationally specified in CHAINS

Auditory event-related potentials

Auditory event related potentials are electric potentials (AERP, AEP) and magnetic fields (AEF) generated by the synchronous activity of large neural populations in the brain, which are time-locked to some actual or expected sound event

A predictive coding account of MMN reduction in schizophrenia

The mismatch negativity (MMN) is thought to be an index of the automatic activation of a specialized network for active prediction and deviance detection in the auditory cortex. It is consistently reduced in schizophrenic patients and has received a lot of interest as a clinical and translational tool. The main neuronal hypothesis regarding the mechanisms leading to a reduced MMN in schizophrenic patients is a dysfunction of NMDA receptors (NMDA-R). However, this hypothesis has never been implemented in a neuronal model. In this paper, we examine the consequences of NMDA-R dysfunction in a neuronal model of MMN based on predictive coding principle. I also investigate how predictive processes may interact with synaptic adaptation in MMN generations and examine the consequences of this interaction for the use of MMN paradigms in schizophrenia research

Gaze-evoked nystagmus secondaire à une lésion ischémique du noyau prepositus de l’hypoglosse, à propos d’un cas

International audienc

Recurrent neural networks that learn multistep visual routines with reinforcement learning

Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant intermediate results need to be stored by neurons and propagated to the next subproblem, until the overarching goal has been completed. We will here consider visual tasks, which can be decomposed into sequences of elemental visual operations. Experimental evidence suggests that intermediate results of the elemental operations are stored in working memory as an enhancement of neural activity in the visual cortex. The focus of enhanced activity is then available for subsequent operations to act upon. The main question at stake is how the elemental operations and their sequencing can emerge in neural networks that are trained with only rewards, in a reinforcement learning setting. We here propose a new recurrent neural network architecture that can learn composite visual tasks that require the application of successive elemental operations. Specifically, we selected three tasks for which electrophysiological recordings of monkeys’ visual cortex are available. To train the networks, we used RELEARNN, a biologically plausible four-factor Hebbian learning rule, which is local both in time and space. We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operations, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observed in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subsequent subroutines. Our results demonstrate how a biologically plausible learning rule can train a recurrent neural network on multistep visual tasks

Motor Control by Sensory Cortex

International audienc

Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness

International audienceDetecting residual consciousness in unresponsive patients is a major clinical concern and a challenge for theoretical neuroscience. To tackle this issue, we recently designed a paradigm that dissociates two electro-encephalographic (EEG) responses to auditory novelty. Whereas a local change in pitch automatically elicits a mismatch negativity (MMN), a change in global sound sequence leads to a late P300b response. The latter component is thought to be present only when subjects consciously perceive the global novelty. Unfortunately, it can be difficult to detect because individual variability is high, especially in clinical recordings. Here, we show that multivariate pattern classifiers can extract subject-specific EEG patterns and predict single-trial local or global novelty responses. We first validate our method with 38 high-density EEG, MEG and intracranial EEG recordings. We empirically demonstrate that our approach circumvents the issues associated with multiple comparisons and individual variability while improving the statistics. Moreover, we confirm in control subjects that local responses are robust to distraction whereas global responses depend on attention. We then investigate 104 vegetative state (VS), minimally conscious state (MCS) and conscious state (CS) patients recorded with high-density EEG. For the local response, the proportion of significant decoding scores (M = 60%) does not vary with the state of consciousness. By contrast, for the global response, only 14% of the VS patients' EEG recordings presented a significant effect, compared to 31% in MCS patients' and 52% in CS patients'. In conclusion, single-trial multivariate decoding of novelty responses provides valuable information in non-communicating patients and paves the way towards real-time monitoring of the state of consciousness