Internal Neural Dynamics Research Articles

Frequency-dependent effects of EEG phase resetting on reaction time

There is trial-to-trial variability in the reaction time to stimulus presentation. Since this variability exists even in an identical stimulus condition, it reflects the internal neural dynamics of the brain. To understand the neural dynamics that influence the reaction time, we conducted an electroencephalogram (EEG) experiment in which participants were asked to press a response button as quickly as possible when a stimulus was visually presented. Phase-locking factor analysis revealed that phase resetting in two frequency bands, which appeared 0.2 s after the stimulus presentation, characterized the reaction time. The combination of the theta band phase resetting in the left parietal region and the delta band phase resetting mainly in the posterior region was associated with the fastest reaction time, whereas delta band phase resetting without theta band phase resetting was associated with the faster reaction time. The results indicated that there were frequency-dependent effects in the relationships between the EEG phase resetting and reaction time.

Same lesson, varied choices by frontal cortex.

In an unfamiliar situation, animals display variable choice behavior. Based on computational modeling and empirical data, a new study suggests that the variability in decision-making across individuals is driven by differences in internal neural dynamics in the medial frontal cortex.

Local Dynamics in Trained Recurrent Neural Networks.

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Perceptual restoration of masked speech in human cortex.

Humans are adept at understanding speech despite the fact that our natural listening environment is often filled with interference. An example of this capacity is phoneme restoration, in which part of a word is completely replaced by noise, yet listeners report hearing the whole word. The neurological basis for this unconscious fill-in phenomenon is unknown, despite being a fundamental characteristic of human hearing. Here, using direct cortical recordings in humans, we demonstrate that missing speech is restored at the acoustic-phonetic level in bilateral auditory cortex, in real-time. This restoration is preceded by specific neural activity patterns in a separate language area, left frontal cortex, which predicts the word that participants later report hearing. These results demonstrate that during speech perception, missing acoustic content is synthesized online from the integration of incoming sensory cues and the internal neural dynamics that bias word-level expectation and prediction.

Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome.

The brain exhibits complex spatio-temporal patterns of activity. This phenomenon is governed by an interplay between the internal neural dynamics of cortical areas and their connectivity. Uncovering this complex relationship has raised much interest, both for theory and the interpretation of experimental data (e.g., fMRI recordings) using dynamical models. Here we focus on the so-called inverse problem: the inference of network parameters in a cortical model to reproduce empirically observed activity. Although it has received a lot of interest, recovering directed connectivity for large networks has been rather unsuccessful so far. The present study specifically addresses this point for a noise-diffusion network model. We develop a Lyapunov optimization that iteratively tunes the network connectivity in order to reproduce second-order moments of the node activity, or functional connectivity. We show theoretically and numerically that the use of covariances with both zero and non-zero time shifts is the key to infer directed connectivity. The first main theoretical finding is that an accurate estimation of the underlying network connectivity requires that the time shift for covariances is matched with the time constant of the dynamical system. In addition to the network connectivity, we also adjust the intrinsic noise received by each network node. The framework is applied to experimental fMRI data recorded for subjects at rest. Diffusion-weighted MRI data provide an estimate of anatomical connections, which is incorporated to constrain the cortical model. The empirical covariance structure is reproduced faithfully, especially its temporal component (i.e., time-shifted covariances) in addition to the spatial component that is usually the focus of studies. We find that the cortical interactions, referred to as effective connectivity, in the tuned model are not reciprocal. In particular, hubs are either receptors or feeders: they do not exhibit both strong incoming and outgoing connections. Our results sets a quantitative ground to explore the propagation of activity in the cortex.

Learning to Perceive the World as Probabilistic or Deterministic via Interaction With Others: A Neuro-Robotics Experiment.

We suggest that different behavior generation schemes, such as sensory reflex behavior and intentional proactive behavior, can be developed by a newly proposed dynamic neural network model, named stochastic multiple timescale recurrent neural network (S-MTRNN). The model learns to predict subsequent sensory inputs, generating both their means and their uncertainty levels in terms of variance (or inverse precision) by utilizing its multiple timescale property. This model was employed in robotics learning experiments in which one robot controlled by the S-MTRNN was required to interact with another robot under the condition of uncertainty about the other's behavior. The experimental results show that self-organized and sensory reflex behavior-based on probabilistic prediction-emerges when learning proceeds without a precise specification of initial conditions. In contrast, intentional proactive behavior with deterministic predictions emerges when precise initial conditions are available. The results also showed that, in situations where unanticipated behavior of the other robot was perceived, the behavioral context was revised adequately by adaptation of the internal neural dynamics to respond to sensory inputs during sensory reflex behavior generation. On the other hand, during intentional proactive behavior generation, an error regression scheme by which the internal neural activity was modified in the direction of minimizing prediction errors was needed for adequately revising the behavioral context. These results indicate that two different ways of treating uncertainty about perceptual events in learning, namely, probabilistic modeling and deterministic modeling, contribute to the development of different dynamic neuronal structures governing the two types of behavior generation schemes.

Complementary fMRI and EEG evidence for more efficient neural processing of rhythmic vs. unpredictably timed sounds.

The brain’s fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they will enter at optimal processing phases of the spontaneously occurring rhythmic excitability fluctuations. However, some contexts have a more predictable temporal structure than others. Here, we tested the hypothesis that the processing of rhythmic sounds is more efficient than the processing of irregularly timed sounds. To do this, we simultaneously measured functional magnetic resonance imaging (fMRI) and electro-encephalograms (EEG) while participants detected oddball target sounds in alternating blocks of rhythmic (e.g., with equal inter-stimulus intervals) or random (e.g., with randomly varied inter-stimulus intervals) tone sequences. Behaviorally, participants detected target sounds faster and more accurately when embedded in rhythmic streams. The fMRI response in the auditory cortex was stronger during random compared to random tone sequence processing. Simultaneously recorded N1 responses showed larger peak amplitudes and longer latencies for tones in the random (vs. the rhythmic) streams. These results reveal complementary evidence for more efficient neural and perceptual processing during temporally predictable sensory contexts.