Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches

Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma

We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models' processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies

Changes of oscillatory activity in pitch processing network and related tinnitus relief induced by acoustic CR neuromodulation

Chronic subjective tinnitus is characterized by abnormal neuronal synchronization in the central auditory system. As shown in a controlled clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms along with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas, which is often accompanied with a significant tinnitus pitch change. Here we studied if the tinnitus pitch change correlates with a reduction of tinnitus loudness and/or annoyance as assessed by visual analog scale (VAS) scores. Furthermore, we studied if the changes of the pattern of brain synchrony in tinnitus patients induced by 12 weeks of CR therapy depend on whether or not the patients undergo a pronounced tinnitus pitch change. Therefore, we applied standardized low-resolution brain electromagnetic tomography (sLORETA) to EEG recordings from two groups of patients with a sustained CR-induced relief of tinnitus symptoms with and without tinnitus pitch change. We found that absolute changes of VAS loudness and VAS annoyance scores significantly correlate with the modulus, i.e., the absolute value, of the tinnitus pitch change. Moreover, as opposed to patients with small or no pitch change we found a significantly stronger decrease in gamma power in patients with pronounced tinnitus pitch change in right parietal cortex (Brodmann area, BA 40), right frontal cortex (BA 9, 46), left temporal cortex (BA 22, 42), and left frontal cortex (BA 4, 6), combined with a significantly stronger increase of alpha (10–12 Hz) activity in the right and left anterior cingulate cortex (ACC; BA 32, 24). In addition, we revealed a significantly lower functional connectivity in the gamma band between the right dorsolateral prefrontal cortex (BA 46) and the right ACC (BA 32) after 12 weeks of CR therapy in patients with pronounced pitch change. Our results indicate a substantial, CR-induced reduction of tinnitus-related auditory binding in a pitch processing network

Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review

Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent. Emerging research in this field includes phenomenological models of mood fluctuations in bipolar disorder and biophysical models of functional imaging data in psychotic and affective disorders. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior

Using Phase Response Curves to Optimize Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. April 2016. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); vii, 190 pages.Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time

A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code

Recent experimental evidence suggests that oscillatory activity plays a pivotal role in the maintenance of information in working memory, both in rodents and humans. In particular, cross-frequency coupling between theta and gamma oscillations has been suggested as a core mechanism for multi-item memory. The aim of this work is to present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. We show that this model, with different synapse values, can be used to address different problems, such as the reconstruction of an item from partial information, the maintenance of multiple items simultaneously in memory, without any sequential order, and the reconstruction of an ordered sequence starting from an initial cue. The model consists of four interconnected layers; synapses are trained using Hebbian and anti-Hebbian mechanisms, in order to synchronize features in the same items, and desynchronize features in different items. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment ("imagination phase") and stimulated with high uniform noise, can randomly recover sequences previously learned, and link them together by exploiting the similarity among items

Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning

To study the interplay between hippocampus and medial prefrontal cortex (Pfc) and its importance for learning and memory consolidation, we measured the coherence in theta oscillations between these two structures in rats learning new rules on a Y maze. Coherence peaked at the choice point, most strongly after task rule acquisition. Simultaneously, Pfc pyramidal neurons reorganized their phase, concentrating at hippocampal theta trough, and synchronous cell assemblies emerged. This synchronous state may result from increased inhibition exerted by interneurons on pyramidal cells, as measured by cross-correlation, and could be modulated by dopamine: we found similar hippocampal-Pfc theta coherence increases and neuronal phase shifts following local administration of dopamine in Pfc of anesthetized rats. Pfc cell assemblies emerging during high coherence were preferentially replayed during subsequent sleep, concurrent with hippocampal sharp waves. Thus, hippocampal/prefrontal coherence could lead to synchronization of reward predicting activity in prefrontal networks, tagging it for subsequent memory consolidation.European Commission (Contract FP6-IST 027819)European Commission (Contract FP6-IST-027140)European Commission (Contract FP6-IST-027017

Role of the precentral cortex in adapting behavior to different mechanical environments

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 155-171).We routinely produce movements under different mechanical contexts. All interactions with the physical environment, such as swinging a hammer or lifting a carton of milk, alter the forces experienced during movement. With repeated experience, sensorimotor maps are adapted to maintain a high level of movement performance regardless of the mechanical environment. This dissertation explored the contribution of the precentral cortex to this process of motor adaptation. In the first experiment, we recorded precentral neural activity in rhesus monkeys that were trained to perform visually-cued reaching movements while holding on to a robotic manipulandum capable of changing the forces experienced during the task. Preparation and control of the reaching movements were correlated with single cell activity throughout the precentral cortex, including the primary motor cortex and five different premotor areas. Precentral field potential activity was also modulated during the reaching behavior, particularly in the beta and high gamma frequency bands. When novel forces were introduced, single cell activity changed in a manner that specifically compensated for the applied forces and mirrored the time course of behavioral adaptation.(cont.) Force-related changes were present in the field potential activity as well. Some of these changes were maintained following removal of the forces. Control data and simulations revealed that these residual changes were well described by a model of noisy adaptation in a redundant cortical network. In the second experiment, human subjects performed the same reaching paradigm after receiving transcranial magnetic stimulation to transiently inhibit cortical activity. Initial learning of the novel force environment was normal but recall of the field 24 hours later was impaired relative to controls. Taken together, the results suggest that distributed areas within the precentral cortex are involved in recalibrating sensorimotor maps to fit the present mechanical context and in initiating a memory trace of newly-experienced environments.by Andrew Garmory Richardson.Ph.D

Two-Photon Voltage Imaging of Supragranular Barrel Cortex in Mice: Oscillations and Responses

The supragranular layers of cortex are key information integration and computation areas with dominant cortico-cortical connections. While layers 2 and 3 are densely packed with somata, layer 1 is almost free of somata. The absence of somata makes the analysis of layer 1 difficult. Electrical recording from small processes within layer 1 are not possible and electric field recordings are difficult due to the low seal resistance. Imaging processes of layer 1 remains difficult as cells project into it from many distant areas and due to the dense and intermingled packing.Here, I record sensory signals in supragranular layers, including layer 1 through a combination of voltage sensitive dye imaging, and an intracellular calcium indicator. Optical sectioning with two-photon microscopy allowed resolution in depth, showing changes in the sensory signal within layer 1. Additionally, cortical oscillations were detected with the voltage-sensitive dye in the delta, theta, and beta bands, and, for the first time with voltage imaging, also in the slow-gamma (35 Hz) band, in vivo. Delta, theta, and gamma oscillations were modulated by sensory stimuli.As very little is known about membrane voltage oscillations in layer 1 and to optimize optical voltage recordings in layer 1, I developed a novel surgery to apply voltage dye primarily to layer 1, without removing the dura or injecting dye within the brain. I also applied a new voltage-sensitive dye optimized for tissue diffusion with this surgery. I imaged cortical membrane potential oscillations with two-photon microscopy depth-resolved (25 to 100 µm below dura) in anesthetized and awake mice. Again, I found delta (0.5-4 Hz), theta (4-10 Hz), low beta (10-20 Hz), and low gamma (30-40 Hz) oscillations. All oscillation bands were stronger in awake animals. While the power of delta, theta, and low beta oscillations increased with depth, the power of low gamma was more constant throughout layer 1. These findings identify layer 1 as an important coordination hub for the dynamic binding process of neurons mediated by oscillations.Okinawa Institute of Science and Technology Graduate Universit