Switching Operation Modes in the Neocortex via Cholinergic Neuromodulation : A Computational Model of Uncertainty, Learning, and Inhibition.

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty.

Coordinated representational reinstatement in the human hippocampus and lateral temporal cortex during episodic memory retrieval.

Theoretical models of episodic memory have proposed that retrieval depends on interactions between the hippocampus and neocortex, where hippocampal reinstatement of item-context associations drives neocortical reinstatement of item information. Here, we simultaneously recorded intracranial EEG from hippocampus and lateral temporal cortex (LTC) of epilepsy patients who performed a virtual reality spatial navigation task. We extracted stimulus-specific representations of both item and item-context associations from the time-frequency patterns of activity in hippocampus and LTC. Our results revealed a double dissociation of representational reinstatement across time and space: an early reinstatement of item-context associations in hippocampus preceded a later reinstatement of item information in LTC. Importantly, reinstatement levels in hippocampus and LTC were correlated across trials, and the quality of LTC reinstatement was predicted by the magnitude of phase synchronization between hippocampus and LTC. These findings confirm that episodic memory retrieval in humans relies on coordinated representational interactions within a hippocampal-neocortical network.

Cholinergic Behavior State-Dependent Mechanisms of Neocortical Gain Control: a Neurocomputational Study.

The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum, and the amygdala. A particular case in point is the learning paradigm of classical conditioning where acquired representations of states of the world such as sounds and visual features are associated with predefined discrete behavioral responses such as eye blinks and freezing. Learning progresses in a very specific order, where the animal first identifies the features of the task that are predictive of a motivational state and then forms the association of the current sensory state with a particular action and shapes this action to the specific contingency. This adaptive feature selection has both attentional and memory components, i.e., a behaviorally relevant state must be detected while its representation must be stabilized to allow its interfacing to output systems. Here, we present a computational model of the neocortical systems that underlie this feature detection process and its state-dependent modulation mediated by the amygdala and its downstream target the nucleus basalis of Meynert. In particular, we analyze the role of different populations of inhibitory interneurons in the regulation of cortical activity and their state-dependent gating of sensory signals. In our model, we show that the neuromodulator acetylcholine (ACh), which is in turn under control of the amygdala, plays a distinct role in the dynamics of each population and their associated gating function serving the detection of novel sensory features not captured in the state of the network, facilitating the adjustment of cortical sensory representations and regulating the switching between modes of attention and learning.

A functional magnetic resonance imaging study of visuomotor processing in a virtual reality-based paradigm: Rehabilitation Gaming System.

The Rehabilitation Gaming System (RGS) has been designed as a flexible, virtual-reality (VR)-based device for rehabilitation of neurological patients. Recently, training of visuomotor processing with the RGS was shown to effectively improve arm function in acute and chronic stroke patients. It is assumed that the VR-based training protocol related to RGS creates conditions that aid recovery by virtue of the human mirror neuron system. Here, we provide evidence for this assumption by identifying the brain areas involved in controlling the catching of approaching colored balls in the virtual environment of the RGS. We used functional magnetic resonance imaging of 18 right-handed healthy subjects (24 ± 3 years) in both active and imagination conditions. We observed that the imagery of target catching was related to activation of frontal, parietal, temporal, cingulate and cerebellar regions. We interpret these activations in relation to object processing, attention, mirror mechanisms, and motor intention. Active catching followed an anticipatory mode, and resulted in significantly less activity in the motor control areas. Our results provide preliminary support for the hypothesis underlying RGS that this novel neurorehabilitation approach engages human mirror mechanisms that can be employed for visuomotor training.

Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training.

Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.

The hippocampal-striatal axis in learning, prediction and goal-directed behavior.

The hippocampal formation and striatum subserve declarative and procedural memory, respectively. However, experimental evidence suggests that the ventral striatum, as opposed to the dorsal striatum, does not lend itself to being part of either system. Instead, it may constitute a system integrating inputs from the amygdala, prefrontal cortex and hippocampus to generate motivational, outcome-predicting signals that invigorate goal-directed behaviors. Inspired by reinforcement learning models, we suggest an alternative scheme for computational functions of the striatum. Dorsal and ventral striatum are proposed to compute outcome predictions largely in parallel, using different types of information as input. The nature of the inputs to striatum is furthermore combinatorial, and the specificity of predictions transcends the level of scalar value signals, incorporating episodic information.

Behavioral rehabilitation of the eye closure reflex in senescent rats using a real-time biosignal acquisition system.

In this paper the replacement of a lost learning function of rats through a computer-based real-time recording and feedback system is shown. In an experiment two recording electrodes and one stimulation electrode were implanted in an anesthetized rat. During a classical-conditioning paradigm, which includes tone and airpuff stimulation, biosignals were recorded and the stimulation events detected. A computational model of the cerebellum acquired the association between the stimuli and gave feedback to the brain of the rat using deep brain stimulation in order to close the eyelid of the rat. The study shows that replacement of a lost brain function using a direct bidirectional interface to the brain is realizable and can inspire future research for brain rehabilitation.

New technologies and concepts for rehabilitation in the acute phase of stroke: a collaborative matrix.

The process of developing a successful stroke rehabilitation methodology requires four key components: a good understanding of the pathophysiological mechanisms underlying this brain disease, clear neuroscientific hypotheses to guide therapy, adequate clinical assessments of its efficacy on multiple timescales, and a systematic approach to the application of modern technologies to assist in the everyday work of therapists. Achieving this goal requires collaboration between neuroscientists, technologists and clinicians to develop well-founded systems and clinical protocols that are able to provide quantitatively validated improvements in patient rehabilitation outcomes. In this article we present three new applications of complementary technologies developed in an interdisciplinary matrix for acute-phase upper limb stroke rehabilitation - functional electrical stimulation, arm robot-assisted therapy and virtual reality-based cognitive therapy. We also outline the neuroscientific basis of our approach, present our detailed clinical assessment protocol and provide preliminary results from patient testing of each of the three systems showing their viability for patient use.