A Comparison of Neural Decoding Methods and Population Coding Across Thalamo-Cortical Head Direction Cells

Head direction (HD) cells, which fire action potentials whenever an animal points its head in a particular direction, are thought to subserve the animal’s sense of spatial orientation. HD cells are found prominently in several thalamo-cortical regions including anterior thalamic nuclei, postsubiculum, medial entorhinal cortex, parasubiculum, and the parietal cortex. While a number of methods in neural decoding have been developed to assess the dynamics of spatial signals within thalamo-cortical regions, studies conducting a quantitative comparison of machine learning and statistical model-based decoding methods on HD cell activity are currently lacking. Here, we compare statistical model-based and machine learning approaches by assessing decoding accuracy and evaluate variables that contribute to population coding across thalamo-cortical HD cells

Understanding motor control in humans to improve rehabilitation robots

Recent reviews highlighted the limited results of robotic rehabilitation and the low quality of evidences in this field. Despite the worldwide presence of several robotic infrastructures, there is still a lack of knowledge about the capabilities of robotic training effect on the neural control of movement. To fill this gap, a step back to motor neuroscience is needed: the understanding how the brain works in the generation of movements, how it adapts to changes and how it acquires new motor skills is fundamental. This is the rationale behind my PhD project and the contents of this thesis: all the studies included in fact examined changes in motor control due to different destabilizing conditions, ranging from external perturbations, to self-generated disturbances, to pathological conditions. Data on healthy and impaired adults have been collected and quantitative and objective information about kinematics, dynamics, performance and learning were obtained for the investigation of motor control and skill learning. Results on subjects with cervical dystonia show how important assessment is: possibly adequate treatments are missing because the physiological and pathological mechanisms underlying sensorimotor control are not routinely addressed in clinical practice. These results showed how sensory function is crucial for motor control. The relevance of proprioception in motor control and learning is evident also in a second study. This study, performed on healthy subjects, showed that stiffness control is associated with worse robustness to external perturbations and worse learning, which can be attributed to the lower sensitiveness while moving or co-activating. On the other hand, we found that the combination of higher reliance on proprioception with \u201cdisturbance training\u201d is able to lead to a better learning and better robustness. This is in line with recent findings showing that variability may facilitate learning and thus can be exploited for sensorimotor recovery. Based on these results, in a third study, we asked participants to use the more robust and efficient strategy in order to investigate the control policies used to reject disturbances. We found that control is non-linear and we associated this non-linearity with intermittent control. As the name says, intermittent control is characterized by open loop intervals, in which movements are not actively controlled. We exploited the intermittent control paradigm for other two modeling studies. In these studies we have shown how robust is this model, evaluating it in two complex situations, the coordination of two joints for postural balance and the coordination of two different balancing tasks. It is an intriguing issue, to be addressed in future studies, to consider how learning affects intermittency and how this can be exploited to enhance learning or recovery. The approach, that can exploit the results of this thesis, is the computational neurorehabilitation, which mathematically models the mechanisms underlying the rehabilitation process, with the aim of optimizing the individual treatment of patients. Integrating models of sensorimotor control during robotic neurorehabilitation, might lead to robots that are fully adaptable to the level of impairment of the patient and able to change their behavior accordingly to the patient\u2019s intention. This is one of the goals for the development of rehabilitation robotics and in particular of Wristbot, our robot for wrist rehabilitation: combining proper assessment and training protocols, based on motor control paradigms, will maximize robotic rehabilitation effects

Invariant neural dynamics drive commands to control different movements.

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands

Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions

A Search For Principles of Basal Ganglia Function

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in humans. Different modes of basal ganglia dysfunction lead to Parkinson's disease and Huntington's disease, which have debilitating motor and cognitive symptoms. However, despite intensive study, both the internal computational mechanisms of the basal ganglia, and their contribution to normal brain function, have been elusive. The goal of this thesis is to identify basic principles that underlie basal ganglia function, with a focus on signal representation, computation, dynamics, and plasticity. This process begins with a review of two current hypotheses of normal basal ganglia function, one being that they automatically select actions on the basis of past reinforcement, and the other that they compress cortical signals that tend to occur in conjunction with reinforcement. It is argued that a wide range of experimental data are consistent with these mechanisms operating in series, and that in this configuration, compression makes selection practical in natural environments. Although experimental work is outside the present scope, an experimental means of testing this proposal in the future is suggested. The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering Framework (NEF), which provides an integrated theoretical account of computation, representation, and dynamics in large neural circuits. The NEF provides considerable insight into basal ganglia function, but its explanatory power is potentially limited by two assumptions that the basal ganglia violate. First, like most large-network models, the NEF assumes that neurons integrate multiple synaptic inputs in a linear manner. However, synaptic integration in the basal ganglia is nonlinear in several respects. Three modes of nonlinearity are examined, including nonlinear interactions between dendritic branches, nonlinear integration within terminal branches, and nonlinear conductance-current relationships. The first mode is shown to affect neuron tuning. The other two modes are shown to enable alternative computational mechanisms that facilitate learning, and make computation more flexible, respectively. Secondly, while the NEF assumes that the feedforward dynamics of individual neurons are dominated by the dynamics of post-synaptic current, many basal ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation, bursting, and hysterses. Of these, it is shown that the NEF theory of network dynamics applies fairly directly to certain cases of firing-rate adaptation. However, more complex dynamics, including nonlinear dynamics that are diverse across a population, can be described using the NEF equations for representation. In particular, a neuron's response can be characterized in terms of a more complex function that extends over both present and past inputs. It is therefore straightforward to apply NEF methods to interpret the effects of complex cell dynamics at the network level. The role of spike timing in basal ganglia function is also examined. Although the basal ganglia have been interpreted in the past to perform computations on the basis of mean firing rates (over windows of tens or hundreds of milliseconds) it has recently become clear that patterns of spikes on finer timescales are also functionally relevant. Past work has shown that precise spike times in sensory systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these computations do not require spike timing that is very precise. As a consequence, irregular and highly-variable firing patterns can drive behaviour with which they have no detectable correlation. Most of the projection neurons in the basal ganglia are inhibitory, and the effect of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each presynaptic neuron can both excite and inhibit its targets, but this is hardly ever the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei. Finally, the relationship between population coding and synaptic plasticity is discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of neuron responses within the coded space. These results permit a straightforward interpretation of the effects of synaptic plasticity on information processing at the network level. Together with the NEF, these new results provide a rich set of theoretical principles through which the dominant physiological factors that affect basal ganglia function can be more clearly understood

Inhibition and oscillatory activity in human motor cortex

Using transcranial magnetic stimulation (TMS) important information can be obtained about the function of motor cortical circuitry during performance of voluntary movements by conscious human subjects. In particular, pairs of TMS pulses can probe inhibitory pathways projecting onto corticospinal neurones, which themselves project to motoneurones innervating hand muscles. This allows investigation of inhibitory circuitry involved in the performance of specific motor tasks, such as the precision grip. Previous studies have shown that pronounced synchronous oscillatory activity within the hand motor system is present at both cortical and muscular level when subjects maintain steady grasp of an object in a precision grip. The origin of this synchronous activity is unknown. However modelling studies have suggested that inhibitory pathways are likely to play an important role in the generation of cortical oscillations, and therefore TMS was used in this Thesis to investigate the origin of synchrony present during the precision grip task. In the first study, parameters of the paired-pulse test used to measure intracortical inhibition were examined. It was found that by modifying the intensities of the stimuli, and the interval between the paired-pulses, different phases of inhibition could be measured. This enabled specific use of TMS to investigate inhibitory pathways. Both single and paired-pulse TMS were then delivered to the motor cortex of subjects performing a precision grip task. It was found that low intensity TMS could reset the phase of muscle oscillatory activity, consistent with corticospinal neurones being part of the circuitry that generates the oscillatory rhythm. When, in the paired-pulse test, a low intensity stimulus was followed a few milliseconds later with a larger TMS stimulus, in the paired-pulse test, strong intracortical inhibition could be measured. This suggested that inhibitory interneurones activated by low intensity TMS could play an important role in the rhythm-generating network. An additional study looked at the importance of cutaneous receptor feedback on synchrony, by studying the effects of local anaesthesia of the index finger and thumb. Whereas low intensity TMS was shown to enhance synchronous activity between muscle pairs, suppression of cutaneous feedback from the digits reduced it. Results in this Thesis suggest that inhibitory interneurones within the motor cortex are important in the generation of synchronous activity within the hand motor system. This synchrony is also under the influence of cutaneous afferent input

The encoding of model-based control signals in rat anterior cingulate cortex

The anterior cingulate cortex (ACC) has been implicated in a wide variety of behaviours. Yet, a comprehensive theory of the ACC function is lacking. A promising theory is that ACC uses information from past experience to create predictive mental models that guide future response selection – that is the ACC is a model-based controller. This thesis tests the key hypotheses supporting this theory in two separate tasks. The results of the first experiment show that ACC tracks a rat’s state with high spatiotemporal resolution during a binary-choice task. The results of the second task demonstrate that ACC neurons encode several abstract features of a task, including information about the block structure of the task. Together the results suggests that the schema in the ACC is to reconfigure in order to reflect abstract rules of the task, which is essential for optimizing future response selection

Brain Rhythms in Object Recognition and Manipulation

Our manual interactions with objects represent the most fundamental activity in our everyday life. Whereas the grasp of an object is driven by the perceptual senses, using an object for its function relies on learnt experience to retrieve. Recent theories explain how the brain takes decisions based on perceptual information, yet the question of how does it retrieve object knowledge to use tools remains unanswered. Discovering the neuronal implementation of the retrieval of object knowledge would help understanding praxic impairments and provide appropriate neurorehabilitation. This thesis reports five investigations on the neuronal oscillatory activity involved in accessing object knowledge. Employing an original paradigm combining EEG recordings with tool use training in virtual reality, I demonstrated that beta oscillations are crucial to the retrieval of object knowledge during object recognition. Multiple evidence points toward an access to object knowledge during the 300 to 400 ms of visual processing. The different topographies of the beta oscillations suggest that tool knowledge is encoded in distinct brain areas but generally located within the left hemisphere. Importantly, learning action information about an object has consequences on its manipulations. Multiplying tool use knowledge about an object increases the beta desynchronization and slows down motor control. Furthermore, the present data report an influence of language on object manipulations and beta oscillations, in a way that learning the name of an object speeds up its use while impedes its grasp. This shred of evidence led to the formulation of three testable hypotheses extending contemporary theories of object manipulation and semantic memory. First, the preparation of object transportation or use could be distinguished by the synchronization/desynchronization patterns of mu and beta rhythms. Second, action competitions originate from both perceptuo-motor and memory systems. Third, accessing to semantic object knowledge during object processing could be indexed by the bursts of desynchronization of high-beta oscillations in the brain.MSCA-ETN SECURE [642667

Learning and Decision Making in Social Contexts: Neural and Computational Models

Social interaction is one of humanity's defining features. Through it, we develop ideas, express emotions, and form relationships. In this thesis, we explore the topic of social cognition by building biologically-plausible computational models of learning and decision making. Our goal is to develop mechanistic explanations for how the brain performs a variety of social tasks, to test those theories by simulating neural networks, and to validate our models by comparing to human and animal data. We begin by introducing social cognition from functional and anatomical perspectives, then present the Neural Engineering Framework, which we use throughout the thesis to specify functional brain models. Over the course of four chapters, we investigate many aspects of social cognition using these models. We begin by studying fear conditioning using an anatomically accurate model of the amygdala. We validate this model by comparing the response properties of our simulated neurons with real amygdala neurons, showing that simulated behavior is consistent with animal data, and exploring how simulated fear generalization relates to normal and anxious humans. Next, we show that biologically-detailed networks may realize cognitive operations that are essential for social cognition. We validate this approach by constructing a working memory network from multi-compartment cells and conductance-based synapses, then show that its mnemonic performance is comparable to animals performing a delayed match-to-sample task. In the next chapter, we study decision making and the tradeoffs between speed and accuracy: our network gathers information from the environment and tracks the value of choice alternatives, making a decision once certain criteria are met. We apply this model to a two-choice decision task, fit model parameters to recreate the behavior of individual humans, and reproduce the speed-accuracy tradeoff evident in the human population. Finally, we combine our networks for learning, working memory, and decision making into a cognitive agent that uses reinforcement learning to play a simple social game. We compare this model with two other cognitive architectures and with human data from an experiment we ran, and show that our three cognitive agents recreate important patterns in the human data, especially those related to social value orientation and cooperative behavior. Our concluding chapter summarizes our contributions to the field of social cognition and proposes directions for further research. The main contribution of this thesis is the demonstration that a diverse set of social cognitive abilities may be explained, simulated, and validated using a functionally-descriptive, biologically-plausible theoretical framework. Our models lay a foundation for studying increasingly-sophisticated forms of social cognition in future work

Neurophysiological correlates of preparation for action measured by electroencephalography

The optimal performance of an action depends to a great extend on the ability of a person to prepare in advance the appropriate kinetic and kinematic parameters at a specific point in time in order to meet the demands of a given situation and to foresee its consequences to the surrounding environment. In the research presented in this thesis, I employed high-density electroencephalography in order to study the neural processes underlying preparation for action. A typical way for studying preparation for action in neuroscience is to divide it in temporal preparation (when to respond) and event preparation (what response to make). In Chapter 2, we identified electrophysiological signs of implicit temporal preparation in a task where such preparation was not essential for the performance of the task. Electrophysiological traces of implicit timing were found in lateral premotor, parietal as well as occipital cortices. In Chapter 3, explicit temporal preparation was assessed by comparing anticipatory and reactive responses to periodically or randomly applied external loads, respectively. Higher (pre)motor preparatory activity was recorded in the former case, which resulted in lower post-load motor cortex activation and consequently to lower long-latency reflex amplitude. Event preparation was the theme of Chapter 4, where we introduced a new method for studying (at the source level) the generator mechanisms of lateralized potentials related to response selection, through the interaction with steady-state somatosensory responses. Finally, in Chapter 5 we provided evidence for the existence of concurrent and mutually inhibiting representations of multiple movement options in premotor and primary motor areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo