Emergence of a stable cortical map for neuroprosthetic control.

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram

A Statistical Description of Neural Ensemble Dynamics

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility

Towards a bionic bat: A biomimetic investigation of active sensing, Doppler-shift estimation, and ear morphology design for mobile robots.

Institute of Perception, Action and BehaviourSo-called CF-FM bats are highly mobile creatures who emit long calls in which much of the energy is concentrated in a single frequency. These bats face sensor interpretation problems very similar to those of mobile robots provided with ultrasonic sensors, while navigating in cluttered environments. This dissertation presents biologically inspired engineering on the use of narrowband Sonar in mobile robotics. It replicates, using robotics as a modelling medium, how CF-FM bats process and use the constant frequency part of their emitted call for several tasks, aiming to improve the design and use of narrowband ultrasonic sensors for mobile robot navigation. The experimental platform for the work is RoBat, the biomimetic sonarhead designed by Peremans and Hallam, mounted on a commercial mobile platform as part of the work reported in this dissertation. System integration, including signal processing capabilities inspired by the bat’s auditory system and closed loop control of both sonarhead and mobile base movements, was designed and implemented. The result is a versatile tool for studying the relationship between environmental features, their acoustic correlates and the cues computable from them, in the context of both static, and dynamic real-time closed loop, behaviour. Two models of the signal processing performed by the bat’s cochlea were implemented, based on sets of bandpass filters followed by full-wave rectification and low-pass filtering. One filterbank uses Butterworth filters whose centre frequencies vary linearly across the set. The alternative filterbank uses gammatone filters, with centre frequencies varying non-linearly across the set. Two methods of estimating Doppler-shift from the return echoes after cochlear signal processing were implemented. The first was a simple energy-weighted average of filter centre frequencies. The second was a novel neural network-based technique. Each method was tested with each of the cochlear models, and evaluated in the context of several dynamic tasks in which RoBat was moved at different velocities towards stationary echo sources such as walls and posts. Overall, the performance of the linear filterbank was more consistent than the gammatone. The same applies to the ANN, with consistently better noise performance than the weighted average. The effect of multiple reflectors contained in a single echo was also analysed in terms of error in Doppler-shift estimation assuming a single wider reflector. Inspired by the Doppler-shift compensation and obstacle avoidance behaviours found in CF-FM bats, a Doppler-based controller suitable for collision detection and convoy navigation in robots was devised and implemented in RoBat. The performance of the controller is satisfactory despite low Doppler-shift resolution caused by lower velocity of the robot when compared to real bats. Barshan’s and Kuc’s 2D object localisation method was implemented and adapted to the geometry of RoBat’s sonarhead. Different TOF estimation methods were tested, the parabola fitting being the most accurate. Arc scanning, the ear movement technique to recover elevation cues proposed by Walker, and tested in simulation by her, Peremans and Hallam, was here implemented on RoBat, and integrated with Barshan’s and Kuc’s method in a preliminary narrowband 3D tracker. Finally, joint work with Kim, K¨ampchen and Hallam on designing optimal reflector surfaces inspired by the CF-FM bat’s large pinnae is presented. Genetic algorithms are used for improving the current echolocating capabilities of the sonarhead for both arc scanning and IID behaviours. Multiple reflectors around the transducer using a simple ray light-like model of sound propagation are evolved. Results show phase cancellation problems and the need of a more complete model of wave propagation. Inspired by a physical model of sound diffraction and reflections in the human concha a new model is devised and used to evolve pinnae surfaces made of finite elements. Some interesting paraboloid shapes are obtained, improving performance significantly with respect to the bare transducer

Temporally Precise Cell-Specific Coherence Develops in Corticostriatal Networks during Learning

SummaryIt has been postulated that selective temporal coordination between neurons and development of functional neuronal assemblies are fundamental for brain function and behavior. Still, there is little evidence that functionally relevant coordination emerges preferentially in neuronal assemblies directly controlling behavioral output. We investigated coherence between primary motor cortex and the dorsal striatum as rats learn an abstract operant task. Striking coherence developed between these regions during learning. Interestingly, coherence was selectively increased in cells controlling behavioral output relative to adjacent cells. Furthermore, the temporal offset of these interactions aligned closely with corticostriatal conduction delays, demonstrating highly precise timing. Spikes from either region were followed by a consistent phase in the other, suggesting that network feedback reinforces coherence. Together, these results demonstrate that temporally precise coherence develops during learning specifically in output-relevant neuronal populations and further suggest that correlations in oscillatory activity serve to synchronize widespread brain networks to produce behavior

Bridging the day and night domain gap for semantic segmentation

2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9-12 Jun. 2019Perception in autonomous vehicles has progressed exponentially in the last years thanks to the advances of visionbased methods such as Convolutional Neural Networks (CNNs). Current deep networks are both efficient and reliable, at least in standard conditions, standing as a suitable solution for the perception tasks of autonomous vehicles. However, there is a large accuracy downgrade when these methods are taken to adverse conditions such as nighttime. In this paper, we study methods to alleviate this accuracy gap by using recent techniques such as Generative Adversarial Networks (GANs). We explore diverse options such as enlarging the dataset to cover these domains in unsupervised training or adapting the images on-the-fly during inference to a comfortable domain such as sunny daylight in a pre-processing step. The results show some interesting insights and demonstrate that both proposed approaches considerably reduce the domain gap, allowing IV perception systems to work reliably also at night.Ministerio de Economía y competitividadComunidad de Madri

Function Identification in Neuron Populations via Information Bottleneck

It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment

Physical principles for scalable neural recording

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices