Network analysis of the cellular circuits of memory

Intuitively, memory is conceived as a collection of static images that we accumulate as we experience the world. But actually, memories are constantly changing through our life, shaped by our ongoing experiences. Assimilating new knowledge without corrupting pre-existing memories is then a critical brain function. However, learning and memory interact: prior knowledge can proactively influence learning, and new information can retroactively modify memories of past events. The hippocampus is a brain region essential for learning and memory, but the network-level operations that underlie the continuous integration of new experiences into memory, segregating them as discrete traces while enabling their interaction, are unknown. Here I show a network mechanism by which two distinct memories interact. Hippocampal CA1 neuron ensembles were monitored in mice as they explored a familiar environment before and after forming a new place-reward memory in a different environment. By employing a network science representation of the co-firing relationships among principal cells, I first found that new associative learning modifies the topology of the cells’ co-firing patterns representing the unrelated familiar environment. I fur- ther observed that these neuronal co-firing graphs evolved along three functional axes: the first segregated novelty; the second distinguished individual novel be- havioural experiences; while the third revealed cross-memory interaction. Finally, I found that during this process, high activity principal cells rapidly formed the core representation of each memory; whereas low activity principal cells gradually joined co-activation motifs throughout individual experiences, enabling cross-memory in- teractions. These findings reveal an organizational principle of brain networks where high and low activity cells are differentially recruited into coactivity motifs as build- ing blocks for the flexible integration and interaction of memories. Finally, I employ a set of manifold learning and related approaches to explore and characterise the complex neural population dynamics within CA1 that underlie sim- ple exploration.Open Acces

Micro-, Meso- and Macro-Dynamics of the Brain

Neurosciences, Neurology, Psychiatr

Visual statistics using neural networks.

This thesis describes the application of statistical techniques to natural images as a means of gaining insight into the operation of low level vision. First, the statistical technique of principal component analysis is applied to a collection of natural images: a match with psychophysical data is found; and a solution to the dynamic range problem proposed. The problem of learning and calibrating psychological and physiological representations of space is t hen investigated. The grey level correlations in natural images are measured and their physical causes investigated. The resulting correlations are related both to psychological distortions of space and to the cortical representation of space in \T in macaque monkey. The interpretation in terms of a system calibrating itself using the correlations in the input signals is shown to produce accurate psychological and physiological predictions. Lastly the problems of creating low level models of the visual input is looked at using a framework originally proposed by Hinton and Sejnowski (1983). The way in which phase coherence of (neuronal) firing in a network can label the probability of an interpretation is demonstrated. A new search technique, inspired by the different time courses of inhibition and excitation in the cortex, is proposed for searching for the most likely visual interpretation. It is concluded that statistical techniques can provide insight into the operation of low level vision

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs

A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces