Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Unveiling the role of plasticity rules in reservoir computing

Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training method. Likewise, successful implementation of neuro-inspired plasticity rules into RC artificial networks has boosted the performance of the original models. In this manuscript, we analyze the role that plasticity rules play on the changes that lead to a better performance of RC. To this end, we implement synaptic and non-synaptic plasticity rules in a paradigmatic example of RC model: the Echo State Network. Testing on nonlinear time series prediction tasks, we show evidence that improved performance in all plastic models are linked to a decrease of the pair-wise correlations in the reservoir, as well as a significant increase of individual neurons ability to separate similar inputs in their activity space. Here we provide new insights on this observed improvement through the study of different stages on the plastic learning. From the perspective of the reservoir dynamics, optimal performance is found to occur close to the so-called edge of instability. Our results also show that it is possible to combine different forms of plasticity (namely synaptic and non-synaptic rules) to further improve the performance on prediction tasks, obtaining better results than those achieved with single-plasticity models

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.Comment: 35 pages, 3 figure

Control of Ca2+ influx and calmodulin activation by SK-channels in dendritic spines (dataset)

A 3-dimensional model of Ca2+ and calmodulin dynamics within an idealised, but biophysically-plausible, dendritic spine, demonstrates that SK-channels regulate calmodulin activation specifically during neurone firing patterns associated with induction of spike timing-dependent plasticity.The journal article associated with this dataset is available at: http://hdl.handle.net/10871/21745.The key trigger for Hebbian synaptic plasticity is influx of Ca2+ into postsynaptic dendritic spines. The magnitude of [Ca2+] increase caused by NMDA-receptor (NMDAR) and voltage-gated Ca2+ -channel (VGCC) activation is thought to determine both the amplitude and direction of synaptic plasticity by differential activation of Ca2+ -sensitive enzymes such as calmodulin. Ca2+ influx is negatively regulated by Ca2+ -activated K+ channels (SK-channels) which are in turn inhibited by neuromodulators such as acetylcholine. However, the precise mechanisms by which SK-channels control the induction of synaptic plasticity remain unclear. Using a 3-dimensional model of Ca2+ and calmodulin dynamics within an idealised, but biophysically-plausible, dendritic spine, we show that SK-channels regulate calmodulin activation specifically during neuron-firing patterns associated with induction of spike timing-dependent plasticity. SK-channel activation and the subsequent reduction in Ca2+ influx through NMDARs and L-type VGCCs results in an order of magnitude decrease in calmodulin (CaM) activation, providing a mechanism for the effective gating of synaptic plasticity induction. This provides a common mechanism for the regulation of synaptic plasticity by neuromodulators

Towards a Brain-inspired Information Processing System: Modelling and Analysis of Synaptic Dynamics: Towards a Brain-inspired InformationProcessing System: Modelling and Analysis ofSynaptic Dynamics

Biological neural systems (BNS) in general and the central nervous system (CNS) specifically exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms of information processing within the BNS and their computational capabilities is a core objective of modern computer science, computational sciences and neuroscience. Among the main reasons of this tendency to understand the brain is to help in improving the quality of life of people suffer from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces (BCI), neural prostheses and other similar approaches are potential solutions either to help these patients through therapy or to push the progress in rehabilitation. There is however a significant lack of knowledge regarding the basic information processing within the CNS. Without a better understanding of the fundamental operations or sequences leading to cognitive abilities, applications like BCI or neural prostheses will keep struggling to find a proper and systematic way to help patients in this regard. In order to have more insights into these basic information processing methods, this thesis presents an approach that makes a formal distinction between the essence of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with expert systems. This approach investigates the underlying mechanisms allowing the CNS to be capable of performing a massive amount of computational tasks with a sustainable efficiency and flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent. The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities. These capabilities can be imported into spiking neural networks (SNN) by emulating the dynamic neural system. Emulating the dynamic system requires simulating both the inner workings of the system and the framework of performing the information processing tasks. Thus, this work comprises two main parts. The first part is concerned with introducing a proper and a novel dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system. This model represents a balanced integration between the needed biophysical details and being computationally inexpensive. Being a biophysical model is important to allow for the abilities of the target dynamic system to be inherited, and being simple is needed to allow for further implementation in large scale simulations and for hardware implementation in the future. Besides, the energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks seeking for stable states of activities. The second part of the thesis is consequently concerned with importing the processing framework of the dynamic system into the environment of SNN. This part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The concepts of computing with states are extended to investigate a computational model that is based on the finite-state machines and reservoir computing. Biological plausible validations of the introduced model and frameworks are performed. Results and discussions of these validations indicate that this study presents a significant advance on the way of empowering the knowledge about the mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on how to adopt the biological computational capabilities in computation science in general and in biologically-inspired spiking neural networks in specific. Large scale simulations and the development of neuromorphic hardware are work-in-progress and future work. Among the applications of the introduced work are neural prostheses and bionic automation systems

Biological networks as defense against adversarial attacks.

In recent years, more and more importance is given to interpretability in the ML field. The best known and most famous area in which the interpretability of a neural network is needed is that of cyber-security. The first paper to expose the potential issue is by Ian Goodfellow et al. 2014, in ”Intriguing properties of neural networks”, in which it is shown how an image, if altered in the right way, can be completely misclassified by a network trained to classify images. In this thesis I proposed a new method based on a hybrid network, i.e half biological and half artificial, in order to develop a neural network capable of resisting a lot of different adversarial attacks. The biological part is based on the hebbian-anti hebbian neural-dynamics, while the artificial one is based on probability and Boltzmann machines."In recent years, more and more importance is given to interpretability in the ML field. The best known and most famous area in which the interpretability of a neural network is needed is that of cyber-security. The first paper to expose the potential issue is by Ian Goodfellow et al. 2014, in ”Intriguing properties of neural networks”, in which it is shown how an image, if altered in the right way, can be completely misclassified by a network trained to classify images. In this thesis I proposed a new method based on a hybrid network, i.e half biological and half artificial, in order to develop a neural network capable of resisting a lot of different adversarial attacks. The biological part is based on the hebbian-anti hebbian neural-dynamics, while the artificial one is based on probability and Boltzmann machines.

Control of Ca2+ Influx and Calmodulin Activation by SK-Channels in Dendritic Spines

© 2016 Griffith et al. The key trigger for Hebbian synaptic plasticity is influx of Ca2+ into postsynaptic dendritic spines. The magnitude of [Ca2+] increase caused by NMDA-receptor (NMDAR) and voltage-gated Ca2+ -channel (VGCC) activation is thought to determine both the amplitude and direction of synaptic plasticity by differential activation of Ca2+ -sensitive enzymes such as calmodulin. Ca2+ influx is negatively regulated by Ca2+ -activated K+ channels (SK-channels) which are in turn inhibited by neuromodulators such as acetylcholine. However, the precise mechanisms by which SK-channels control the induction of synaptic plasticity remain unclear. Using a 3-dimensional model of Ca2+ and calmodulin dynamics within an idealised, but biophysically-plausible, dendritic spine, we show that SK-channels regulate calmodulin activation specifically during neuron-firing patterns associated with induction of spike timing-dependent plasticity. SK-channel activation and the subsequent reduction in Ca2+ influx through NMDARs and L-type VGCCs results in an order of magnitude decrease in calmodulin (CaM) activation, providing a mechanism for the effective gating of synaptic plasticity induction. This provides a common mechanism for the regulation of synaptic plasticity by neuromodulators

Non-linear synaptic integration on dendrites of striatal medium-spiny neuron : a computational study

Striatum is the main input nucleus of basal ganglia. Medium-spiny neurons (MSNs), the principal neurons of the striatum, receive convergent excitatory inputs from cortex and thalamus, thus “gate” the information flow to the basal ganglia. The activity of MSNs is further modulated by massive inhibition from their neighboring MSNs as well as from GABAergic interneurons. At corticostriatal synapses in MSNs, a potent and reliable spike timing-dependent plasticity (STDP) can be found. It has been suggested this plasticity follows an “anti-Hebbian” learning rule: pre-synaptic signals preceding post-synaptic action potentials (‘pre-post’ paring) induces LTD while post-synaptic action potentials preceding pre-synaptic signals (‘postpre’ paring) leads to LTP. The long-term potentiation (LTP) relies on NMDAR-mediated calcium influx, while the long-term depression relies on L-type calcium channels and endocannabinoid (eCB) dependent signaling pathways. The sign ofSTDP rule at the corticostriatal synapses appears to be influenced by the presence of GABAergic inputs. In addition to the role of synaptic interactions for modulating and controlling plasticity, synaptic interactions can also give rise to “dendritic plateaus” were found in MSNs. Clustered activation ofspines at distal dendrites, within a short temporal window, can evoke a long-lasting plateau potential in MSNs. It is generally assumed that this supra-linear integration could promote spiking in MSNs, however, it has not been clear how dendritic plateaus are controlled by excitatory and inhibitory inputs in MSNs. In this thesis, using biophysically detailed models of MSNs, we explored: (1) the possible mechanisms of GABA in STDP formation, (2) the roles of different NMDAR subunits in STDP formation, and (3) how dendritic plateaus affect the integration of excitatory and inhibitory inputs in MSNs. We found that in brain slices the GABA tightly controlled the polarity of STDP in MSNs, while blocking GABA could reverse the STDP rule from anti-Hebbian learning to Hebbian. Surprisingly, the model predicted that GABA depolarizes the dendrites during the STDP protocols and such depolarizing effects further change the balance between NMDA-mediated calcium and the calcium influx from L-type calcium channels. In “pre-post” parings, the GABA strength pushes the balance towards L-type calcium, thus promoting LTD formation. In contrast, during “post-pre” parings, the presence of GABA pushes the balance more towards NMDAR-mediated calcium, thus favoring LTP formation. Next, we identified the role of NMDAR subunits in LTP formation. The model predicted that the GluN2B subunit could broaden the timing window of LTP. We confirmed the prediction with experiments. At last, we investigated the functional importance of dendritic plateaus in MSNs. The model predicted that dendritic plateaus could enhance neuron-wide integration of excitatory inputs and promote spiking. In contrast, the impact of dendritic inhibition depends on a particular “spatiotemporal” window: the efficacy of dendritic inhibition could be dramatically increased if it is positioned close to the plateau initiation zone and activated within a specific timing window. Intriguingly, the model predicted that such branch-specific inhibition is not due to shutting of GABAARs, but relies on the Magnesium (Mg2+) block of NMDARs. We verified the mechanism with two-photon uncaging of glutamate and single-photon uncaging of GABA. To conclude, we found GABA tightly controlled the direction of STDP in MSNs through depolarizing effects and could effectively suppress the dendritic plateau in MSNs through an NMDAR Mg2+ block dependent mechanism

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care