Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

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

Modulation of feeding and its role in appetitive memory consolidation in Lymnaea stagnalis

Feeding in the pond snail (Lymnaea stagnalis) is heavily regulated by modulatory neurons throughout the brain. Following appetitive conditioning many of these identified neurons show changes in electrophysiological properties that contribute to the conditioned feeding response. The aim of this thesis was to further characterise modulatory feeding neurons and the neural plasticity that occurs within them during appetitive memory consolidation. Previous research had shown that the cerebral giant cell (CGC) is persistently depolarised 16-24 hours following appetitive conditioning and onwards. A Hodgkin-Huxley model of the CGC predicted this nonsynaptic plasticity to occur via an increase in the maximum conductance of the persistent sodium current (INaP), delayed rectifier current (IK) and high voltage activated calcium current (IHVA). To test this hypothesis these three modelled conductances were introduced into the membrane of the CGC of untrained snails, using dynamic clamp. Little effect on electrophysiological properties was initially seen due to an unexpectedly large cell surface area. To correct for this underestimation the response of the membrane to sine wave current injection was used to calculate the total cell surface area prior to dynamic clamp. When correctly scaled conductances were applied to the cell, there was still no significant change in the resting membrane potential (RMP). This suggests that the changes in ionic conductances causing CGC nonsynaptic plasticity during long term memory are more complex than previously realised and may involve other membrane conductances. A simpler attempt to depolarise the CGC with pattern clamp was able to cause significant RMP depolarisation, but also led to ectopic spike initiation in the proximal axon. Sub-optimal classical conditioning of Lymnaea with dilute sucrose (US) results in memory expression which is temporarily undetectable both at 30 minutes and 2 hours after training. The neural mechanisms involved during these memory lapses are unknown. Therefore, two key modulatory neurons, the CGC and the pleural buccal interneuron (PlB) were recorded during lapse and non-lapse time points after sub-optimal conditioning. These neurons were targeted as they can upregulate and downregulate the entire feeding system, and modulatory cells have previously been shown to play a role in memory consolidation. When conditioned preparations were compared to unconditioned preparations there was no significant difference in spontaneous firing frequency or CS-induced firing frequency in the CGC or PlB. This suggests that neither of these identified modulatory neurons play a role at 1 hr (non-lapse), 2 hr (lapse), and 3 hr (non-lapse) following sub-optimal appetitive conditioning. To better understand the neural mechanisms of appetitive memory storage it is also important to characterise the feeding network. Particularly little is known about the control of the modulatory feeding neurons which project to the buccal ganglia, where the feeding CPG and motoneurons are located. Therefore, a search was made for a neuron presynaptic to modulatory projection cells. Such a cell was identified in the parietal ganglion and was termed parietal dorsal 4 (PD4). Intracellular dye filling revealed an expansive morphology with a large axon projecting through the pleural ganglion, cerebral ganglion, and lastly the buccal ganglion. The axon was also seen to branch extensively and cross both the cerebral and buccal commissure. Furthermore, a peripheral projection was observed from a nerve of the visceral ganglion. This extensive morphology makes this neuron an ideal candidate to regulate control of the feeding system as a whole. When artificially activated by depolarisation PD4 caused monosynaptic excitation of both the ipsilateral and contralateral CGC. Delayed excitation was also observed on the ipsilateral CV1a and inhibition on the ipsilateral PlB. However, no direct connection was found between PD4 and identified dorsal buccal feeding neurons. Depolarisation of PD4 could occasionally cause feeding cycles to occur but could also inhibit spontaneous fictive feeding. This differential effect is likely due to polysynaptic depolarisation of the inhibitory CPG neuron N3t, which becomes dominant when there are high frequency action potentials in the CGC. In a semi-intact preparation PD4 did not respond to sucrose applied to the lips, demonstrating it is not involved in the unconditioned feeding response. PD4 was depolarised to threshold by extracellular stimulation of multiple nerves including the lip, tentacle, anal/intestinal and parietal nerves. Activation of PD4 also caused inhibition of the caudodorsal cells (CDCs) and excitation of the ring neuron (RN), suggesting that the neuron may be involved in controlling modulation of the feeding network during egg laying behaviour. In summary, this thesis examines important conceptual gaps in the understanding of appetitive memory consolidation in Lymnaea. It also provides the characterisation of an entirely new cell with widespread influence over the feeding system

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches

Differentiating noise and modulators in artificial neural networks

Research in Computational Neural Networks is currently taking place at many different levels; from coarse-grain symbolic models to fine-grain representations of neurons and cell processes. One feature that the different approaches share, is that they are all in relative infancy. Thus, most research concentrates on gross aspects of neural communication and methods of computational simulation. Recently, some clues have been found which point to more subtle mechanisms underlying the information processing capability of neural 'nodes'. These clues are the improvement in network operation by the injection of random noise; and the neurobiological finding that neuropeptides may exist as slower Signal transmission channels between neurons. This study concerns the difference between random noise injection, and directed, low-level, activity injections which are postulated to be produced by neuromodulators such as neuropeptides. The findings of this study are that random noise does, indeed, enhance the operation of coarse-grain neural models; and that a 'neuropeptidergic' analogue also enhances operation; but to a different extent, and probably through a different mechanism. Further testing of a medium-grain computer model gives some indication of how a neuropeptidergic modulation might affect real neurons, by extending the time-course of the activation of the neuron. This appears to be a similar mechanism to that postulated for the coarse-grain 'neuropeptidergic' simulation model. Given these findings, is it possible that signal transmission in real nervous systems assume these mechanisms? If so, it may be possible that a process of concurrent propagation, through different signal channels, also occurs in real nervous systems, making the nervous system much more complex than current models allow

Human Brain/Cloud Interface

The Internet comprises a decentralized global system that serves humanity’s collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a “human brain/cloud interface” (“B/CI”), would be based on technologies referred to here as “neuralnanorobotics.” Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain’s ∼86 × 109 neurons and ∼2 × 1014 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood–brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 1016 bits per second of synaptically processed and encoded human–brain electrical information via auxiliary nanorobotic fiber optics (30 cm3) with the capacity to handle up to 1018 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as “transparent shadowing” (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family

Using evolutionary artificial neural networks to design hierarchical animat nervous systems.

The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently

The environmental enrichment model revisited: A translatable paradigm to study the stress of our modern lifestyle

Mounting evidence shows that physical activity, social interaction and sensorimotor stimulation provided by environmental enrichment (EE) exert several neurobehavioural effects traditionally interpreted as enhancements relative to standard housing (SH) conditions. However, this evidence rather indicates that SH induces many deficits, which could be ameliorated by exposing animals to an environment vaguely mimicking some features of their wild habitat. Rearing rodents in social isolation (SI) can aggravate such deficits, which can be restored by SH or EE. It is not surprising, therefore, that most preclinical stress models have included severe and unnatural stressors to produce a stress response prominent enough to be distinguishable from SH or SI—frequently used as control groups. Although current stress models induce a stress-related phenotype, they may fail to represent the stress of our urban lifestyle characterized by SI, poor housing and working environments, sedentarism, obesity and limited access to recreational activities and exercise. In the following review, we discuss the stress of living in urban areas and how exposures to and performing activities in green environments are stress relievers. Based on the commonalities between human and animal EE, we discuss how models of housing conditions (e.g., SI–SH– EE) could be adapted to study the stress of our modern lifestyle. The housing conditions model might be easy to implement and replicate leading to more translational results. It may also contribute to accomplishing some ethical commitments by promoting the refinement of procedures to model stress, diminishing animal suffering, enhancing animal welfare and eventually reducing the number of experimental animals needed.Universidad de Costa Rica/[723-B9-197]/UCR/Costa RicaUniversidad de Costa Rica/[837-B8-123]/UCR/Costa RicaUniversidad de Costa Rica/[837-B7-603]/UCR/Costa RicaUniversidad de Costa Rica/[837-C0-606]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Instituto de Investigaciones Psicológicas (IIP)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Neurociencias (CIN)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto de Investigaciones en Salud (INISA

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Optimisation de réseaux de neurones à décharges avec contraintes matérielles pour processeur neuromorphique

Les modèles informatiques basés sur l'apprentissage machine ont démarré la seconde révolution de l'intelligence artificielle. Capables d'atteindre des performances que l'on crut inimaginables au préalable, ces modèles semblent devenir partie courante dans plusieurs domaines. La face cachée de ceux-ci est que l'énergie consommée pour l'apprentissage, et l'utilisation de ces techniques, est colossale. La dernière décennie a été marquée par l'arrivée de plusieurs processeurs neuromorphiques pouvant simuler des réseaux de neurones avec une faible consommation d'énergie. Ces processeurs offrent une alternative aux conventionnelles cartes graphiques qui demeurent à ce jour essentielles au domaine. Ces processeurs sont capables de réduire la consommation d'énergie en utilisant un modèle de neurone événementiel, plus communément appelé neurone à décharge. Ce type de neurone est fondamentalement différent du modèle classique, et possède un aspect temporel important. Les méthodes, algorithmes et outils développés pour le modèle de neurone classique ne sont pas adaptés aux neurones à décharges. Cette thèse de doctorat décrit plusieurs approches fondamentales, dédiées à la création de processeurs neuromorphiques analogiques, qui permettent de pallier l'écart existant entre les systèmes à base de neurones conventionnels et à décharges. Dans un premier temps, nous présentons une nouvelle règle de plasticité synaptique permettant l'apprentissage non supervisé des réseaux de neurones récurrents utilisant ce nouveau type de neurone. Puis, nous proposons deux nouvelles méthodes pour la conception des topologies de ce même type de réseau. Finalement, nous améliorons les techniques d'apprentissage supervisé en augmentant la capacité de mémoire de réseaux récurrents. Les éléments de cette thèse marient l'inspiration biologique du cerveau, l'ingénierie neuromorphique et l'informatique fondamentale pour permettre d'optimiser les réseaux de neurones pouvant fonctionner sur des processeurs neuromorphiques analogiques