Lymnaea stagnalis as model for translational neuroscience research: from pond to bench

The purpose of this review is to illustrate how a reductionistic, but sophisticated, approach based on the use of a simple model system such as the pond snail Lymnaea stagnalis (L. stagnalis), might be useful to address fundamental questions in learning and memory. L. stagnalis, as a model, provides an interesting platform to investigate the dialog between the synapse and the nucleus and vice versa during memory and learning. More importantly, the "molecular actors" of the memory dialogue are well-conserved both across phylogenetic groups and learning paradigms, involving single- or multi-trials, aversion or reward, operant or classical conditioning. At the same time, this model could help to study how, where and when the memory dialog is impaired in stressful conditions and during aging and neurodegeneration in humans and thus offers new insights and targets in order to develop innovative therapies and technology for the treatment of a range of neurological and neurodegenerative disorders

In Vitro Studies of Neuronal Networks and Synaptic Plasticity in Invertebrates and in Mammals Using Multielectrode Arrays

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments

Neuromorphic Computing Applications in Robotics

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, this work implements associative memory with an Unmanned Ground Vehicle (UGV) and neuromorphic hardware, specifically Intel’s Loihi, for an online learning scenario. This system emulates the classic associative learning in rats using the UGV in place of the rats. In specific, it successfully reproduces the fear conditioning with no pretraining procedure or labeled datasets. The UGV is rendered capable of autonomously learning the cause-and-effect relationship of the light stimulus and vibration stimulus and exhibiting a movement response to demonstrate the memorization. Hebbian learning dynamics are used to update the synaptic weights during the associative learning process. The Intel Loihi chip is integrated with this online learning system for processing visual signals with a specialized neural assembly. While processing, the Loihi’s average power usages for computing logic and memory are 30 mW and 29 mW, respectively

Multiple types of control by identified interneurons in a sensory-activated rhythmic motor pattern.

Modulatory interneurons that can drive central pattern generators (CPGs) are considered as good candidates for decision-making roles in rhythmic behaviors. Although the mechanisms by which such neurons activate their target CPGs are known in detail in many systems, their role in the sensory activation of CPG-driven behaviors is poorly understood. In the feeding system of the mollusc Lymnaea, one of the best-studied rhythmical networks, intracellular stimulation of either of two types of neuron, the cerebral ventral 1a (CV1a) and the slow oscillator (SO) cells, leads to robust CPG-driven fictive feeding patterns, suggesting that they might make an important contribution to natural food-activated behavior. In this paper we investigated this contribution using a lip-CNS preparation in which feeding was elicited with a natural chemostimulant rather than intracellular stimulation. We found that despite their CPG-driving capabilities, neither CV1a nor SO were involved in the initial activation of sucrose-evoked fictive feeding, whereas a CPG interneuron, N1M, was active first in almost all preparations. Instead, the two interneurons play important and distinct roles in determining the characteristics of the rhythmic motor output; CV1a by modulating motoneuron burst duration and SO by setting the frequency of the ongoing rhythm. This is an example of a distributed system in which (1) interneurons that drive similar motor patterns when activated artificially contribute differently to the shaping of the motor output when it is evoked by the relevant sensory input, and (2) a CPG rather than a modulatory interneuron type plays the most critical role in initiation of sensory-evoked rhythmic activity

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

Mechanisms of Olfactory Plasticity in Caenorhabditis Elegans

Animals live in constantly changing environments with fluctuating resource availability and hazardous threats. By gathering information from past experiences, individuals modify their behavioral response to adapt to the changing environment, a phenomenon known as “experience-dependent plasticity”. This ability to change is a crucial for survival, and how an organism achieves this adaptive plasticity is a question of much interest. Research in the field has yielded insight into how changes in connectivity within the brain can drive changes in behavior. Understanding the neural mechanisms of plasticity not only satisfies intellectual curiosity, but also provides a basis for understanding pathological conditions that come from excessive or insufficient plasticity. With a well-characterized nervous system, stereotyped behaviors, and an armory of molecular and genetic tools, C. elegans is well-suited for the study of experience-dependent plasticity. Using an olfactory adaptation paradigm in which animals lose attraction to butanone after it is paired with starvation, I here describe neuronal and molecular mechanisms that are associated with and necessary for plasticity in C. elegans. In Chapter 2, I report my findings on circuit mechanisms of butanone adaptation, identifying neurons that are required for adaptation and changes in neuronal activity associated with adaptation. I show that an interneuron is required for adaptive changes in the olfactory sensory neuron. In particular, I show that nuclear translocation of a protein kinase, a process known to be necessary for adaptation, requires activity of the interneuron. This feedback from downstream neurons is transformed into changes in sensory properties. Using pharmacogenetic tools that allowed me to disrupt different parts of the circuit with temporal precision, I identified a group of neurons whose activity is required during adaptation. Finally, I performed functional calcium imaging of animals before and after adaptation, and determined that changes in neuronal responses to butanone can be detected at multiple sites within the circuit, starting as early as the as the sensory neurons. In Chapter 3, I describe the analysis of two genes, a G-protein β subunit and a K+ channel, that have different roles in adaptation. I used whole-genome sequencing and genetic mutations to identify the genes that are required for butanone adaptation, then characterized the odor-specificity of each gene. This analysis provides the basis for future work that should examine the molecular context in which these genes act and the impact they have on circuit mechanisms of adaptation

The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System

Neuromorphic computing, an emerging non-von Neumann computing mimicking the physical structure and signal processing technique of mammalian brains, potentially achieves the same level of computing and power efficiencies of mammalian brains. This chapter will discuss the state-of-the-art research trend on neuromorphic computing with memristors as electronic synapses. Furthermore, a novel three-dimensional (3D) neuromorphic computing architecture combining memristor and monolithic 3D integration technology would be introduced; such computing architecture has capabilities to reduce the system power consumption, provide high connectivity, resolve the routing congestion issues, and offer the massively parallel data processing. Moreover, the design methodology of applying the capacitance formed by the through-silicon vias (TSVs) to generate a membrane potential in 3D neuromorphic computing system would be discussed in this chapter

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods

The Role of Chemical Mechanisms in Neural Computation and Learning

Most computational models of neurons assume that their electrical characteristics are of paramount importance. However, all long-term changes in synaptic efficacy, as well as many short-term effects, are mediated by chemical mechanisms. This technical report explores the interaction between electrical and chemical mechanisms in neural learning and development. Two neural systems that exemplify this interaction are described and modelled. The first is the mechanisms underlying habituation, sensitization, and associative learning in the gill withdrawal reflex circuit in Aplysia, a marine snail. The second is the formation of retinotopic projections in the early visual pathway during embryonic development