Neuromorphic Instantiation of Spiking Half-Centered Oscillator Models for Central Pattern Generation.

In both invertebrate and vertebrate animals, small networks called central pattern generators (CPGs) form the building blocks of the neuronal circuits involved in locomotion. Most CPGs contain a simple half-center oscillator (HCO) motif which consists of two neurons, or populations of neurons, connected by reciprocal inhibition. CPGs and HCOs are well characterized neuronal networks and have been extensively modeled at different levels of abstraction. In the past two decades, hardware implementation of spiking CPG and HCO models in neuromorphic hardware has opened up new applications in mobile robotics, computational neuroscience, and neuroprosthetics. Despite their relative simplicity, the parameter space of GPG and HCO models can become exhaustive when considering various neuron models and network topologies. Motivated by computational work in neuroscience that used a brute-force approach to generate a large database of millions of simulations of the heartbeat HCO of the leech, we have started to build a database of spiking chains of multiple HCOs for different neuron model types and network topologies. Here we present preliminary results using the Izhikevich and Morris-Lecar neuron models for single and pairs of HCOs with different inter-HCO coupling schemes.

Responsiveness to inhibitory signals changes as a function of colony size in honeybees (Apis mellifera).

Biological collectives, like honeybee colonies, can make intelligent decisions and robustly adapt to changing conditions via intricate systems of excitatory and inhibitory signals. In this study, we explore the role of behavioural plasticity and its relationship to network size by manipulating honeybee colony exposure to an artificial inhibitory signal. As predicted, inhibition was strongest in large colonies and weakest in small colonies. This is ecologically relevant for honeybees, for which reduced inhibitory effects may increase robustness in small colonies that must maintain a minimum level of foraging and food stores. We discuss evidence for size-dependent plasticity in other types of biological networks.

Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics.

OBJECTIVE: Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. METHODS: Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. RESULTS: We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABAA, GABAC, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. CONCLUSION: Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. SIGNIFICANCE: A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems.

OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. APPROACH: This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. MAIN RESULTS: Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. SIGNIFICANCE: Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.

Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

Sequential steps underlying neuronal plasticity induced by a transient exposure to gabazine.

Periods of intense electrical activity can initiate neuronal plasticity leading to long lasting changes of network properties. By combining multielectrode extracellular recordings with DNA microarrays, we have investigated in rat hippocampal cultures the temporal sequence of events of neuronal plasticity triggered by a transient exposure to the GABA(A) receptor antagonist gabazine (GabT). GabT induced a synchronous bursting pattern of activity. The analysis of electrical activity identified three main phases during neuronal plasticity induced by GabT: (i) immediately after termination of GabT, an early synchronization (E-Sync) of the spontaneous electrical activity appears that progressively decay after 3-6 h. E-Sync is abolished by inhibitors of the ERK1/2 pathway but not by inhibitors of gene transcription; (ii) the evoked response (induced by a single pulse of extracellular electrical stimulation) was maximally potentiated 3-10 h after GabT (M-LTP); and (iii) at 24 h the spontaneous electrical activity became more synchronous (L-Sync). The genome-wide analysis identified three clusters of genes: (i) an early rise of transcription factors (Cluster 1), primarily composed by members of the EGR and Nr4a families, maximally up-regulated 1.5 h after GabT; (ii) a successive up-regulation of some hundred genes, many of which known to be involved in LTP (Cluster 2), 3 h after GabT likely underlying M-LTP. Moreover, in Cluster 2 several genes coding for K(+) channels are down-regulated at 24 h. (iii) Genes in Cluster 3 are up-regulated at 24 h and are involved in cellular homeostasis. This approach allows relating different steps of neuronal plasticity to specific transcriptional profiles.

Characterization of the time course of changes of the evoked electrical activity in a model of a chemically-induced neuronal plasticity.

BACKGROUND: Neuronal plasticity is initiated by transient elevations of neuronal networks activity leading to changes of synaptic properties and providing the basis for memory and learning 1. An increase of electrical activity can be caused by electrical stimulation 2 or by pharmacological manipulations: elevation of extracellular K+ 3, blockage of inhibitory pathways 4 or by an increase of second messengers intracellular concentrations 5. Neuronal plasticity is mediated by several biochemical pathways leading to the modulation of synaptic strength, density of ionic channels and morphological changes of neuronal arborisation 6. On a time scale of a few minutes, neuronal plasticity is mediated by local protein trafficking 7 while, in order to sustain modifications beyond 2-3 h, changes of gene expression are required 8. FINDINGS: In the present manuscript we analysed the time course of changes of the evoked electrical activity during neuronal plasticity and we correlated it with a transcriptional analysis of the underlying changes of gene expression. Our investigation shows that treatment for 30 min. with the GABAA receptor antagonist gabazine (GabT) causes a potentiation of the evoked electrical activity occurring 2-4 hours after GabT and the concomitant up-regulation of 342 genes. Inhibition of the ERK1/2 pathway reduced but did not abolish the potentiation of the evoked response caused by GabT. In fact not all the genes analysed were blocked by ERK1/2 inhibitors. CONCLUSION: These results are in agreement with the notion that neuronal plasticity is mediated by several distinct pathways working in unison.

On the dynamics of the spontaneous activity in neuronal networks.

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABA(A) receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics.

Embryonic stem cell-derived neurons form functional networks in vitro.

Embryonic stem (ES) cells provide a flexible and unlimited source for a variety of neuronal types. Because mature neurons establish neuronal networks very easily, we tested whether ES-derived neurons are capable of generating functional networks and whether these networks, generated in vitro, are capable of processing information. Single-cell electrophysiology with pharmacological antagonists demonstrated the presence of both excitatory and inhibitory synaptic connections. Extracellular recording with planar multielectrode arrays showed that spontaneous bursts of electrical activity are present in ES-derived networks with properties remarkably similar to those of hippocampal neurons. When stimulated with extracellular electrodes, ES-derived neurons fired action potentials, and the evoked electrical activity spread throughout the culture. A statistical analysis indicated that ES-derived networks discriminated between stimuli of different intensity at a single trial level, a key feature for an efficient information processing. Thus, ES-derived neurons provide a novel in vitro strategy to create functional networks with defined computational properties.