Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Global exponential periodicity of a class of neural networks with recent-history distributed delays

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.

Intact imitation of emotional facial actions in autism spectrum conditions.

It has been proposed that there is a core impairment in autism spectrum conditions (ASC) to the mirror neuron system (MNS): If observed actions cannot be mapped onto the motor commands required for performance, higher order sociocognitive functions that involve understanding another person's perspective, such as theory of mind, may be impaired. However, evidence of MNS impairment in ASC is mixed. The present study used an 'automatic imitation' paradigm to assess MNS functioning in adults with ASC and matched controls, when observing emotional facial actions. Participants performed a pre-specified angry or surprised facial action in response to observed angry or surprised facial actions, and the speed of their action was measured with motion tracking equipment. Both the ASC and control groups demonstrated automatic imitation of the facial actions, such that responding was faster when they acted with the same emotional expression that they had observed. There was no difference between the two groups in the magnitude of the effect. These findings suggest that previous apparent demonstrations of impairments to the MNS in ASC may be driven by a lack of visual attention to the stimuli or motor sequencing impairments, and therefore that there is, in fact, no MNS impairment in ASC. We discuss these findings with reference to the literature on MNS functioning and imitation in ASC, as well as theories of the role of the MNS in sociocognitive functioning in typical development.

Dynamic knowledge representation in connectionist systems

One of the most pervading concepts underlying computational models of information processing in the brain is linear input integration of rate coded uni-variate information by neurons. After a suitable learning process this results in neuronal structures that statically represent knowledge as a vector of real valued synaptic weights. Although this general framework has contributed to the many successes of connectionism, in this paper we argue that for all but the most basic of cognitive processes, a more complex, multi-variate dynamic neural coding mechanism is required - knowledge should not be spacially bound to a particular neuron or group of neurons. We conclude the paper with discussion of a simple experiment that illustrates dynamic knowledge representation in a spiking neuron connectionist system.

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

Perspex machine X: software development

The Perspex Machine arose from the unification of computation with geometry. We now report significant redevelopment of both a partial C compiler that generates perspex programs and of a Graphical User Interface (GUI). The compiler is constructed with standard compiler-generator tools and produces both an explicit parse tree for C and an Abstract Syntax Tree (AST) that is better suited to code generation. The GUI uses a hash table and a simpler software architecture to achieve an order of magnitude speed up in processing and, consequently, an order of magnitude increase in the number of perspexes that can be manipulated in real time (now 6,000). Two perspex-machine simulators are provided, one using trans-floating-point arithmetic and the other using transrational arithmetic. All of the software described here is available on the world wide web. The compiler generates code in the neural model of the perspex. At each branch point it uses a jumper to return control to the main fibre. This has the effect of pruning out an exponentially increasing number of branching fibres, thereby greatly increasing the efficiency of perspex programs as measured by the number of neurons required to implement an algorithm. The jumpers are placed at unit distance from the main fibre and form a geometrical structure analogous to a myelin sheath in a biological neuron. Both the perspex jumper-sheath and the biological myelin-sheath share the computational function of preventing cross-over of signals to neurons that lie close to an axon. This is an example of convergence driven by similar geometrical and computational constraints in perspex and biological neurons.

Robotic movement elicits automatic imitation

Recent behavioural and neuroimaging studies have found that observation of human movement, but not of robotic movement, gives rise to visuomotor priming. This implies that the 'mirror neuron' or 'action observation–execution matching' system in the premotor and parietal cortices is entirely unresponsive to robotic movement. The present study investigated this hypothesis using an 'automatic imitation' stimulus–response compatibility procedure. Participants were required to perform a prespecified movement (e.g. opening their hand) on presentation of a human or robotic hand in the terminal posture of a compatible movement (opened) or an incompatible movement (closed). Both the human and the robotic stimuli elicited automatic imitation; the prespecified action was initiated faster when it was cued by the compatible movement stimulus than when it was cued by the incompatible movement stimulus. However, even when the human and robotic stimuli were of comparable size, colour and brightness, the human hand had a stronger effect on performance. These results suggest that effector shape is sufficient to allow the action observation–matching system to distinguish human from robotic movement. They also indicate, as one would expect if this system develops through learning, that to varying degrees both human and robotic action can be 'simulated' by the premotor and parietal cortices.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Measuring the effects of manipulating stimulus presentation time on sensorimotor alpha and low beta reactivity during hand movement observation

The present study addresses three methodological questions that have been ignored in previous research on EEG indices of the human mirror neuron system (hMNS), particularly in regard to autistic individuals. The first question regards how to elicit the EEG indexed hMNS during movement observation: Is hMNS activation best elicited using long stimulus presentations or multiple short repetitions? The second question regards what EEG sensorimotor frequency bands reflect sensorimotor reactivity during hand movement observation? The third question regards how widespread is the EEG reactivity over the sensorimotor cortex during movement observation? The present study explored sensorimotor alpha and low beta reactivity during hand movement versus static hand or bouncing balls observation and compared two experimental protocols (long exposure vs. multiple repetitions) in the same participants. Results using the multiple repetitions protocol indicated a greater low beta desynchronisation over the sensorimotor cortex during hand movement compared to static hand and bouncing balls observation. This result was not achieved using the long exposure protocol. Therefore, the present study suggests that the multiple repetitions protocol is a more robust protocol to use when exploring the sensorimotor reactivity induced by hand action observation. In addition, sensorimotor low beta desynchronisation was differently modulated during hand movement, static hand and bouncing balls observation (non-biological motion) while it was not the case for sensorimotor alpha and that suggest that low beta may be a more sensitive index of hMNS activation during biological motion observation. In conclusion the present study indicates that sensorimotor reactivity of low beta during hand movement observation was found to be more widespread over the sensorimotor cortex than previously thought.

EEG activation differences in the pre-motor cortex and supplementary motor area between normal individuals with high and low traits of autism

The human mirror neuron system (hMNS) is believed to provide a basic mechanism for social cognition. Event-related desynchronization (ERD) in alpha (8–12 Hz) and low beta band (12–20 Hz) over sensori-motor cortex has been suggested to index mirror neurons' activity. We tested whether autistic traits revealed by high and low scores on the Autistic Quotient (AQ) in the normal population are linked to variations in the electroencephalogram (EEG) over motor, pre-motor cortex and supplementary motor area (SMA) during action observation. Results revealed that in the low AQ group, the pre-motor cortex and SMA were more active during hand action than static hand observation whereas in the high AQ group the same areas were active both during static and hand action observation. In fact participants with high traits of autism showed greater low beta ERD while observing the static hand than those with low traits and this low beta ERD was not significantly different when they watched hand actions. Over primary motor cortex, the classical alpha and low beta ERD during hand actions relative to static hand observation was found across all participants. These findings suggest that the observation–execution matching system works differently according to the degree of autism traits in the normal population and that this is differentiated in terms of the EEG according to scalp site and bandwidth.

Reduced cortico-motor facilitation in a normal sample with high traits of autism

Recent research in social neuroscience proposes a link between mirror neuron system (MNS) and social cognition. The MNS has been proposed to be the neural mechanism underlying action recognition and intention understanding and more broadly social cognition. Pre-motor MNS has been suggested to modulate the motor cortex during action observation. This modulation results in an enhanced cortico-motor excitability reflected in increased motor evoked potentials (MEPs) at the muscle of interest during action observation. Anomalous MNS activity has been reported in the autistic population whose social skills are notably impaired. It is still an open question whether traits of autism in the normal population are linked to the MNS functioning. We measured TMS-induced MEPs in normal individuals with high and low traits of autism as measured by the autistic quotient (AQ), while observing videos of hand or mouth actions, static images of a hand or mouth or a blank screen. No differences were observed between the two while they observed a blank screen. However participants with low traits of autism showed significantly greater MEP amplitudes during observation of hand/mouth actions relative to static hand/mouth stimuli. In contrast, participants with high traits of autism did not show such a MEP amplitude difference between observation of actions and static stimuli. These results are discussed with reference to MNS functioning.

Localization of calcitonin receptor-like receptor (CLR) and receptor activity-modifying protein 1 (RAMP1) in human gastrointestinal tract

Calcitonin gene-related peptide (CGRP) exerts its diverse effects on vasodilation, nociception, secretion, and motor function through a heterodimeric receptor comprising of calcitonin receptor-like receptor (CLR) and receptor activity-modifying protein 1 (RAMP1). Despite the importance of CLR.RAMP1 in human disease, little is known about its distribution in the human gastrointestinal (GI) tract, where it participates in inflammation and pain. In this study, we determined that CLR and RAMP1 mRNAs are expressed in normal human stomach, ileum and colon by RT-PCR. We next characterized antibodies that we generated to rat CLR and RAMP1 in transfected HEK cells. Having characterized these antibodies in vitro, we then localized CLR-, RAMP1-, CGRP- and intermedin-immunoreactivity (IMD-IR) in various human GI segments. In the stomach, nerve bundles in the myenteric plexus and nerve fibers throughout the circular and longitudinal muscle had prominent CLR-IR. In the proximal colon and ileum, CLR was found in nerve varicosities of the myenteric plexus and surrounding submucosal neurons. Interestingly, CGRP expressing fibers did not co-localize, but were in close proximity to CLR. However, CLR and RAMP1, the two subunits of a functional CGRP receptor were clearly localized in myenteric plexus, where they may form functional cell-surface receptors. IMD, another member of calcitonin peptide family was also found in close proximity to CLR, and like CGRP, did not co-localize with either CLR or RAMP1 receptors. Thus, CGRP and IMD appear to be released locally, where they can mediate their effect on their receptors regulating diverse functions such as inflammation, pain and motility.

Expression and function of the bile acid receptor GpBAR1 (TGR5) in the murine enteric nervous system

BACKGROUND: Bile acids (BAs) regulate cells by activating nuclear and membrane-bound receptors. G protein coupled bile acid receptor 1 (GpBAR1) is a membrane-bound G-protein-coupled receptor that can mediate the rapid, transcription-independent actions of BAs. Although BAs have well-known actions on motility and secretion, nothing is known about the localization and function of GpBAR1 in the gastrointestinal tract. METHODS: We generated an antibody to the C-terminus of human GpBAR1, and characterized the antibody by immunofluorescence and Western blotting of HEK293-GpBAR1-GFP cells. We localized GpBAR1 immunoreactivity (IR) and mRNA in the mouse intestine, and determined the mechanism by which BAs activate GpBAR1 to regulate intestinal motility. KEY RESULTS: The GpBAR1 antibody specifically detected GpBAR1-GFP at the plasma membrane of HEK293 cells, and interacted with proteins corresponding in mass to the GpBAR1-GFP fusion protein. GpBAR1-IR and mRNA were detected in enteric ganglia of the mouse stomach and small and large intestine, and in the muscularis externa and mucosa of the small intestine. Within the myenteric plexus of the intestine, GpBAR1-IR was localized to approximately 50% of all neurons and to >80% of inhibitory motor neurons and descending interneurons expressing nitric oxide synthase. Deoxycholic acid, a GpBAR1 agonist, caused a rapid and sustained inhibition of spontaneous phasic activity of isolated segments of ileum and colon by a neurogenic, cholinergic and nitrergic mechanism, and delayed gastrointestinal transit. CONCLUSIONS & INFERENCES: G protein coupled bile acid receptor 1 is unexpectedly expressed in enteric neurons. Bile acids activate GpBAR1 on inhibitory motor neurons to release nitric oxide and suppress motility, revealing a novel mechanism for the actions of BAs on intestinal motility.

Localization of calcitonin receptor-like receptor and receptor activity modifying protein 1 in enteric neurons, dorsal root ganglia, and the spinal cord of the rat

Calcitonin receptor-like receptor (CLR) and receptor activity modifying protein 1 (RAMP1) comprise a receptor for calcitonin gene related peptide (CGRP) and intermedin. Although CGRP is widely expressed in the nervous system, less is known about the localization of CLR and RAMP1. To localize these proteins, we raised antibodies to CLR and RAMP1. Antibodies specifically interacted with CLR and RAMP1 in HEK cells coexpressing rat CLR and RAMP1, determined by Western blotting and immunofluorescence. Fluorescent CGRP specifically bound to the surface of these cells and CGRP, CLR, and RAMP1 internalized into the same endosomes. CLR was prominently localized in nerve fibers of the myenteric and submucosal plexuses, muscularis externa and lamina propria of the gastrointestinal tract, and in the dorsal horn of the spinal cord of rats. CLR was detected at low levels in the soma of enteric, dorsal root ganglia (DRG), and spinal neurons. RAMP1 was also localized to enteric and DRG neurons and the dorsal horn. CLR and RAMP1 were detected in perivascular nerves and arterial smooth muscle. Nerve fibers containing CGRP and intermedin were closely associated with CLR fibers in the gastrointestinal tract and dorsal horn, and CGRP and CLR colocalized in DRG neurons. Thus, CLR and RAMP1 may mediate the effects of CGRP and intermedin in the nervous system. However, mRNA encoding RAMP2 and RAMP3 was also detected in the gastrointestinal tract, DRG, and dorsal horn, suggesting that CLR may associate with other RAMPs in these tissues to form a receptor for additional peptides such as adrenomedullin.

NeMo: a platform for neural modelling of spiking neurons using GPUs

Simulating spiking neural networks is of great interest to scientists wanting to model the functioning of the brain. However, large-scale models are expensive to simulate due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present NeMo, a platform for such simulations which achieves high performance through the use of highly parallel commodity hardware in the form of graphics processing units (GPUs). NeMo makes use of the Izhikevich neuron model which provides a range of realistic spiking dynamics while being computationally efficient. Our GPU kernel can deliver up to 400 million spikes per second. This corresponds to a real-time simulation of around 40 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.