2,660 research outputs found
Sensitivity and stability: A signal propagation sweet spot in a sheet of recurrent centre crossing neurons
No full textIn this paper we demonstrate that signal propagation across a laminar sheet of recurrent neurons is maximised when two conditions are met. First, neurons must be in the so-called centre crossing configuration. Second, the network’s topology and weights must be such that the network comprises strongly coupled nodes, yet lies within the weakly coupled regime. We develop tools from linear stability analysis with which to describe this regime, and use them to examine the apparent tension between the sensitivity and instability of centre crossing networks
Spatially embedded dynamics and complexity
No full textTo gain a deeper understanding of the impact of spatial embedding on the dynamics of complex systems we employ a measure of interaction complexity developed within neuroscience using the tools of statistical information theory. We apply this measure to a set of simple network models embedded within Euclidean spaces of varying dimensionality in order to characterise the way in which the constraints imposed by low-dimensional spatial embedding contribute to the dynamics (rather than the structure) of complex systems. We demonstrate that strong spatial constraints encourage high intrinsic complexity, and discuss the implications for complex systems in general
Sensitivity and stability: A signal propagation sweet spot in a sheet of recurrent centre crossing neurons
No full textIn this paper we demonstrate that signal propagation across a laminar sheet of recurrent neurons is maximised when two conditions are met. First, neurons must be in the so-called centre crossing configuration. Second, the network’s topology and weights must be such that the network comprises strongly coupled nodes, yet lies within the weakly coupled regime. We develop tools from linear stability analysis with which to describe this regime, and use them to examine the apparent tension between the sensitivity and instability of centre crossing networks
Embracing the tyranny of distance: Space as an enabling constraint
No full textArchitectural design is typically limited by the constraints imposed by physical space. If and when opportunities to attenuate or extinguish these limits arise, should they be seized? Here it is argued that the limiting influence of spatial embedding should not be regarded as a frustrating "tyranny" to be escaped wherever possible, but as a welcome enabling constraint to be leveraged. Examples from the natural world are presented, and an appeal is made to some recent results on complex systems and measures of interaction complexity
Emergent associative memory as a local organising principle for global adaptation in adaptive networks
No full textComplex adaptive systems composed of self-interested agents can in some circumstances self-organise into structures that enhance global adaptation or efficiency. However, the general conditions for such an outcome are poorly understood. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, generalisation and optimisation, are well-understood. While such global functions within a single agent or organism may arise from mechanisms (e.g., Hebbian learning) that were selected for this purpose, agents in a multi-agent system have no obvious reason to produce such global behaviours when acting from individual interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we use an adaptive network model in which agents can modify their behaviours (states) but also their interactions with other agents (network topology). We show that when self-interested agents can modify how they are affected by other agents then, in adapting these inter- agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. When the agents adapt their behaviours relatively quickly, and their relationships with other agents relatively slowly, we find that the overall network dynamics are modified to find better adapted states more reliably. This separation in timescales causes the state dynamics to spend most of their time at attractors. Thus, the network develops an associative memory that amplifies a subset of its own attractor states. This self-organised modification to the network dynamics enhances its ability to resolve conflicts between agents. Moreover, we show that the system is not merely ‘recalling’ high quality states that have been previously visited, but ‘predicting’ their location by generalising over local attractor states that have already been visited. Thus, globally adaptive behaviours can emerge from self-organising adaptive networks that follow organisational principles familiar in connectionist models of organismic learning
Neural complexity and structural connectivity
No full textTononi et al. Proc. Natl. Acad. Sci. U.S.A. 91, 5033 1994 proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond.We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model
A systemic analysis of the ideas immanent in neuromodulation
No full textThis thesis focuses on the phenomena of neuromodulation — these are a set of diffuse chemical pathways that modify the properties of neurons and act in concert with the more traditional pathways mediated by synapses (neurotransmission). There is a growing opinion within neuroscience that such processes constitute a radical challenge to the centrality of neurotransmission in our understanding of the nervous system. This thesis is an attempt to understand how the idea of neuromodulation should impact on the canonical ideas of information processing in the nervous system.The first goal of this thesis has been to systematise the ideas immanent in neuromodulation such that they are amenable to investigation through both simulation and analytical techniques. Specifically, the physiological properties of neuromodulation are distinct from those traditionally associated with neurotransmission. Hence, a first contribution has been to develop a principled but minimal mechanistic description of neuromodulation. Furthermore, neuromodulators are thought to underpin a distinct set of functional roles. Hence, a second contribution has been to define these in terms of a set of dynamical motifs. Subsequently the major goal of thesis has been to investigate the relationship between the mechanistic properties of neuromodulation and their dynamical motifs in order to understand whether the physiological properties of neuromodulation predispose them toward their functional roles?This thesis uses both simulation and analytical techniques to explore this question. The most significant progress, however, is made through the application of dynamical systems analysis. These results demonstrate that there is a strong relationship between the mechanistic and dynamical abstractions of neuromodulation developed in this thesis. In particular they suggest that in contrast to neurotransmission, neuromodulatory pathways are predisposed toward bifurcating a system’s dynamics. Consequently, this thesis argues that a true canonical picture of the dynamics of the nervous system requires an appreciation of the interplay between the properties of neurotransmission and the properties immanent in the idea of neuromodulation
Neural complexity: a graph theoretic interpretation
No full textOne of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end Tononi et al. [Proc. Nat. Acad. Sci. USA 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system's dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns et al. [Cereb. Cortex 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia et al. [Phys. Rev. E 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular we explicitly establish a dependency of neural complexity on cyclic graph motifs
Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks
No full textThe natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks
sj-docx-1-aop-10.1177_10600280231179999 – Supplemental material for Physical and Chemical Compatibility of Medications Commonly Used in Critically Ill Patients With Balanced Crystalloids: A Systematic Review
No full textSupplemental material, sj-docx-1-aop-10.1177_10600280231179999 for Physical and Chemical Compatibility of Medications Commonly Used in Critically Ill Patients With Balanced Crystalloids: A Systematic Review by Christopher T. Buckley, Julie E. Farrar, Mary Schleicher, Joanna L. Stollings, Abhijit Duggal and Seth R. Bauer in Annals of Pharmacotherapy</p
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