1,721,739 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
Embracing the tyranny of distance: Space as an enabling constraint
Full text linkArchitectural 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
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
Design for an individual: connectionist approaches to the evolutionary transitions in individuality
No full textThe truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.<br/
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
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
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
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