1,081 research outputs found
A Bayesian model for identifying hierarchically organised states in neural population activity
Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states. These cortical states vary on multiple time-scales and also across areas and layers of the neocortex. To understand information processing in cortical circuits, we need to understand the statistical structure of internal states and their interaction with sensory inputs. Here, we present a statistical model for extracting hierarchically organized neural population states from multi-channel recordings of neural spiking activity. We model population states using a hidden Markov decision tree with state-dependent tuning parameters and a generalized linear observation model. Using variational Bayesian inference, we estimate the posterior distribution over parameters from population recordings of neural spike trains. On simulated data, we show that we can identify the underlying sequence of population states over time and reconstruct the ground truth parameters. Using extracellular population recordings from visual cortex, we find that a model with two levels of population states outperforms a generalized linear model which does not include state-dependence, as well as models which only including a binary state. Finally, modelling of state-dependence via our model also improves the accuracy with which sensory stimuli can be decoded from the population response
The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome.
The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods-and data scales-hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system-a multidimensional, generative network-that renders its richness tractable within future models of its function in health and disease
The autonomic brain: multi-dimensional generative hierarchical modelling of the autonomic connectome
The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods — and data scales — hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system — a multidimensional, generative network — that renders its richness tractable within future models of its function in health and disease
Hierarchical Influences on Human Decision-Making
Deciding how to act is complicated because people often hold simultaneous intentions to meet multiple goals. These many goals can be arranged in a hierarchy of goals and sub-goals, and a hierarchy of behaviours can be established to attain them. The hierarchical structure of human behaviour is well established, but the precise form of that hierarchical structure remains unclear. Further, we do not know whether and how this hierarchical organisation of action influences the cognitive processes of deciding between candidate actions. This thesis aims to address these two open questions.
In Chapter 2, I tackle the first of these two questions. Using behavioural experiments in combination with hierarchical reinforcement learning models of behaviour, I demonstrate that people can learn entirely novel sequences of action without practice, and that this ability requires a hierarchical organisation of action built from two distinct operations. First, the brain must sequence low-level components into higher-level routines of action. Second, the brain must have a method of abstracting the relational structure of a sequence away from its content. In sum, this chapter provides evidence for a theoretical framework which can be used to understand hierarchically structured action more deeply.
In Chapters 3 and 4, I tackle the second question: does hierarchical structure influence decision-making? I begin (in Chapter 3) by investigating how hierarchical structure and self-efficacy interact to influence choice between candidate actions. I find that higher level actions are associated with lesser self-efficacy and therefore a lesser willingness to commit to them. This effect arises not only because higher-level actions are more difficult to carry out due to their length, but also because the restrictions that they place on future choices represent a cost. I then (in Chapter 4) investigate whether there are any subjective biases in how outcomes at high or low hierarchical levels are evaluated. I find no overall subjective bias in the evaluation of such outcomes, but I find that social context can prompt strong biases to weight evaluation of outcomes according to their hierarchical level. In sum, I find that hierarchical structure can and does influence decision-making, and I provide evidence for two distinct processes that play a part in this.
These findings establish both a novel theoretical framework for future investigations of hierarchically structured action, and a novel set of interactions between the structure of behaviour and how people make action decisions
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Linking fast and slow: the case for generative models
A pervasive challenge in neuroscience is testing whether neuronal
connectivity changes over time due to specific causes, such as stimuli, events,
or clinical interventions. Recent hardware innovations and falling data storage
costs enable longer, more naturalistic neuronal recordings. The implicit
opportunity for understanding the self-organised brain calls for new analysis
methods that link temporal scales: from the order of milliseconds over which
neuronal dynamics evolve, to the order of minutes, days or even years over
which experimental observations unfold. This review article demonstrates how
hierarchical generative models and Bayesian inference help to characterise
neuronal activity across different time scales. Crucially, these methods go
beyond describing statistical associations among observations and enable
inference about underlying mechanisms. We offer an overview of fundamental
concepts in state-space modeling and suggest a taxonomy for these methods.
Additionally, we introduce key mathematical principles that underscore a
separation of temporal scales, such as the slaving principle, and review
Bayesian methods that are being used to test hypotheses about the brain with
multi-scale data. We hope that this review will serve as a useful primer for
experimental and computational neuroscientists on the state of the art and
current directions of travel in the complex systems modelling literature.Comment: 20 pages, 5 figure
Recommended from our members
From symbols to icons: the return of resemblance in the cognitive neuroscience revolution
We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above" - that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research
- …