Secondary

Prof Greg Stuart with Aniket Dhawan, the Canberra regional finalist for the 2017 A&NZBBC.

Brain research as a potential career

We help introduce secondary students to brain research, with the aim of sparking their interest and encouraging them to pursue a career in neuroscience.

We host the annual Australian & New Zealand Brain Bee Challenge (A&NZBBC) and facilitate work experience for secondary students with our collaborating organisations. We are also working with teachers to develop ways to increase brain research in the syllabus.

Secondary school teachers interested in providing opportunities for their students to learn more about the brain are encouraged to contact us.


Primary

Brain Awareness Week – creative art competition

Every year as part of our Brain Awareness Week activities, we hold an art competition for primary school students around Australia. The competition encourages students to think about the brain and its functions, and also helps teachers introduce brain science to their students.

Previous art competition winners

2019 art competition winners

2018 art competition winners

2017 art competition winners

 


Education and Training

Educating our next generation

Supporting our next generation of brain researchers and sparking their desire to discover how the brain functions is at the heart of our Education Program.

We bring together neuroscientists and students, engaging them in active learning and providing resources with the latest information on how the brain functions.

We encourage primary school students to explore the brain through art with our annual creative art competition and we also take part in Brain Awareness Week each March.

Secondary students are introduced to brain research through the Australian & New Zealand Brain Bee Challenge, with the aim of sparking their interest and encouraging them to pursue a career in neuroscience.

For Early Career Researchers (ECRs), including PhD students, we offer professional support, development and mentoring. Supporting new brain researchers is critical for retaining their scientific talents and ensuring future excellence in Australian brain research.


Decision

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Understanding decision-making

Every day we make decisions based on information captured by multiple senses, and on our internal goals. For example, when crossing the road we need to decide how to coordinate our movements to reach the destination safely and at the right time. To achieve this goal we make a guess about where the car is likely to be while we cross the road, given what we see of its trajectory and the sound it produces. Our estimate of the car’s future location is inevitably imperfect, and is combined with our experience of how fast cars encountered in the past have been travelling. For example, we may know that the car is likely to slow down if we are at a pedestrian crossing. This uncertainty places the problem of estimating the future position of the car and how and when we should move to cross the road in a statistical setting. A practical way to conceive of the problem is that the brain uses 'rules of thumb' that approximate statistical methods of incorporating prior knowledge and uncertainty (Bayesian theory). The aim of Centre research is to determine these rules of thumb and how they can be implemented in neural circuitry.


Prediction

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Testing predictive coding models

Our Centre research program on Prediction is based on recent demonstrations that the brain does not simply respond to external events, but rather compares sensory information against predictions based on internal representations (memories). The difference between predictions and external inputs ('prediction errors') are used to initiate adaptive behaviours. For example, when you are crossing the street, the observed trajectory of an oncoming car (sensory input) allows your brain to predict your movement relative to that of the car, based on past experience (memory) of trajectories of moving vehicles. Appropriate movements are then initiated to avoid collision. The computational load on the brain is thus reduced, from all-encompassing sensory perception to the more tractable problem of comparing sensory inputs to internally stored predictions. This 'predictive error' framework can be used to unify apparently diverse behavioural data, from low-level functions such as control of eye movements, through to attention and high-level functions such as decision.

Most of our physiologically based studies of Prediction employ the well-established fear conditioning paradigm in rodents. This paradigm is adapted to test predictive coding models by manipulating statistical regularities in the properties or timing of conditioned and unconditioned stimuli. For example, the probabilities have been changed so that only 50 per cent of the tones (conditioned stimuli) are paired with the shocks (unconditioned stimuli). Thus animals can modify their expectation of a shock and respond adaptively.

In other studies, we are using eye movement-based prediction tasks in non-human primates trained to 'intercept' visual targets based on their prior history of movement, with or without concurrent multisensory cues (e.g. an auditory stimulus that predicts stimulus acceleration or deceleration with different probabilities). This same paradigm can be adapted to human studies once we have learned more about the physiological signatures of prediction errors. The new approach that the Centre research brings is to undertake multi-scale experiments based on these paradigms, and analyse and interpret the data acquired from the scale of the single neuron, through neural circuits, up to the whole brain.


Attention

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Understanding attention has wide implications

The Centre research program addressing Attention comprises multiple projects, each crossing more than one research theme.

In a complex environment, we prioritise certain objects and actions at the expense of others. Likewise, sudden or unexpected stimuli (e.g. an approaching car) can capture attention during an ongoing task (crossing the street). Attention therefore has two main aspects: an experience and state dependent ('top-down') component for filtering complex information, and a stimulus driven ('bottom-up') component that captures attention when there is an unexpected or salient change in the environment.

Understanding the brain mechanisms underlying attention is of obvious importance, with implications for many areas including education, driving, surveillance, and workplace safety. We know that when attention is focused on part of the visual world (e.g. when reading this text) there is increased activity in visual centres, where cells show enhanced electrical and biochemical activity, and responses synchronise. We also know that these changes depend on neural commands interchanged between brain areas with 'executive' functions (in the parietal and frontal lobes), and those involved in sensory processing and motor control.


Brain Functions

The brain functions guiding our research

The ARC Centre of Excellence for Integrative Brain Function seeks to better understand how the brain interacts with the world by focusing on the brain’s intricate structure and functions that underlie attention, prediction and decision-making.

Attention

Our research program addressing Attention comprises multiple projects, each crossing more than one research theme. In a complex environment, we prioritise certain objects and actions at the expense of others. Likewise, sudden or unexpected stimuli (e.g., an approaching car) can capture attention during an ongoing task (crossing the street).

Prediction

Our research on Prediction is based on recent demonstrations that the brain does not simply respond to external events, but rather compares sensory information against predictions based on internal representations (memories). The difference between predictions and external inputs (‘prediction errors’) are used to initiate adaptive behaviours.

Decision

Every day we make decisions based on information captured by multiple senses and on our internal goals. For example, to cross a road, we must decide how to coordinate our movements to reach the destination safely and at the right time. To achieve this goal, we guess where the car is likely to be while we cross the road, given what we see of its trajectory and the sound it produces.


Models and Technologies

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Overview

Historically, data collection in neuroscience has outpaced developments in theory and computation. As a result the field lacks the simple concepts needed to unify results of huge numbers of experiments. However, in recent years the power of theoretical approaches has increased, and we are now starting to see the formulation of models that are linked tightly to brain physiology and anatomy.

At the same time, physics and engineering have advanced our understanding of complex systems and networks, and advances in digital computing hardware and software have made it feasible to test these models in detail. Finally, massive databases detailing brain structure and function are becoming available, covering scales from neuronal microcircuitry up to whole brain connectivity.

This confluence of rapid advances gives our Centre an exceptional new opportunity: to develop quantitative, testable theories of integrated brain function, and to test the predictions against experimental data that span multiple spatial and temporal scales.

Models and Technologies - examining all integrative brain functions

In essence, the Models and Technologies research theme institutes the same kind of theory-experiment interaction that has proven fruitful in the physical sciences and engineering. In parallel, our expertise in data analysis, data fusion, and control engineering is harnessed to monitor, stimulate, and potentially control brain activity. For example, we have the capacity to use microelectronics and nanoscale fabrication to make electrodes capable of both measurement and stimulation (developed in the Bionic Eye project), wireless interfacing, and new data fusion algorithms and techniques. These developments enable the integrative brain functions of attention, prediction, and decision to be probed and related to the underlying physiology and anatomy, thereby distinguishing between competing theories of how these functions are realised.


Cells and Synapses

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Overview

Brain function relies on spiking activity under control
 of sensory inputs and stored brain states (memories).
 However, spiking activity also depends on the
biophysical properties of neurons and their
 connections (synapses), as well as whole brain 
(behavioural and hormonal) states.

Ultimately, the 
generation of spikes requires the movement of
 charged ions. Thus, short- and long-term changes in
 neural properties and connections can arise via
 changes in spiking behaviour (e.g. via biophysical changes in ionic pores or 'channels' through cell membranes), or by dynamic changes in synapses.

To understand brain function it is thus critical to determine how the biophysical properties of neurons and their connections serve to govern network activity.

Cells and Synapses – functional properties of neurons

The Cells and Synapses research theme addresses the functional (electrophysiological) properties of neurons and their projections via axons (membrane specialisations that provide the physical 'wiring' of the brain) at critical sites of brain networks underlying attention, prediction, and decision.

At microscopic scales, the distribution of different membrane channels is being investigated, and their properties will be characterised. Critically, we are establishing for different brain regions how neurons combine their inputs to produce an output signal ('spike train') for communication to other brain regions. These questions are being investigated with tools such as axonal tracing, electrophysiology, and optogenetics.


Neural Circuits

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Overview

The mammalian brain is assembled from local neural circuits that are connected into networks, in which signals are encoded as brief voltage 'spikes'. This spiking activity is used to communicate information between neurons, and is the basis of the computations performed in the brain. Spiking rates in different neurons, and their change over time, are thought to encode diverse features such as the properties of sensory stimuli, the location of an important object in the environment, movements to be made, or memories of events. Ultimately, our ability to attend to particular aspects of the world, to predict events, and to make decisions results from activity in neuronal circuits, but our understanding of how these circuits are organised, and how they are formed into large-scale networks, remains rudimentary.

From studies in the last 50 years we understand the initial processing of sensory stimuli. Moreover, physiological studies in animals and human imaging studies have revealed the brain regions that are involved in simple behavioural tasks, while anatomical tracing has shown the broad principles of how brain areas are connected. However, the functional nature of connections – i.e., how they determine activity and behaviour – remains poorly understood, as is the encoding of information. These data are essential to develop specific models of brain function. Thus, a central theme of the Centre is to understand neural circuits, and to determine how their functions are encoded in neural networks.

Neural Circuits – behavioural tasks

The Neural Circuits research theme uses behavioural tasks that are well established and for which the brain regions involved are well understood. For example, simple stimulus-response (Pavlovian conditioning) behavioural experiments can be employed in the contexts of attention, prediction, and decision. To study activity in the underlying neural circuits we record from multiple brain sites in awake behaving animals. These recordings lead to a model of how different brain centres drive the relevant behaviour.

Neural Circuits – optogenetic modulation

The new method of optogenetic modulation allows activation or silencing of neurons with millisecond precision, and can be engaged to activate the source (cell bodies) or destination (cell terminals) of functional paths. The anatomy and physiology of these connections can then be determined. These studies provide information on the activity of neural networks in rodents and non-human primates. A key part of our Centre research strategy is to test the relevance of these networks to human brain function, by implementing analogous behavioural tasks in non-invasive imaging studies.