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Personal profile

Research interests


Up close, the brain is a swirling sea of electrical signals, some carrying sensory information about the scene in front of the eyes, others forming thoughts, decisions, and intended actions. Our research seeks to make sense of these signals and to understand the brain as a “computation-machine” for perception and behaviour. This knowledge will help us repair the brain when it becomes damaged, treat mental illness, and build better, smarter technologies.


The Neural Code

Like the roads of a major city, the brain is a vast network of pathways, each carrying electrical signals (traffic) from A to B, to C, and so on. When we listen in to one of these pathways (with a recording electrode), we hear neurons tapping out signals to each other like Morse code. Morse code, however, is vastly more simple; a message is sent as a single stream of dots and dashes, easily decoded by a lone interpreter at the other end. A message in the brain, in contrast, is distributed across thousands of streams (neurons) simultaneously, and is decoded by thousands of interpreters (downstream neurons). Moreover, messages are passed in both directions at the same time! Somehow, out of all of this, arises everything we see, think, feel, and do.

Our approach combines "data-science" and experimental biology to make sense of brain signals.

In our view, the brain’s primary role is to build an internal model of the outside world and make sensible choices for action. Like any statistician, the brain builds this model from noisy data, including real-time sensory signals (vision, hearing, touch etc.), information from the past stored as memories (e.g. the 3D structure of the environment), and internal signals related to the state of the mind and body (e.g. motivation, posture).

Neural computation is a process of integration and statistical inference. 

Key features of our approach:

(1) We probe the input-output relationship of sensory neurons experimentally using electrophysiological recordings and precise visual stimulation;

(2) use statistical tools to identify the patterns of neural activity that carry important signals (i.e., signals that could be the causal basis for behaviour), and;

(3) translate these data-driven insights into mathematical models of brain function to formalise our understanding and predict behaviour in novel scenarios.

Technical features:  

  • High-dimensional time series data (i.e. activity of hundreds of neurons at millisecond resolution)
  • Measures of perception, decisions, and behaviour (e.g. eye/arm movements, classification)
  • Precisely controlled sensory stimulation
  • Automated signal processing pipelines
  • Multivariate statistical models (e.g., General Linear Models, logistic regression, canonical correlation)
  • Machine-learning (e.g., Bayesian classifiers, support-vector machines)
  • Mathematical modelling and simulation (e.g. artificial neural networks, attractor networks, dynamic system models, Markov chains)
  • Dimensionality reduction (e.g., PCA, manifold-learning, factor analysis)
  • System identification (e.g., white-noise analysis, filter estimation) 

The end result is a quantitative, computational model of an aspect of brain function (e.g. visual perception, decision-making, action-planning). With this model in hand, we can simulate brain damage or dysfunction and observe how these changes impact on behaviour. This provides mechanistic insights into disease (e.g. stroke) and hence provides clear guidance for treatment. Similarly, we can use the models to engineer implantable technologies (e.g. microelectrode arrays) that communicate directly with the brain, speaking in the right language, to restore lost sensory (e.g. bionic vision) or motor (e.g. prosthetics, robotics) function.


Dr Adam Morris has led the Computational and Sensorimotor Neuroscience lab in the Department of Physiology at Monash University since 2015. He completed postdoctoral training with Prof. Bart Krekelberg (Rutgers University, USA) and Prof. Michael Ibboston (National Vision Research Institute and University of Melbourne) with the support of an NHMRC Overseas Training (CJ Martin) Fellowship. He gained his PhD under Australian Laureate Fellow Jason Mattingley (University of Melbourne) in 2008 and holds a Bachelor of Science (Hons., University of Melbourne). Dr. Morris’ research combines experimental biology with statistical modelling and theory to understand brain function at the level of cells, circuits, and networks.

Research area keywords

  • Artificial intelligence
  • Data Science
  • Neuroscience
  • Automation
  • Natural Language Processing
  • Computational Neuroscience
  • Mathematical Modelling
  • Neural Networks
  • Health Informatics


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