Ai Computational Neuroscience

Single Neuron Biophysics

Doctoral research examines the physical processes generating electrical activity in individual neurons. Neuron level understanding is the foundation of every larger scale model.

Membrane Potential Dynamics

Research investigates how voltage across the neuronal membrane evolves over time. Membrane dynamics determine when and whether a neuron produces output.

Ion Channel Modelling

Doctoral study addresses mathematical description of the proteins conducting ionic current. Channel properties account for the diversity of neuronal firing behaviour.

Conductance Based Neuron Models

Research examines detailed models representing individual ionic currents explicitly. These models connect molecular properties directly to firing behaviour.

Reduced Neuron Model Development

Doctoral work studies simplified models retaining essential neuronal behaviour. Reduction permits simulation of networks that detailed models cannot support.

Integrate And Fire Model Variants

Research investigates the simplest widely used family of spiking neuron models. These models permit both analysis and very large scale simulation.

Spike Initiation Mechanisms

Doctoral study addresses how and where action potentials begin within a neuron. Initiation properties determine precision of neuronal output timing.

Action Potential Propagation Modelling

Research examines travel of electrical impulses along neuronal processes. Propagation reliability affects whether signals reach their targets at all.

Axonal Conduction Modelling

Doctoral work studies signal transmission along axons of varying geometry. Conduction delays shape the timing relationships circuits depend upon.

Myelination And Conduction Speed

Research investigates insulating sheaths accelerating neural signal transmission. Myelination differences produce conduction delays that circuits exploit functionally.

Dendritic Computation

Doctoral study addresses processing performed within neuronal input structures. Dendrites perform computation before any signal reaches the cell body.

Dendritic Nonlinearity Modelling

Research examines nonlinear interactions between inputs arriving on dendrites. Dendritic nonlinearity substantially expands single neuron computational capability.

Cable Theory And Extensions

Doctoral work studies mathematical description of signal spread through neuronal processes. Cable theory remains the analytical foundation of dendritic modelling.

Morphologically Detailed Simulation

Research investigates simulation preserving the full spatial structure of neurons. Detailed simulation reveals effects that point models entirely miss.

Neuron Morphology Reconstruction

Doctoral study addresses recovery of neuronal shape from microscopy data. Reconstructed morphology is the input every detailed model requires.

Morphology Function Relationships

Research examines how neuronal shape determines computational properties. Shape differences between cell types reflect distinct computational roles.

Synaptic Transmission Modelling

Doctoral work studies the transfer of signals between directly connected neurons. Synaptic properties determine what any given network can compute and can learn.

Neurotransmitter Release Dynamics

Research investigates the probabilistic process by which signalling molecules are released. Release variability is one of the dominant sources of noise in neural systems.

Receptor Kinetics Modelling

Doctoral study addresses time course of postsynaptic receptor responses. Receptor timing determines the temporal window over which inputs combine.

Short Term Synaptic Dynamics

Research examines rapid strengthening and weakening during repeated activity. Short term dynamics implement filtering and gain control within circuits.

Synaptic Noise And Variability

Doctoral work studies random fluctuation in synaptic signal transfer. Synaptic variability constrains the reliability of any neural computation.

Stochastic Neuron Modelling

Research investigates explicitly probabilistic descriptions of neuronal behaviour. Stochastic treatment is essential where noise shapes function.

Channel Noise And Reliability

Doctoral study addresses fluctuation arising from small numbers of ion channels. Channel noise limits the precision small neurons can achieve.

Neuronal Excitability Classification

Research examines categorisation of neurons by their response to input. Excitability class predicts synchronisation behaviour within networks.

Bifurcation Analysis Of Neurons

Doctoral work studies qualitative changes in neuronal behaviour as parameters vary. Bifurcation analysis explains transitions between firing regimes.

Dynamical Systems Approaches

Research investigates neural systems described as evolving dynamical systems. Dynamical framing provides analytical tools simulation alone lacks.

Phase Response Analysis

Doctoral study addresses how input timing shifts ongoing neuronal rhythms. Phase response curves predict synchronisation between oscillating neurons.

Bursting Dynamics Modelling

Research examines neurons producing grouped clusters of action potentials. Bursting conveys information distinct from isolated action potentials.

Intrinsic Rhythm Generation

Doctoral work studies neurons generating rhythmic activity without external input. Intrinsic rhythms provide timing references throughout the nervous system.

Neuromodulator Effects On Excitability

Research investigates chemical signals reconfiguring neuronal and circuit properties. Neuromodulation permits one circuit to perform several functions.

Energy Constraints In Neural Function

Doctoral study addresses the metabolic limits shaping neural design. Energy availability constrains firing rates and connectivity throughout the brain.

Metabolic Cost Of Signalling

Research examines the energy consumed by neural transmission and by computation itself. Cost accounting explains many otherwise puzzling features of neural design.

Glial Contributions To Computation

Doctoral work studies nonneuronal cells that actively influence neural signalling. Glial cells rival neurons in number and actively shape circuit level function.

Neurovascular Coupling Modelling

Research investigates the physiological link between neural activity and local blood flow. This link underlies the entire interpretation of functional brain imaging.

Multiscale Model Integration

Doctoral study addresses connecting molecular, cellular and network level models. Multiscale integration is necessary yet computationally very demanding.

Network Model Construction

Research examines assembly of individual neuron models into functioning circuit models. Construction choices determine what a network model is capable of demonstrating.

Recurrent Network Dynamics

Doctoral work studies networks where neurons feed activity back to one another. Recurrence produces the rich dynamics feedforward structures cannot.

Balanced Network Theory

Research investigates networks where excitation and inhibition approximately cancel. Balanced operation explains the irregular firing observed in cortex.

Excitation Inhibition Balance

Doctoral study addresses the maintained relationship between opposing influences. Balance disturbance is implicated in several neurological conditions.

Inhibitory Circuit Function

Research examines the computational roles performed by inhibitory neurons. Inhibition performs far more than simple suppression of activity.

Interneuron Diversity Modelling

Doctoral work studies the many distinct classes of inhibitory neuron. Interneuron diversity suggests functionally specialised inhibitory operations.

Cortical Microcircuit Models

Research investigates repeating local circuit motifs within cerebral cortex. Microcircuit structure appears broadly conserved across cortical regions.

Laminar Circuit Organisation

Doctoral study addresses the layered organisation of cortical connectivity. Layer specific connectivity implies layer specific computational roles.

Canonical Circuit Hypotheses

Research examines proposals that cortex performs one repeated computation. Canonical proposals remain influential and substantially unresolved.

Attractor Network Models

Doctoral work studies networks that settle into stable self sustaining activity patterns. Attractor dynamics provide a candidate mechanism for both memory and decision.

Continuous Attractor Dynamics

Research investigates networks maintaining a continuum of stable states. Continuous attractors can represent position and orientation persistently.

Ring Attractor Models

Doctoral study addresses circular attractor structures representing angular variables. Ring attractors have been directly identified in insect navigation circuits.

Line Attractor And Integration

Research examines networks accumulating and holding a continuous quantity. Integrating circuits convert transient inputs into persistent representation.

Working Memory Circuit Models

Doctoral work studies circuits holding information over short intervals. Working memory mechanisms remain actively contested between competing models.

Persistent Activity Mechanisms

Research investigates maintained firing after a stimulus has ended. Persistent activity is the classical proposed substrate of short term memory.

Sequence Generation In Networks

Doctoral study addresses circuits producing ordered activity patterns over time. Sequence generation underlies movement, birdsong and episodic recall.

Oscillation Generation Mechanisms

Research examines how rhythmic population activity arises within neural circuits. Oscillations are proposed to coordinate activity across widely separated brain regions.

Gamma Rhythm Modelling

Doctoral work studies fast rhythmic activity generated within local cortical circuits. Gamma rhythms depend critically on the precise timing of inhibitory circuit action.

Theta Rhythm And Navigation

Research investigates slower rhythms that are prominent during movement and memory tasks. Theta rhythm organises the precise timing of spatial coding activity in the brain.

Slow Wave And Sleep Dynamics

Doctoral study addresses large scale rhythmic brain activity occurring during sleep states. Sleep rhythms are strongly implicated in the consolidation of newly formed memories.

Cross Frequency Interaction Analysis

Research examines coupling between rhythms at different frequencies. Cross frequency coupling may coordinate processing across spatial scales.

Synchronisation In Neural Populations

Doctoral work studies coordinated firing timing across groups of connected neurons. Synchronisation strongly affects the impact that activity has on downstream targets.

Phase Locking And Coordination

Research investigates consistent timing relationships between neural signals. Phase relationships may implement selective routing of information.

Travelling Wave Analysis

Doctoral study addresses activity that propagates spatially across sheets of neural tissue. Travelling waves organise the timing of activity across substantial cortical distances.

Criticality In Neural Systems

Research examines whether brain dynamics operate near a critical regime. Critical operation would optimise several information processing measures.

Avalanche Dynamics Analysis

Doctoral work studies cascades of activity spreading through neural populations. Avalanche statistics provide the principal evidence for critical dynamics.

Chaos And Stability In Networks

Research investigates sensitivity of network dynamics to small perturbations. Chaotic dynamics limit predictability yet may support rich computation.

Noise Correlations In Populations

Doctoral study addresses shared variability between simultaneously recorded neurons. Correlated noise fundamentally limits population coding accuracy.

Population Coding Theory

Research examines how groups of neurons jointly represent information. Population level representation differs qualitatively from single neuron coding.

Neural Manifold Analysis

Doctoral work studies the low dimensional structure of population activity. Manifold structure suggests strong constraints on achievable activity patterns.

Low Dimensional Dynamics Extraction

Research investigates recovery of underlying dynamics from high dimensional recordings. Dimensionality reduction reveals structure individual neurons obscure.

Latent Variable Models Of Activity

Doctoral study addresses inference of unobserved variables driving neural activity. Latent models separate shared dynamics from independent variability.

Dimensionality Reduction For Neural Data

Research examines compact representation of very large recording datasets. Reduction is prerequisite to interpreting modern recording volumes.

State Space Models Of Neural Activity

Doctoral work studies models representing activity as an evolving hidden state. State space framing connects neural dynamics to control theory.

Trial To Trial Variability Analysis

Research investigates why identical stimuli produce differing neural responses. Variability structure carries information about internal state.

Spontaneous Activity Structure

Doctoral study addresses organised activity occurring without external stimulation. Spontaneous activity consumes most of the brain energy budget.

Resting State Dynamics Modelling

Research examines large scale activity patterns in the absence of task. Resting dynamics reveal the intrinsic organisation of brain networks.

Whole Brain Network Modelling

Doctoral work studies simulation of activity across entire brain networks. Whole brain models connect regional dynamics to global measurements.

Connectome Based Simulation

Research investigates simulation constrained by measured anatomical connectivity. Anatomical constraint substantially narrows the space of plausible models.

Structural Connectivity Analysis

Doctoral study addresses measurement and description of physical neural connections. Structural connectivity provides the substrate all dynamics operate upon.

Functional Connectivity Estimation

Research examines statistical relationships between activity in different regions. Functional relationships change with state and task demands.

Effective Connectivity Inference

Doctoral work studies inference of directed causal influence between regions. Effective connectivity requires assumptions that correlation alone does not.

Graph Theoretic Brain Analysis

Research investigates brain networks described using graph theoretical measures. Graph measures summarise organisation across thousands of connections.

Network Control Theory In Neuroscience

Doctoral study addresses how brain states might be steered toward targets by applied input. Control theoretic framing informs the design of stimulation therapies directly.

Perturbation Response Modelling

Research examines predicted network responses to targeted intervention. Perturbation prediction is essential for designing stimulation protocols.

Synaptic Plasticity Modelling

Doctoral work studies mathematical description of connection strength change. Plasticity rules determine what a network can learn and retain.

Spike Timing Dependent Plasticity

Research investigates plasticity governed by the relative timing of activity. Timing dependence provides a mechanism for learning causal structure.

Homeostatic Plasticity Mechanisms

Doctoral study addresses processes maintaining stable activity levels over time. Homeostasis prevents the runaway change simple learning rules produce.

Metaplasticity Modelling

Research examines how the rules governing synaptic plasticity themselves change over time. Metaplasticity stabilises learning across widely varying activity conditions.

Synaptic Consolidation Models

Doctoral work studies stabilisation of connection changes into lasting form. Consolidation determines which experiences produce durable memory.

Memory Engram Modelling

Research investigates the distributed neural traces storing specific memories. Engram identification connects memory theory to identified cell populations.

Systems Consolidation Theory

Doctoral study addresses reorganisation of memory across brain regions over time. Systems consolidation explains the pattern of memory loss after injury.

Forgetting And Interference Models

Research examines mechanisms by which stored information becomes inaccessible. Forgetting appears to be an active rather than passive process.

Continual Learning In Neural Systems

Doctoral work studies learning new material without destroying prior knowledge. Biological systems achieve this far better than artificial ones.

Credit Assignment In Neural Circuits

Research investigates how circuits determine which connections to adjust. Credit assignment is the central unsolved problem in biological learning.

Biologically Plausible Learning Rules

Doctoral study addresses learning mechanisms consistent with known biology. Plausibility constraints exclude most artificial learning algorithms.

Gradient Learning And Brain Plausibility

Research examines whether biological brains could implement gradient based learning at all. This question connects machine learning theory directly to experimental neuroscience.

Predictive Coding Frameworks

Doctoral work studies theories in which circuits continuously predict their own inputs. Predictive coding explains a wide range of response properties very parsimoniously.

Free Energy Formulations

Research investigates unifying theories based on minimising a statistical quantity. These formulations claim to unify perception, learning and action.

Hierarchical Inference Models

Doctoral study addresses inference performed across layered representations. Hierarchical inference matches the layered organisation of sensory systems.

Bayesian Brain Hypotheses

Research examines proposals that neural computation implements probabilistic inference. Probabilistic framing accounts for behaviour under uncertainty accurately.

Probabilistic Population Codes

Doctoral work studies population activity representing probability distributions. These codes permit uncertainty to be carried through computation.

Efficient Coding Principles

Research investigates the proposal that neural coding maximises information transmitted. Efficiency principles predict receptive field properties from stimulus statistics.

Sparse Coding Models

Doctoral study addresses representations using few simultaneously active units. Sparse coding predicts observed receptive field structure remarkably well.

Redundancy Reduction Theories

Research examines removal of statistical redundancy in sensory representation. Redundancy reduction is among the oldest normative coding principles.

Information Theoretic Neural Analysis

Doctoral work studies quantification of information carried by neural activity. Information measures provide assumption light characterisation of coding.

Mutual Information Estimation In Neurons

Research investigates statistical estimation of information from limited recordings. Estimation bias is severe with the sample sizes experiments provide.

Neural Coding Of Stimuli

Doctoral study addresses how sensory features are represented in neural activity. Coding understanding is prerequisite to interpreting any recording.

Rate And Timing Code Comparison

Research examines whether firing frequency or precise timing carries information. This question remains contested across many neural systems.

Temporal Coding Mechanisms

Doctoral work studies information carried by the precise timing of individual spikes. Temporal codes could in principle carry far more information than firing rate alone.

Correlation Coding Hypotheses

Research investigates information carried by relationships between neurons. Correlations may carry information single neuron analysis discards.

Decoding Neural Population Activity

Doctoral study addresses recovery of represented information from recorded activity. Decoding quantifies what information is actually present in a population.

Encoding Model Construction

Research examines models predicting neural responses from stimulus features. Encoding models formalise hypotheses about what neurons represent.

Receptive Field Estimation

Doctoral work studies characterisation of the stimulus features driving a neuron. Receptive field description remains the standard characterisation approach.

Nonlinear System Identification

Research investigates characterisation of neurons as nonlinear input output systems. Nonlinear identification captures behaviour linear description misses.

Adaptation In Sensory Coding

Doctoral study addresses adjustment of neural responses to recent stimulus statistics. Adaptation maintains sensitivity across enormous dynamic ranges.

Gain Control Mechanisms

Research examines circuit mechanisms adjusting response magnitude according to context. Gain control prevents saturation across the enormous range of natural stimulus intensities.

Normalisation Computations

Doctoral work studies division of individual responses by summed surrounding activity. Normalisation appears throughout sensory, motor and cognitive neural systems.

Divisive Normalisation Models

Research investigates the specific mathematical form of normalisation operations. This operation is proposed as a canonical neural computation.

Attention Modelling In Circuits

Doctoral study addresses circuit mechanisms implementing selective processing. Attention modulates responses throughout sensory hierarchies.

Reinforcement Learning In The Brain

Research examines learning from reward implemented in neural circuits. Reinforcement theory connects neuroscience and machine learning directly.

Reward Prediction Error Models

Doctoral work studies signals reporting the difference between expected and received reward. These signals were identified in the brain before being widely used artificially.

Dopaminergic System Modelling

Research investigates circuits releasing a neuromodulator central to learning. Dopaminergic function is implicated in motivation, learning and disease.

Decision Making Models

Doctoral study addresses circuit mechanisms selecting among available actions. Decision models connect neural activity to measured behaviour quantitatively.

Evidence Accumulation Modelling

Research examines gradual integration of information toward a decision. Accumulation models predict both choices and reaction time distributions.

Visual System Modelling

Doctoral work studies computational description of processing throughout the visual system. Vision is by a considerable margin the most extensively modelled sensory system.

Retinal Computation Models

Research investigates processing performed within the retina before transmission. The retina performs substantial computation rather than simple transduction.

Early Visual Cortex Models

Doctoral study addresses the earliest cortical stages of visual information processing. Early visual cortex remains the best characterised region of the cerebral cortex.

Object Recognition Modelling

Research examines computational accounts of recognising objects across variation. Recognition invariance remains a demanding computational problem.

Deep Networks As Visual Models

Doctoral work studies artificial networks compared against visual system responses. These networks predict neural responses better than earlier models.

Auditory System Modelling

Research investigates computational description of hearing and sound processing. Auditory processing operates on far faster timescales than vision.

Cochlear Processing Models

Doctoral study addresses mechanical and neural transduction of sound within the inner ear. Cochlear models underpin both hearing research and the design of auditory prostheses.

Speech Processing In The Brain

Research examines neural computation converting sound into linguistic content. Speech processing combines acoustic and linguistic constraints continuously.

Somatosensory System Modelling

Doctoral work studies computational description of touch and body sensing. Somatosensory processing integrates signals across the entire body surface.

Tactile Coding Models

Research investigates representation of touch features in neural activity. Tactile coding informs both neuroscience and prosthetic feedback design.

Olfactory System Modelling

Doctoral study addresses computation performed within olfactory sensing pathways. Olfactory coding operates within an extremely high dimensional chemical stimulus space.

Chemical Sensing Circuit Models

Research examines circuit architectures processing chemical signals across many species. These circuits are comparatively simple and unusually accessible experimentally.

Motor Control Modelling

Doctoral work studies computational description of movement generation and control. Motor control connects neural activity to measurable physical output.

Optimal Control Theory In Movement

Research investigates movement understood as the optimisation of an internal cost function. Optimal control accounts for many observed regularities in natural human movement.

Motor Cortex Population Dynamics

Doctoral study addresses population level activity patterns during movement. Dynamical framing transformed interpretation of motor cortex activity.

Cerebellar Computation Models

Research examines computation performed by cerebellar circuitry. Cerebellar architecture is unusually regular and invites computational theory.

Basal Ganglia Circuit Models

Doctoral work studies deep brain circuits implicated in action selection. These circuits are central to movement disorders and reinforcement learning.

Action Selection Modelling

Research investigates mechanisms choosing among competing possible actions. Selection mechanisms must resolve competition without deadlock or oscillation.

Spinal Circuit Modelling

Doctoral study addresses computation performed within spinal circuitry. Spinal circuits implement reflexes and pattern generation independently.

Locomotion Pattern Generation

Research examines circuits producing rhythmic movement without descending command. Pattern generators are among the best understood neural circuits.

Sensorimotor Integration Models

Doctoral work studies the combination of sensory feedback with ongoing motor commands. Integration is required for accurate movement under sensory and motor uncertainty.

Internal Model Theories

Research investigates neural representations predicting consequences of action. Internal models compensate for delays that would otherwise destabilise control.

Hippocampal Circuit Modelling

Doctoral study addresses computation within circuits central to memory and navigation. Hippocampal circuits are the most intensively modelled in the brain.

Spatial Representation Models

Research examines neural representation of position and environment. Spatial coding provides an unusually clear link between activity and variable.

Grid And Place Cell Theories

Doctoral work studies neurons displaying strikingly structured spatial firing patterns. These cell types prompted a very large body of computational theoretical work.

Navigation Circuit Models

Research investigates circuits supporting route planning and spatial behaviour. Navigation models connect spatial representation to behavioural output.

Prefrontal Cortex Modelling

Doctoral study addresses computation in regions supporting flexible behaviour. Prefrontal function resists the modelling clarity sensory regions permit.

Cognitive Control Modelling

Research examines mechanisms directing neural processing according to current goals. Control mechanisms account for the flexible task dependent behaviour humans display.

Language Processing Models

Doctoral work studies neural computation underlying language comprehension and production. Language models from machine learning increasingly inform this work.

Semantic Representation In The Brain

Research investigates how meaning is represented across neural populations. Semantic representation appears distributed across very wide cortical areas.

Social Cognition Modelling

Doctoral study addresses computation supporting reasoning about other agents. Social computation requires modelling of other minds and intentions.

Emotion And Affect Modelling

Research examines computational accounts of affective states and their influence. Affective states modulate essentially all other neural computation.

Consciousness Theories And Models

Doctoral work studies computational proposals concerning conscious experience. Competing theories make testable and differing empirical predictions.

Sleep And Memory Modelling

Research investigates computational accounts of memory processing during sleep. Sleep replay is proposed as a mechanism of memory consolidation.

Developmental Circuit Modelling

Doctoral study addresses the emergence of circuit organisation during development. Development produces organisation without any explicit blueprint.

Critical Period Modelling

Research examines developmental windows of markedly heightened neural plasticity. Critical period mechanisms inform approaches to both education and rehabilitation.

Ageing And Neural Dynamics

Doctoral work studies systematic change in neural computation across the lifespan. Ageing changes both dynamics and the computations they support.

Individual Differences In Neural Models

Research investigates variation in circuit properties between individuals. Individual variation limits how far group averaged models generalise.

Comparative Circuit Modelling

Doctoral study addresses circuit comparison across species and lineages. Comparison distinguishes general principles from species specific arrangements.

Invertebrate Circuit Models

Research examines computation in small and experimentally accessible nervous systems. Invertebrate circuits permit complete characterisation that vertebrates do not.

Neural Data Analysis Methods

Doctoral work studies statistical treatment of recorded neural activity data. Analysis method choice materially changes the scientific conclusions that are reached.

Spike Sorting Algorithms

Research investigates attribution of recorded activity to individual neurons. Sorting errors propagate into every subsequent analysis silently.

Calcium Imaging Analysis Methods

Doctoral study addresses extraction of neural activity from fluorescence recordings. Calcium signals are an indirect and temporally blurred activity measure.

Voltage Imaging Data Analysis

Research examines analysis of membrane voltage recorded through optical imaging methods. Voltage imaging preserves the precise timing that calcium measurement necessarily loses.

Electrophysiology Signal Processing

Doctoral work studies processing of directly recorded electrical signals. Processing choices determine which signal components survive to analysis.

Large Scale Recording Analysis

Research investigates analysis of simultaneous recordings from many thousands of neurons. Recording scale has grown faster than analysis capability.

Behavioural Tracking And Quantification

Doctoral study addresses automated measurement and description of animal behaviour. Behavioural measurement precision now limits many otherwise capable neural analyses.

Behaviour Neural Alignment Methods

Research examines relating neural activity to simultaneously measured behaviour. Alignment quality determines what neural activity can be attributed to.

Statistical Testing In Neuroscience

Doctoral work studies statistical inference practice throughout neuroscience research. Statistical practice in this field has attracted substantial and sustained criticism.

Reproducibility In Computational Neuroscience

Research investigates whether published models and analyses can be regenerated. Incomplete method reporting is the principal obstacle to reproduction.

Model Fitting And Parameter Estimation

Doctoral study addresses determination of model parameters from data. Fitting quality determines whether a model tells us anything about biology.

Model Comparison And Selection

Research examines principled choice among competing model explanations. Comparison methods must penalise the flexibility complex models possess.

Identifiability In Neural Models

Doctoral work studies whether parameters can be determined uniquely from data. Many neural models have parameters that data cannot constrain.

Sensitivity Analysis Of Models

Research investigates which parameters actually determine model behaviour. Sensitivity analysis identifies where measurement effort should concentrate.

Uncertainty Quantification In Models

Doctoral study addresses honest expression of confidence in model conclusions. Neural models are frequently presented without any uncertainty at all.

Simulation Based Inference

Research examines parameter inference where likelihoods cannot be computed. These methods made complex simulator models statistically tractable.

Surrogate Modelling For Simulation

Doctoral work studies fast approximations replacing expensive neural simulation. Surrogates permit exploration that direct simulation cannot afford.

Simulation Software Development

Research investigates software supporting neural model construction and execution. Simulator capability determines what models the community can build.

Standardisation Of Model Description

Doctoral study addresses formal languages describing neural models unambiguously. Standard description permits models to be shared and reproduced.

Model Sharing And Reuse

Research examines infrastructure supporting publication and reuse of neural models. Published models are frequently impossible for other groups to run independently.

Data Standards In Neuroscience

Doctoral work studies common formats for neural recordings and metadata. Format fragmentation obstructs reuse of expensively collected data.

Open Data Infrastructure

Research investigates repositories holding neural data for community reuse. Shared data permits analyses no single laboratory could support.

Benchmarking Of Neural Models

Doctoral study addresses systematic evaluation of models against common data. Benchmarks reveal whether reported model advances are genuine.

Computational Resource Demands

Research examines the very large computing capacity that neural simulation requires. Resource requirements determine which classes of model can be explored at all.

Neuromorphic Hardware Implementation

Doctoral work studies hardware built to emulate neural computation directly. Neuromorphic hardware targets the extreme efficiency brains achieve.

Spiking Network Hardware

Research investigates devices implementing event driven neural computation. Event driven hardware consumes energy only when activity occurs.

Energy Efficient Neural Computation

Doctoral study addresses the principles behind the remarkable efficiency of brains. Brains perform complex processing at power levels electronics cannot approach.

Brain Inspired Machine Learning

Research examines transfer of neuroscience principles into artificial systems. Biological principles have repeatedly informed machine learning advances.

Comparison Of Artificial And Neural Systems

Doctoral work studies systematic comparison between artificial networks and brains. Comparison requires methods that neither field developed alone.

Representational Similarity Analysis

Research investigates comparison of representational structure across systems. This method compares brains and models without requiring correspondence.

Mechanistic Interpretability Of Networks

Doctoral study addresses understanding computation within artificial neural networks. Interpretability methods increasingly borrow directly from neuroscience.

Digital Representation Of Brain Systems

Research examines synchronised computational models of individual brains. Individualised models could support personalised intervention planning.

Disease Circuit Modelling

Doctoral work studies computational accounts of neurological disease mechanisms. Circuit models connect molecular pathology to observed symptoms.

Epilepsy Network Modelling

Research investigates the network mechanisms that generate and terminate seizure activity. These models increasingly inform both surgical planning and stimulation therapy.

Parkinsonian Circuit Dynamics

Doctoral study addresses circuit changes underlying movement disorder symptoms. Circuit models guide stimulation targeting and parameter selection.

Psychiatric Computational Modelling

Research examines computational accounts of psychiatric symptoms and conditions. Computational framing offers measurable descriptions of subjective phenomena.

Computational Psychiatry Methods

Doctoral work studies methods applying computational models to clinical populations. These methods aim to make psychiatric assessment quantitative.

Stimulation Response Prediction

Research investigates forecasting neural responses to applied stimulation. Prediction is essential for individualising stimulation therapies.

Ethics In Brain Modelling

Doctoral study addresses moral questions raised by detailed brain simulation. Questions concern both research conduct and the models themselves.

Training In Computational Neuroscience

Research examines preparation of researchers spanning neuroscience and computation. Skills spanning both domains remain scarce and slow to acquire.