Neuroscience | Mahyar's world 🌏

Temporal Predictive Coding

Sun, 15 Dec 2024 00:00:00 +0000

Authors: Beren Millidge, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper, Rafal Bogacz
Publication: PLOS Computational Biology (April 2024)
Code: GitHub Repository
Read the paper: PLOS Computational Biology

Overview

How does the brain process dynamic sensory inputs; like tracking a moving object or predicting the next note in a melody? In this work, we propose temporal predictive coding (tPC), a biologically plausible neural framework that extends classical predictive coding to handle time-varying stimuli. By unifying Bayesian inference, neural dynamics, and synaptic plasticity, tPC explains how cortical circuits might learn to predict and adapt to continuously changing environments.

Key Contributions

A unified model for temporal prediction: tPC bridges predictive coding (for static inputs) and Kalman filtering (for dynamic systems), enabling online inference and learning without complex matrix operations.
Biologically plausible implementation: The model uses only local computations and Hebbian plasticity, aligning with known cortical microcircuit architecture.
Motion-sensitive receptive fields: When trained on natural movies, tPC neurons develop Gabor-like, direction-selective receptive fields—matching properties of early visual cortex neurons.
Nonlinear generalization: The framework extends to nonlinear systems (e.g., pendulum dynamics), outperforming linear models in complex tasks.

Methodology

Core Principles

Generative model: Hidden states evolve via a Markov process, with observations generated from hidden states.

$$ x_k = A f(x_{k-1}) + B u_k + \omega_x $$

$$ y_k = C f(x_k) + \omega_y $$

Free energy minimization: Neurons minimize prediction errors at two levels:
1. Sensory errors: Mismatch between predicted and actual observations.
2. Temporal errors: Mismatch between predicted and inferred hidden states.
Neural implementation:
- Explicit prediction error neurons or dendritic computations.
- Synaptic updates follow local Hebbian rules (e.g., ΔA ∝ prediction error × presynaptic activity).

Comparison to Kalman Filtering

tPC approximates Kalman filtering but avoids tracking posterior uncertainty.
Achieves similar accuracy in linear tracking tasks with simpler computations.

$$ \hat{x}_k^- = A \hat{x}_{k-1} + B u_k $$

$$ \hat{x}_k = \hat{x}_k^- + K(y_k - C\hat{x}_k^-) $$

$$ K = \Sigma_x C^T \left[C \Sigma_x C^T + \Sigma_y \right]^{-1} $$

Results

1. Robust Tracking of Dynamic States

tPC accurately infers position, velocity, and acceleration from noisy observations, even with scrambled sensory inputs.
Matches Kalman filter performance with 5 inference steps (MSE difference < 1%).

2. Learning Synaptic Weights

Hebbian plasticity enables online learning of dynamics (A, B) and observation (C) matrices.
Learns to predict observations even with random initial weights.

3. Biological Receptive Fields

Trained on natural movies, tPC develops:
- Spatiotemporal receptive fields with motion direction selectivity.
- Gabor-like filters resembling V1 simple cells. Representations developed by the model when trained with patches from movies of dynamic natural scenes. A: First 10 frames of 2 example training movies used in our experiments. Patches extracted from movies obtained at websites pexels.com, pixabay.com and commons.wikimedia.org (for wikimedia attributions see https://github.com/C16Mftang/temporal-predictive-coding). B: The projective fields C developed Gabor-like filters after training. C: Space-time receptive fields developed by hidden neurons of the tPC model. https://doi.org/10.1371/journal.pcbi.1011183.g006

4. Nonlinear Dynamics

Outperforms linear models in predicting pendulum motion (20% lower MSE).
Captures nonlinearities where linear approximations fail (e.g., large-angle oscillations). Simulations of the pendulum. A: A free-body diagram of a simple pendulum that has a mass m attached to a string with length $L$. Also shown are the forces applied to the mass. The restoring force $-mg sin \theta$ is a net force toward the equilibrium position. B: A phase portrait of the pendulum simulation showing the result of our linear versus nonlinear models prediction for the ground-truth data. The vector field (i.e. set of small arrows) was created by computing the derivatives of $\frac{d\theta_1}{dt}$ and $\frac{d\theta_2}{dt}$ at $t = 0$ a grid of 30 points over the range of $-\pi$ to $\pi$ and -4 to +4 for $\theta_1$ and $\theta_2$ respectively. C: The barplot shows the difference between the mean prediction errors of the linear model versus the nonlinear model from 100 simulations with varying noise profiles. The mean errors are significantly different $(p \ll 0.001)$ https://doi.org/10.1371/journal.pcbi.1011183.g007

Implications

Theoretical: Provides a unified account of static and dynamic sensory processing under predictive coding.
Experimental: Predicts neural responses to time-varying stimuli and receptive field properties.
Translational: Framework for neuromorphic hardware and efficient machine learning models.

Temporal Predictive Coding: A New Framework for Neural Processing of Dynamic Stimuli

Fri, 15 Nov 2024 00:00:00 +0000

Introduction

One of the most fascinating aspects of the brain is its ability to process and predict dynamic sensory inputs that continuously change over time. From tracking a moving object to predicting the next note in a melody, our brains are remarkably adept at temporal prediction. In a recent paper published in PLOS Computational Biology titled “Predictive coding networks for temporal prediction,” my colleagues and I proposed a new computational framework that may help explain how the brain accomplishes this feat.

Predictive coding has emerged as an influential theoretical model for understanding cortical function. The core idea is deceptively simple: the brain constantly generates predictions of incoming sensory inputs and compares these predictions with actual sensory data. Any mismatch results in prediction errors that drive learning and perceptual processing. This framework has successfully explained many neural phenomena and receptive field properties in visual cortex.

However, most previous predictive coding models have focused on static inputs, neglecting the temporal dimension that is crucial for real-world perception. Our work addresses this gap by extending predictive coding to the temporal domain while maintaining its elegant biological implementation.

The Temporal Predictive Coding Model

Generative Model and Free Energy

At the foundation of our temporal predictive coding (tPC) model is a Hidden Markov Model (HMM) structure, which assumes that observations are generated by hidden states that evolve according to a Markov process. Mathematically, we can express this generative model as:

$$ x_k = A f(x_{k-1}) + B u_k + \omega_x $$

$$ y_k = C f(x_k) + \omega_y $$

Where:

$x_{k}$ is the hidden state at time $k$.
$y_{k}$ is the observed sensory input at time $k$.
$u_{k}$ is the control input at time $k$.
$A$ is the dynamics matrix governing state transitions.
$B$ is the control matrix.
$C$ is the observation matrix.
$f$ is a potentially nonlinear function.
$\omega_x$ and $\omega_y$ are Gaussian process and observation noise.

The goal is to infer the current hidden state $x_{k}$ given the current observation $y_{k}$ and previous state estimate $y_{1:k-1}$. To achieve this, we formulate a variational free energy objective:

$$ \mathcal{F}_k = \frac{1}{2}(y_k - C f(x_k))^T \Sigma_y^{-1} (y_k - C f(x_k)) + \frac{1}{2}(x_k - A f(\hat{x}_{k-1}) - B u_k)^T \Sigma_x^{-1} (x_k - A f(\hat{x}_{k-1}) - B u_k) $$

This free energy can be understood as the sum of two weighted prediction errors:

Sensory prediction errors: The difference between observed and predicted sensory inputs $y_k - Cf(x_k)$.
Temporal prediction errors: The difference between the current state and the prediction from the previous state $x_k - Af(\hat{x}_{k-1}) - Bu_k$.

Each prediction error is weighted by the precision (inverse variance) of the corresponding noise distribution, ensuring that more reliable predictions carry more weight.

Neural Implementation

A crucial contribution of our work is showing how temporal predictive coding can be implemented in neural circuits using biologically plausible mechanisms. The neural dynamics for inferring the hidden state follow gradient descent on the free energy:

$$ \tau \frac{d x_k}{d t} = -\epsilon_x + f'(x_k) \odot C^T \epsilon_y $$

Where $\epsilon_x$ and $\epsilon_y$ are precision-weighted prediction errors:

$$ \epsilon_y = \Sigma_y^{-1} \left( y_k - C f(x_k) \right) $$

$$ \epsilon_x = \Sigma_x^{-1} \left( x_k - A f(\hat{x}_{k-1}) - B u_k \right) $$

We proposed multiple neural circuit implementations of this model:

Network with explicit prediction error neurons: Where dedicated neurons represent prediction errors at each level of processing
Dendritic computing implementation: Where prediction errors are computed as differences between somatic and dendritic potentials
Single-iteration implementation: A simplified version that performs single updates per time step

Neural circuit implementation

Importantly, all of these implementations rely on local information and Hebbian plasticity. The synaptic weights are updated according to:

$$ \Delta A = \eta \epsilon_x f(\hat{x}_{k-1})^T $$

$$ \Delta B = \eta \epsilon_x u_k^T $$

$$ \Delta C = \eta \epsilon_y f(x_k)^T $$

These update rules are Hebbian in nature because they depend only on the activities of pre- and post-synaptic neurons, making them biologically plausible.

Relationship to Kalman Filtering

An intriguing property of our model is its relationship to the Kalman filter, which is the optimal solution for linear Gaussian filtering problems. We demonstrated that both Kalman filtering and temporal predictive coding can be derived as special cases of Bayesian filtering, with the key difference being how they handle uncertainty.

The Kalman filter propagates uncertainty estimates through time, tracking the full posterior covariance at each step. In contrast, tPC approximates this by assuming a point estimate (Dirac distribution) for the previous state. Despite this simplification, our tPC model achieves comparable performance to the Kalman filter in tracking tasks while being computationally simpler and more biologically plausible.

For linear systems, the tPC dynamics at equilibrium yield:

$$ \hat{x}_k^- = A\hat{x}_{k-1} + Bu_k $$

$$ \hat{x}_k = \hat{x}_k^- + K(y_k - C\hat{x}_k^-) $$

$$ K = \Sigma_x C^T \left[C \Sigma_x C^T + \Sigma_y \right]^{-1} $$

This resembles the Kalman filter update equations but with a fixed gain matrix $K$ rather than a dynamically updated one based on posterior uncertainty.

Experimental Results

Performance in Linear Filtering Tasks

We tested our model on classic tracking problems, where the goal is to infer the hidden state (position, velocity, acceleration) of an object undergoing unknown acceleration based on noisy observations. Even with just a few inference steps between observations, tPC achieved performance approaching that of the optimal Kalman filter.

A key advantage of our model is its ability to learn the parameters of the generative model (matrices $A$, $B$, and $C$) using Hebbian plasticity. Even when starting with random matrices, tPC could learn to accurately predict observations. Interestingly, the model also implicitly encoded noise covariance information in its recurrent connections, without needing explicit representation of precision matrices.

Motion-Sensitive Receptive Fields

Perhaps most excitingly, when trained on natural movies, our tPC model developed spatiotemporal receptive fields resembling those observed in the visual cortex. These fields exhibited Gabor-like patterns and direction selectivity, a hallmark of motion-sensitive neurons in early visual areas.

Nonlinear Extensions

We extended the model to handle nonlinear dynamics by incorporating nonlinear activation functions. When tested on a simulated pendulum task, the nonlinear tPC significantly outperformed the linear model, accurately predicting the pendulum’s motion even at extreme angles where nonlinear effects are strongest.

Implications and Future Directions

Our temporal predictive coding framework has several important implications:

It provides a biologically plausible explanation for how the brain processes dynamic stimuli and performs temporal predictions.
It demonstrates that complex temporal filtering operations can be implemented in neural circuits using simple, local computations.
It offers a unified framework that connects normative theories of perception (Bayesian inference) with mechanistic models of neural circuits.
It suggests that the same computational principles might underlie both static and dynamic sensory processing in the brain.

Conclusion

The temporal predictive coding model we’ve developed bridges an important gap in our understanding of how the brain processes dynamic sensory inputs. By extending predictive coding to the temporal domain while maintaining its biological plausibility, our model provides a compelling computational mechanism for temporal prediction in neural circuits.

The fact that our model develops receptive fields resembling those in the visual cortex and approximates optimal filtering solutions suggests that temporal predictive coding may indeed capture fundamental principles of neural computation in the brain. As we continue to refine these models and test them against empirical data, we hope to gain deeper insights into the remarkable predictive capabilities of the brain.

This blog is based on the paper “Predictive coding networks for temporal prediction” by Beren Millidge, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper, and Rafal Bogacz, published in PLOS Computational Biology, April 2024. Link to the paper

3D Reconstruction of Human Olfactory Projection

Fri, 10 Nov 2023 00:00:00 +0000

Authors: Victoria F Low, Chinchien Lin, Shan Su, Mahyar Osanlouy, Mona Khan, Soroush Safaei, Gonzalo Maso Talou, Maurice A Curtis, Peter Mombaerts
Publication: Communications Biology (November 2024)
Code: Zenodo Repository
Interactive 3D Model: Neuroglancer Platform

Overview

This study presents a computational pipeline to reconstruct the 3D microanatomy of the human olfactory system, from the nasal cavity to the olfactory bulb—using fluorescence histology, deep learning, and high-performance computing (HPC). The workflow addresses challenges in processing terabyte-scale datasets and provides new insights into olfactory sensory neuron (OSN) distribution and axon trajectories.

Key Computational Contributions

First end-to-end pipeline for large-scale 3D reconstruction of human olfactory tissues (~7.45 cm³ specimen, 1234 sections).
CNN-based segmentation achieving Dice scores >0.85 for critical structures (OSNs, glomeruli, vasculature).
HPC-optimized registration reducing banana-effect artifacts via multi-resolution deformable alignment.
Public 3D dataset enabling interactive exploration of olfactory projection via Neuroglancer.

Methodology

Computational Pipeline

From fluorescence histology to 3D visualization – click to expand

Fluorescence Histology
- Quadruple staining: Hoechst (nuclei), UEA1 (OSNs), OMP (mature OSNs), VGLUT2 (axon terminals).
- Whole-slide scanning: 1.097 µm/pixel resolution, ~2.9 TB raw data.
CNN Segmentation
- Architecture: Modified 2D U-Net with 4 input channels (Hoechst + 3 markers).
- Training: Bootstrap approach with iterative ground truth expansion (20-45 sections/structure).
- Key layers:
```
# Simplified U-Net structure
encoder = [Conv2D(8→16→32), MaxPooling2D]
bottleneck = Conv2D(64) + Upsampling2D
decoder = [Conv2D(32→16→8), Concatenate(skip connections)]
```
HPC Registration
- Intra-block: Parallel registration of 247 blocks (5 sections each) using SimpleElastix.
- Inter-block: Banana-effect correction via affine + B-spline transformations between blocks.
- Metrics: Mutual information for intensity alignment, DSC for structural consistency.

Results

Segmentation Performance

Structure	Dice Score	Binary Cross-Entropy
Vasculature	0.808	0.0148
OSNs	0.760	0.0173
Glomeruli	0.779	0.0017

OSN segmentation achieved single-cell resolution in sparse regions but grouped cells in dense zones.

Registration Efficiency

1082 CPU hours on 96 Intel Xeon Gold 6136 cores
16% error reduction vs. sequential registration
Tolerance: ±80 µm axial drift corrected

Key Findings

OSN Count: ~2.7 million OSNs calculated via morphometric extrapolation (90% CI: 2.4–2.9M).
Fila Olfactoria: 34 foramina identified in cribriform plate (17/side).
Non-uniform Distribution: Olfactory epithelium showed serrated borders and posterior-anterior density gradient.

Implications

Technical Advancements

Scalable ML: Method enables processing of whole-brain datasets (~100x mouse brain volume).
Clinical Potential: Pipeline adaptable for Parkinson’s/Alzheimer’s studies via α-synuclein/tau staining.
Open Science: First public 3D olfactory dataset with ~5.8 GB/channel resolution.

Biological Insights

Challenges mouse-to-human extrapolation: ~10x fewer OSNs/glomerulus vs. mice.
Provides baseline for studying SARS-CoV-2 olfactory dysfunction.

Computational Tools Used

Segmentation: TensorFlow U-Net, Fiji for ground truth
Registration: SimpleElastix, ITK
Visualization: ParaView, Neuroglancer
HPC: New Zealand eScience Infrastructure (NeSI)

Interactive 3D exploration

The SPARC DRC

Wed, 20 Sep 2023 00:00:00 +0000

Authors: Mahyar Osanlouy, Anita Bandrowski, Bernard de Bono, David Brooks, Antonino M. Cassarà, Richard Christie, Nazanin Ebrahimi, Tom Gillespie, Jeffrey S. Grethe, Leonardo A. Guercio, Maci Heal, Mabelle Lin, Niels Kuster, Maryann E. Martone, Esra Neufeld, David P. Nickerson, Elias G. Soltani, Susan Tappan, Joost B. Wagenaar, Katie Zhuang, Peter J. Hunter
Publication: Front. Physiol. (June 2021)
Website: SPARC Science
Interactive 3D Models & Data: MAP Web App

Overview

The NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is an ambitious initiative aimed at revolutionizing our understanding of the autonomic nervous system (ANS) and its role in organ function. As the lead developer for the Mapping Core (MAP-Core) of the SPARC Data and Resource Center (DRC), I collaborated with an interdisciplinary team to create a comprehensive framework for curating, visualizing, and analyzing ANS data across different species.

The SPARC DRC serves as the technological foundation for the SPARC program, providing researchers worldwide with access to standardized experimental data, computational models, and visualization tools through a unified web portal (https://sparc.science). Our work addresses a critical gap in biomedical research: the need for standardized, reproducible, and interoperable data and computational resources to advance our understanding of neural control of organ function and develop effective neuromodulation therapies.

Key Contributions

Our team developed several groundbreaking innovations that form the technological foundation of the SPARC program:

1. FAIR-Compliant Data Management Platform

We implemented comprehensive data standards and workflows to ensure all SPARC data adheres to FAIR principles (Findable, Accessible, Interoperable, and Reusable). This includes standardized metadata schemas, dataset organization structures, and persistent identifiers for long-term data discoverability.

2. Knowledge Management System

We created a sophisticated knowledge graph integrating anatomical relationships, neural connectivity, and semantic annotations from multiple species. This system enables powerful cross-species comparisons and semantic searches that were previously impossible with traditional database approaches.

3. Multi-Scale Anatomical Mapping Framework

One of our most significant innovations is the development of 3D material coordinate systems (“scaffolds”) for organs and bodies. These scaffolds provide a revolutionary solution to a complex problem: how to map and compare data from organs that undergo substantial deformation (e.g., beating hearts, inflating lungs) across different experimental conditions and species.

4. Computational Modeling Platform

We developed o²S²PARC (“open, online simulations for SPARC”), an online computational environment that enables researchers to analyze data, develop models, and simulate neuromodulation scenarios using cloud computing resources.

Methodology

Developing Standardized Data Workflows

We established comprehensive curation and data standards to ensure consistency across all SPARC datasets. Key aspects include:

Implementation of the SPARC Dataset Structure (SDS) for consistent file organization
Development of the Minimal Information Standard (MIS) for semantic metadata
Integration with community ontologies and specialized annotation tools
Creation of SODA (Software for Organizing Data Automatically) to assist researchers in preparing data submissions

Creating Neural Connectivity Maps

To represent the complex connectivity of the ANS across different species, we:

Utilized the ApiNATOMY toolkit to create topological and semantic models of neural pathways
Generated interactive flatmap diagrams that visualize neural connectivity in 2D for multiple species
Linked these maps to the SPARC Knowledge Graph for data integration

3D Scaffold Framework for Anatomical Mapping

A cornerstone of our innovation is the development of the 3D scaffold system. This methodology addresses the fundamental challenge of mapping data from organs that undergo significant deformation. Our approach includes:

1. Material Coordinate System Development

We developed Scaffold-Maker, specialized CAD software that creates 3D material coordinate systems for body organs. These scaffolds use finite element meshes with smooth interpolation to represent complex anatomical geometries.

2. Cross-Species Anatomical Representation

We designed the scaffolds to accommodate topological differences between species, allowing for meaningful cross-species comparisons despite anatomical variations. For example, we created heart scaffolds that accommodate different numbers of pulmonary veins in humans (4), pigs (2), and rats (3).

3. Deformation-Invariant Mapping

Our material coordinate system maintains consistent references to tissue locations regardless of organ deformation. This allows data to be mapped and compared across different physiological states (e.g., cardiac contraction phases).

4. Integration with Whole-Body Models

We developed methods to embed organ scaffolds within whole-body coordinate systems, facilitating multi-scale data integration from cellular to organism levels.

Results

Our work has produced several tangible outcomes that demonstrate the power of the SPARC DRC infrastructure:

Comprehensive ANS Data Resource

The SPARC Portal now serves as the world’s most comprehensive resource for standardized ANS data, with:

Curated datasets from multiple species and organ systems
Rich semantic annotations linking experimental data to anatomical locations
Standardized protocols and metadata ensuring reproducibility

Species-Specific Anatomical Scaffolds

We have successfully created detailed 3D scaffolds for key organs across multiple species:

Heart scaffolds for human, pig, and rat with species-specific topologies
Colon scaffolds capturing species variations in haustra and taeniae coli structures
Whole-body scaffolds with embedded organ systems
Bladder, stomach, and lung scaffolds for integrating diverse experimental data

Neural Mapping Demonstrations

We have demonstrated successful registration of neural data to our scaffold systems:

Mapping of intrinsic cardiac neurons (ICNs) from rat hearts to a standardized cardiac scaffold
Integration of data from multiple specimens into “integrative” scaffolds
Preservation of spatial relationships across deformed organ states

Computational Modeling Environment

The o²S²PARC platform now provides:

Online access to sophisticated computational models
Tools for simulating neuromodulation effects
Reproducible workflows for data analysis
Collaborative environments for model development

Implications

The SPARC DRC infrastructure we’ve developed has significant implications for both basic science and clinical applications:

Advancing Basic Science

Our work enables new approaches to understanding the ANS by:

Facilitating cross-species comparisons of neural circuitry
Providing standardized reference systems for integrating multi-scale data
Creating a foundation for quantitative, data-driven neural mapping
Enabling reproducible computational analyses

Clinical Applications

The infrastructure directly supports the development of bioelectronic medicine by:

Providing detailed anatomical maps for targeting neuromodulation
Enabling simulation of device effects on neural activity
Supporting the optimization of stimulation parameters
Facilitating translation between animal models and human applications

Beyond SPARC

The methodologies and tools we’ve developed have applications beyond the ANS:

The scaffold concept can be applied to any deformable biological system
Our FAIR data management approaches set standards for other large-scale initiatives
The o²S²PARC computational platform can support diverse modeling applications

Conclusion

The SPARC DRC represents a paradigm shift in how we approach the study of the autonomic nervous system. By creating standardized frameworks for data organization, anatomical mapping, and computational modeling, we’ve laid the groundwork for a new era of quantitative, integrative neuroscience research that can directly inform the development of bioelectronic therapies. Our 3D scaffold methodology, in particular, solves the fundamental challenge of mapping biological data in deformable systems, enabling unprecedented integration of experimental data across scales, species, and physiological states.

AI-Driven Radiomics for Precision Prostate Cancer Therapy

Sun, 20 Aug 2023 00:00:00 +0000

AI-Driven Radiomics for Precision Prostate Cancer Therapy

Authors: Tsz Him Chan, Annette Haworth, Alan Wang, Mahyar Osanlouy et al.
Publication: EJNMMI Research (2023)
Code: Available on Request

Overview

This pioneering study developed an AI-powered radiomics pipeline to optimize biologically targeted radiation therapy (BiRT) for prostate cancer. By integrating PSMA PET/CT with multiparametric MRI (mpMRI), we created voxel-level predictions of tumor location and grade through advanced image registration and machine learning techniques.

Key Innovations

Multi-modal fusion: First study combining PSMA PET radiomics with DCE MRI perfusion parameters
Voxel-wise prediction: Achieved 0.89 AUC for tumor detection using 3D radiomic features
Grade differentiation: Developed two-stage RFC model separating high/low-grade disease (Accuracy: 0.67-0.99)
HPC-optimized registration: Reduced spatial uncertainty to 3.3mm using deformable histology alignment

Computational Pipeline

Data Integration: Co-registered PSMA PET/CT (5 scanners), mpMRI (2x Siemens 3T), and whole-mount histology
Key Steps:

align_pet_ct() → rigid_registration(mpMRI) →
deformable_registration(ex_vivo_MRI) →
histology_annotation_propagation()

Challenges Solved:
Bladder filling artifacts in PET
Partial volume effects in 3.27mm PET slices
Non-linear prostate deformation post-resection

2. Radiomic Feature Engineering

Modality	Features Extracted	Key Parameters
PSMA PET	3D LoG, LBP, GLCM textures	SUVmax, metabolic tumor volume
DCE MRI	Ktrans, iAUGC60, TTP perfusion maps	Pharmacokinetic modeling
ADC Maps	NGTDM coarseness, percentile values	b=1200 s/mm² diffusion restriction

Feature Selection:

ANOVA filtering → Gini impurity ranking
Final feature set: 50 most discriminative parameters

Machine Learning Architecture

Tumor Detection Model

Two-stage RFC Framework:

Location Detection: 842 sensitivity/804 specificity
Grade Classification: Low vs High Grade (ISUP ≥3)

Performance Comparison:

Model	AUC	Sensitivity	Specificity
PET Alone	0.865	0.781	0.799
mpMRI Alone	0.882	0.802	0.801
Combined	0.890	0.842	0.804

Receiver operating characteristics for different models

Key Radiomic Predictors

PET: 3D LoG(σ=3mm) minimum
ADC: NGTDM Coarseness
DCE MRI: Ktrans 90th percentile

Clinical Implications

Personalized Radiotherapy: Enables voxel-level dose painting based on metabolic/perfusion features
Early Recurrence Prediction: High-grade lesions showed 2.8× higher PSMA uptake (p<0.01)
Technical Impact:
Solved partial volume effects in PET-guided planning
Demonstrated perfusion > diffusion for grade prediction
Open-source registration framework [3D Slicer Plugin]

Future Directions

Multi-institutional validation across PET/MRI scanners
DL Enhancement: Replace handcrafted features with 3D CNNs
Real-time Adaptation: Integrate with MR-Linac systems

This project received funding from Prostate Cancer Foundation of Australia and Health Research Council of New Zealand.

Neuroscience | Mahyar's world 🌏

Temporal Predictive Coding

Overview

Key Contributions

Methodology

Core Principles

Comparison to Kalman Filtering

Results

1. Robust Tracking of Dynamic States

2. Learning Synaptic Weights

3. Biological Receptive Fields

4. Nonlinear Dynamics

Implications

Temporal Predictive Coding: A New Framework for Neural Processing of Dynamic Stimuli

Introduction

The Temporal Predictive Coding Model

Generative Model and Free Energy

Neural Implementation

We proposed multiple neural circuit implementations of this model:

Neural circuit implementation

Relationship to Kalman Filtering

Experimental Results

Performance in Linear Filtering Tasks

Motion-Sensitive Receptive Fields

Nonlinear Extensions

Implications and Future Directions

Conclusion

3D Reconstruction of Human Olfactory Projection

Overview

Key Computational Contributions

Methodology

Computational Pipeline

Results

Segmentation Performance

Registration Efficiency

Key Findings

Implications

Technical Advancements

Biological Insights

Computational Tools Used

The SPARC DRC

Overview

Key Contributions

1. FAIR-Compliant Data Management Platform

2. Knowledge Management System

3. Multi-Scale Anatomical Mapping Framework

4. Computational Modeling Platform

Methodology

Developing Standardized Data Workflows

Creating Neural Connectivity Maps

3D Scaffold Framework for Anatomical Mapping

1. Material Coordinate System Development

2. Cross-Species Anatomical Representation

3. Deformation-Invariant Mapping

4. Integration with Whole-Body Models

Results

Comprehensive ANS Data Resource

Species-Specific Anatomical Scaffolds

Neural Mapping Demonstrations

Computational Modeling Environment

Implications

Advancing Basic Science

Clinical Applications

Beyond SPARC

Conclusion

AI-Driven Radiomics for Precision Prostate Cancer Therapy

AI-Driven Radiomics for Precision Prostate Cancer Therapy

Overview

Key Innovations

Computational Pipeline

1. Multi-modal Image Registration

2. Radiomic Feature Engineering

Machine Learning Architecture

Tumor Detection Model

Key Radiomic Predictors

Clinical Implications

Future Directions