Each of the five groups of images represents an image set.

In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets.

Each of the five groups of images represents an image set.

Moreover, FFA offers bio-plausibility compared to traditional back-propagation (BP) methods in implementation.

Deep neural networks (DNNs) trained on visual tasks develop feature representations that resemble those in the human visual system. Although DNN-based encoding models can accurately predict brain responses to visual stimuli, they offer limited insight into the specific features driving these responses. Here, we demonstrate that activation maximization -- a technique designed to interpret vision DNNs -- can be applied to DNN-based encoding models of the human brain. We extract and adaptively downsample activations from multiple layers of a pretrained Inception V3 network, then use linear regression to predict fMRI responses. This yields a full image-computable model of brain responses. Next, we apply activation maximization to generate images optimized for predicted responses in individual cortical voxels. We find that these images contain visual characteristics that qualitatively correspond with known selectivity and enable exploration of selectivity across the visual cortex. We further extend our method to whole regions of interest (ROIs) of the brain and validate its efficacy by presenting these images to human participants in an fMRI study. We find that the generated images reliably drive activity in targeted regions across both low- and high-level visual areas and across subjects. These results demonstrate that activation maximization can be successfully applied to DNN-based encoding models. By addressing key limitations of alternative approaches that require natively generative models, our approach enables flexible characterization and modulation of responses across the human visual system.

In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets.

We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily stem from the prevalent reliance on back-propagation for model training and fine-tuning. Building upon the recently proposed Forward-Forward Algorithm (FFA), we introduce the Convolutional Forward-Forward Algorithm (CFFA), a parameter-efficient reformulation that is suitable for advanced image analysis and overcomes the speed and generalisation constraints of the original FFA. To address hyper-parameter sensitivity of FFAs we are also introducing a self-adapting framework SaFF-Net fine-tuning parameters during warmup and training in parallel. Our approach enables more effective model training and eliminates the previously essential requirement for an arbitrarily chosen Goodness function in FFA. We evaluate our approach on several benchmarking datasets in comparison with standard Back-Propagation (BP) neural networks showing that FFA-based networks with notably fewer parameters and function evaluations can compete with standard models, especially, in one-shot scenarios and large batch sizes. The code will be available at the time of the conference.