Anindya Ghosh

Alejandro Rodriguez-Garcia, Anindya Ghosh, Jie Mei et al.

Recent progress in artificial intelligence (AI) has been driven by insights from physics and neuroscience, particularly through the development of artificial neural networks (ANNs) capable of complex cognitive tasks such as vision and language processing. Despite these advances, they struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency -- capabilities that biological systems handle seamlessly. Specifically, neuromorphic systems and artificial neural networks often overlook two key biophysical properties of neural circuits: neuronal diversity and cell-specific neuromodulation. These mechanisms, essential for regulating dynamic learning across brain scales, allow neuromodulators to introduce degeneracy in biological neural networks, ensuring stability and adaptability under changing conditions. In this article, we summarize recent bioinspired models, learning rules, and architectures, and propose a framework for augmenting ANNs, which has the potential to bridge the gap between neuroscience and AI through neurobiological first principles. Our proposed dual-framework approach leverages spiking neural networks to emulate diverse spiking behaviors and dendritic compartmental dynamics, thereby simulating the morphological and functional diversity of neuronal computations. Finally, we outline how integrating these biophysical principles into task-driven spiking neural networks and neuromorphic systems provides scalable solutions for continual learning, adaptability, robustness, and resource-efficiency. Additionally, this approach will not only provide insights into how emergent behaviors arise in neural networks but also catalyze the development of more efficient, reliable, and intelligent neuromorphic systems and robotic agents.

Alejandro Rodriguez-Garcia, Anindya Ghosh, Srikanth Ramaswamy

Recent studies in continual learning have identified a transient drop in performance on mastered tasks when assimilating new ones, known as the stability gap. Such dynamics contradict the objectives of continual learning, revealing a lack of robustness in mitigating forgetting, and notably, persisting even under an ideal joint-loss regime. Examining this gap within this idealized joint training context is critical to isolate it from other sources of forgetting. We argue that it reflects an imbalance between rapid adaptation and robust retention at task boundaries, underscoring the need to investigate mechanisms that reconcile plasticity and stability within continual learning frameworks. Biological brains navigate a similar dilemma by operating concurrently on multiple timescales, leveraging neuromodulatory signals to modulate synaptic plasticity. However, artificial networks lack native multitimescale dynamics, and although optimizers like momentum-SGD and Adam introduce implicit timescale regularization, they still exhibit stability gaps. Inspired by locus coeruleus mediated noradrenergic bursts, which transiently enhance neuronal gain under uncertainty to facilitate sensory assimilation, we propose uncertainty-modulated gain dynamics - an adaptive mechanism that approximates a two-timescale optimizer and dynamically balances integration of knowledge with minimal interference on previously consolidated information. We evaluate our mechanism on domain-incremental and class-incremental variants of the MNIST and CIFAR benchmarks under joint training, demonstrating that uncertainty-modulated gain dynamics effectively attenuate the stability gap. Finally, our analysis elucidates how gain modulation replicates noradrenergic functions in cortical circuits, offering mechanistic insights into reducing stability gaps and enhance performance in continual learning tasks.