Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Abstract

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Keywords: artificial neural networks; behavior; cognition; neuromodulators; plasticity; vision.

Figure 1.

Dendritic integration of inputs and neuromodulation-aware deep learning. A, How a pyramidal neuron responds to an input depends on dendritic location. Feedforward inputs located near the soma directly drive the firing rate of the neuron, whereas feedback inputs on apical dendrites affect burst firing (P(Burst)). B, The firing rate of presynaptic and postsynaptic neurons and P(Burst) control plasticity long-term potentiation - LTP; long-term depression - LTD. C, The dendritic integration of feedback and feedforward inputs by cortical neurons could solve the credit assignment problem in hierarchical ANNs. D, Diagram of how neuromodulation can be integrated by ANNs. Left, Error signal of a network perturbation is carried through a global neuromodulatory influence. Middle, Error signals are carried through node-specific neuromodulatory inputs. Right, Various neuromodulatory inputs could take part in signaling distinct error functions. A–C, Adapted from Payeur et al. (2021).

Figure 2.

Feedforward versus bio-inspired CNNs. By adding connections inspired by the anatomy and physiology of the visual system, such as lateral (e.g., center surround suppression) or feedback (e.g., top-down predictions), CNNs with recurrent connections show improved accuracy. Black and red arrows represent feedforward and recurrent connections, respectively. Adapted from Lindsay et al. (2022).

Figure 3.

Using RNNs to study neuronal correlates of complex tasks. In an example task where various contexts (u1) and sensory cues (u2) guide task outputs (z) or decisions, recordings of neurons from associative brain areas (e.g., pre-frontal cortex - PFC) show multidimensional encoding of task variables by individual neurons. Representing population dynamics in neural state space where each point in space represents a unique pattern of neuronal activity that is useful to dissect how the correlated activity of a large number of neurons represents task variables. To model physiological dynamics, RNNs are trained to perform a similar task. Key features of physiological dynamics of neuronal populations are reproduced by RNNs. Complex perturbation studies can thus be performed with these trained RNNs to test causality. In a recent study, Langdon and Engel (2022) found that low-dimensional latent circuits can be extracted from high-dimensional RNN dynamics and used to perform patterned connectivity perturbations. Adapted from Mante et al. (2013) and Langdon and Engel (2022).