The last decade has seen a burgeoning interest in studying the neural and computational mechanisms that underpin social learning (learning from others). Many findings support the view that learning from other people is underpinned by the same, ‘domain-general’, mechanisms underpinning learning from non-social stimuli. Despite this, the idea that humans possess social-specific learning mechanisms - adaptive specializations moulded by natural selection to cope with the pressures of group living - persists. In this talk I explore the persistence of this idea. First, I present dissociations between social and non-social learning - patterns of data which are difficult to explain under the domain-general thesis and which therefore support the idea that we have evolved special mechanisms for social learning. Subsequently, I argue that most studies that have dissociated social and non-social learning have employed paradigms in which social information comprises a secondary, additional, source of information that can be used to supplement learning from non-social stimuli. Thus, in most extant paradigms, social and non-social learning differ both in terms of social nature (social or non-social) and status (primary or secondary). I conclude that status is an important driver of apparent differences between social and non-social learning. When we account for differences in status, we see that social and non-social learning share common (dopamine-mediated) mechanisms.

Young children have sophisticated representations of their visual and linguistic environment. Where do these representations come from? How much knowledge arises through generic learning mechanisms applied to sensory data, and how much requires more substantive (possibly innate) inductive biases? We examine these questions by training neural networks solely on longitudinal data collected from a single child (Sullivan et al., 2020), consisting of egocentric video and audio streams. Our principal findings are as follows: 1) Based on visual only training, neural networks can acquire high-level visual features that are broadly useful across categorization and segmentation tasks. 2) Based on language only training, networks can acquire meaningful clusters of words and sentence-level syntactic sensitivity. 3) Based on paired visual and language training, networks can acquire word-referent mappings from tens of noisy examples and align their multi-modal conceptual systems. Taken together, our results show how sophisticated visual and linguistic representations can arise through data-driven learning applied to one child’s first-person experience.

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally show that our memory-equipped SNN model outperforms state-of-the-art deep learning mechanisms in a sequential pattern-memorization task, as well as demonstrate superior out-of-distribution generalization capabilities compared to these models. We further show that our model can be successfully applied to one-shot learning and classification of handwritten characters, improving over the state-of-the-art SNN model. We also demonstrate the capability of our model to learn associations for audio to image synthesis from spoken and handwritten digits. Our SNN model further presents a novel solution to a variety of cognitive question answering tasks from a standard benchmark, achieving comparable performance to both memory-augmented ANN and SNN-based state-of-the-art solutions to this problem. Finally we demonstrate that our model is able to learn from rewards on an episodic reinforcement learning task and attain near-optimal strategy on a memory-based card game. Hence, our results show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. Since local Hebbian plasticity can easily be implemented in neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. In this talk, I will present our recent work where we introduced a PyTorch simulation framework of memristive crossbar arrays that enables accurate investigation of such challenges. I will show that recently proposed e-prop learning rule can be used to train spiking RNNs whose weights are emulated in the presented simulation framework. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial device non-idealities. I will mention several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates.

A core question in human development is how we bring meaning to conventional symbols. This question is deeply connected to understanding how children learn mathematics—a symbol system with unique vocabularies, syntaxes, and written forms. In this talk, I will present findings from a program of research focused on children’s acquisition of place value symbols (i.e., multidigit number meanings). The base-10 symbol system presents a variety of obstacles to children, particularly in English. Children who cannot overcome these obstacles face years of struggle as they progress through the mathematics curriculum of the upper elementary and middle school grades. Through a combination of longitudinal, cross-sectional, and pretest-training-posttest approaches, I aim to illuminate relational learning mechanisms by which children sometimes succeed in mastering the place value system, as well as instructional techniques we might use to help those who do not.

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans – which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of-the-art neural interface performance. I’ll then survey recent work that highlights the likelihood that BMIs, much like natural motor learning, engages multiple distributed learning mechanisms that can be carefully interrogated with BMI.