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Behavioral Timescale Synaptic Plasticity (BTSP) for biologically plausible credit assignment across multiple layers via top-down gating of dendritic plasticity
A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflect a diversity of excitatory (only positive weights) and inhibitory (only negative weights) cell types, they do not prescribe a clear mechanism for how to coordinate learning across multiple layers and propagate error information accurately across the network. In recent years, several groups have drawn inspiration from the known dendritic non-linearities of pyramidal neurons to propose new learning rules and network architectures that enable biologically plausible multi-layer learning by processing error information in segregated dendrites. Meanwhile, recent experimental results from the hippocampus have revealed a new form of plasticity—Behavioral Timescale Synaptic Plasticity (BTSP)—in which large dendritic depolarizations rapidly reshape synaptic weights and stimulus selectivity with as little as a single stimulus presentation (“one-shot learning”). Here we explore the implications of this new learning rule through a biologically plausible implementation in a rate neuron network. We demonstrate that regulation of dendritic spiking and BTSP by top-down feedback signals can effectively coordinate plasticity across multiple network layers in a simple pattern recognition task. By analyzing hidden feature representations and weight trajectories during learning, we show the differences between networks trained with standard backpropagation, Hebbian learning rules, and BTSP.
NMC4 Short Talk: Neural Representation: Bridging Neuroscience and Philosophy
We understand the brain in representational terms. E.g., we understand spatial navigation by appealing to the spatial properties that hippocampal cells represent, and the operations hippocampal circuits perform on those representations (Moser et al., 2008). Philosophers have been concerned with the nature of representation, and recently neuroscientists entered the debate, focusing specifically on neural representations. (Baker & Lansdell, n.d.; Egan, 2019; Piccinini & Shagrir, 2014; Poldrack, 2020; Shagrir, 2001). We want to know what representations are, how to discover them in the brain, and why they matter so much for our understanding of the brain. Those questions are framed in a traditional philosophical way: we start with explanations that use representational notions, and to more deeply understand those explanations we ask, what are representations — what is the definition of representation? What is it for some bit of neural activity to be a representation? I argue that there is an alternative, and much more fruitful, approach. Rather than asking what representations are, we should ask what the use of representational *notions* allows us to do in neuroscience — what thinking in representational terms helps scientists do or explain. I argue that this framing offers more fruitful ground for interdisciplinary collaboration by distinguishing the philosophical concerns that have a place in neuroscience from those that don’t (namely the definitional or metaphysical questions about representation). And I argue for a particular view of representational notions: they allow us to impose the structure of one domain onto another as a model of its causal structue. So, e.g., thinking about the hippocampus as representing spatial properties is a way of taking structures in those spatial properties, and projecting those structures (and algorithms that would implement them) them onto the brain as models of its causal structure.
Data-driven Artificial Social Intelligence: From Social Appropriateness to Fairness
Designing artificially intelligent systems and interfaces with socio-emotional skills is a challenging task. Progress in industry and developments in academia provide us a positive outlook, however, the artificial social and emotional intelligence of the current technology is still limited. My lab’s research has been pushing the state of the art in a wide spectrum of research topics in this area, including the design and creation of new datasets; novel feature representations and learning algorithms for sensing and understanding human nonverbal behaviours in solo, dyadic and group settings; designing longitudinal human-robot interaction studies for wellbeing; and investigating how to mitigate the bias that creeps into these systems. In this talk, I will present some of my research team’s explorations in these areas including social appropriateness of robot actions, virtual reality based cognitive training with affective adaptation, and bias and fairness in data-driven emotionally intelligent systems.
V2 builds a generalizable texture representation
COSYNE 2023
Emotional feature representation in prefrontal ensemble dynamics
FENS Forum 2024
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