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1. Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance

   https://doi.org/10.1371/journal.pcbi.1010418  

   Stock, Christopher H; Harvey, Sarah E; Ocko, Samuel A; Ganguli, Surya (September 2022, PLOS Computational Biology)

   Richards, Blake A (Ed.)

   We introduce a novel, biologically plausible local learning rule that provably increases the robustness of neural dynamics to noise in nonlinear recurrent neural networks with homogeneous nonlinearities. Our learning rule achieves higher noise robustness without sacrificing performance on the task and without requiring any knowledge of the particular task. The plasticity dynamics—an integrable dynamical system operating on the weights of the network—maintains a multiplicity of conserved quantities, most notably the network’s entire temporal map of input to output trajectories. The outcome of our learning rule is a synaptic balancing between the incoming and outgoing synapses of every neuron. This synaptic balancing rule is consistent with many known aspects of experimentally observed heterosynaptic plasticity, and moreover makes new experimentally testable predictions relating plasticity at the incoming and outgoing synapses of individual neurons. Overall, this work provides a novel, practical local learning rule that exactly preserves overall network function and, in doing so, provides new conceptual bridges between the disparate worlds of the neurobiology of heterosynaptic plasticity, the engineering of regularized noise-robust networks, and the mathematics of integrable Lax dynamical systems. 

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2. Theory and model of thalamocortical processing in decision making under uncertainty. Thalamocortical Interactions Gordon Research Conference 2024, Ventura, California, February 2024. Abstract, Poster.

   Wang, Mien Brabeeba; Lynch, Nancy; Halassa, Michael (February 2024, Thalamocortical Interactions Gordon Research Conference 2024)

   Animals flexibly select actions that maximize future rewards despite facing uncertainty in sen- sory inputs, action-outcome associations or contexts. The computational and circuit mechanisms underlying this ability are poorly understood. A clue to such computations can be found in the neural systems involved in representing sensory features, sensorimotor-outcome associations and contexts. Specifically, the basal ganglia (BG) have been implicated in forming sensorimotor-outcome association [1] while the thalamocortical loop between the prefrontal cortex (PFC) and mediodorsal thalamus (MD) has been shown to engage in contextual representations [2, 3]. Interestingly, both human and non-human animal experiments indicate that the MD represents different forms of uncertainty [3, 4]. However, finding evidence for uncertainty representation gives little insight into how it is utilized to drive behavior. Normative theories have excelled at providing such computational insights. For example, de- ploying traditional machine learning algorithms to fit human decision-making behavior has clarified how associative uncertainty alters exploratory behavior [5, 6]. However, despite their computa- tional insight and ability to fit behaviors, normative models cannot be directly related to neural mechanisms. Therefore, a critical gap exists between what we know about the neural representa- tion of uncertainty on one end and the computational functions uncertainty serves in cognition. This gap can be filled with mechanistic neural models that can approximate normative models as well as generate experimentally observed neural representations. In this work, we build a mechanistic cortico-thalamo-BG loop network model that directly fills this gap. The model includes computationally-relevant mechanistic details of both BG and thalamocortical circuits such as distributional activities of dopamine [7] and thalamocortical pro- jection modulating cortical effective connectivity [3] and plasticity [8] via interneurons. We show that our network can more efficiently and flexibly explore various environments compared to com- monly used machine learning algorithms and we show that the mechanistic features we include are crucial for handling different types of uncertainty in decision-making. Furthermore, through derivation and mathematical proofs, we approximate our models to two novel normative theories. We show mathematically the first has near-optimal performance on bandit tasks. The second is a generalization on the well-known CUMSUM algorithm, which is known to be optimal on single change point detection tasks [9]. Our normative model expands on this by detecting multiple sequential contextual changes. To our knowledge, our work is the first to link computational in- sights, normative models and neural realization together in decision-making under various forms of uncertainty. 

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3. Formalizing locality for normative synaptic plasticity models

   Bredenberg, E; Williams, E; Savin, C; Richards, B; Lajoie, G (September 2023, Advances In Neural Information Processing Systems (NeurIPS))

   In recent years, many researchers have proposed new models for synaptic plasticity in the brain based on principles of machine learning. The central motivation has been the development of learning algorithms that are able to learn difficult tasks while qualifying as "biologically plausible". However, the concept of a biologically plausible learning algorithm is only heuristically defined as an algorithm that is potentially implementable by biological neural networks. Further, claims that neural circuits could implement any given algorithm typically rest on an amorphous concept of "locality" (both in space and time). As a result, it is unclear what many proposed local learning algorithms actually predict biologically, and which of these are consequently good candidates for experimental investigation. Here, we address this lack of clarity by proposing formal and operational definitions of locality. Specifically, we define different classes of locality, each of which makes clear what quantities cannot be included in a learning rule if an algorithm is to qualify as local with respect to a given (biological) constraint. We subsequently use this framework to distill testable predictions from various classes of biologically plausible synaptic plasticity models that are robust to arbitrary choices about neural network architecture. Therefore, our framework can be used to guide claims of biological plausibility and to identify potential means of experimentally falsifying a proposed learning algorithm for the brain. 

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4. Neural representational geometry underlies few-shot concept learning

   https://doi.org/10.1073/pnas.2200800119  

   Sorscher, Ben; Ganguli, Surya; Sompolinsky, Haim (October 2022, Proceedings of the National Academy of Sciences)

   Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing that it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to predictions about behavioral outcomes by delineating several fundamental and measurable geometric properties of neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. This theory reveals, for instance, that high-dimensional manifolds enhance the ability to learn new concepts from few examples. Intriguingly, we observe striking mismatches between the geometry of manifolds in the primate visual pathway and in trained DNNs. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments. 

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5. The neuron as a direct data-driven controller

   https://doi.org/10.1073/pnas.2311893121  

   Moore, Jason J; Genkin, Alexander; Tournoy, Magnus; Pughe-Sanford, Joshua L; de_Ruyter_van_Steveninck, Rob R; Chklovskii, Dmitri B (July 2024, Proceedings of the National Academy of Sciences)

   In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch–Pitts–Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks. 

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