Hebbian learning

…drift is a result of noisy continual learning in the brain. Previous computational studies have demonstrated that drift could occur under different types of noise such as synaptic noise, or sample stochasticity. However, exact contribution of various noises remains elusive. For example, it’s unclear what’s the contribution of stimuli that are relevant for the task, and the ones that are continually present in the environment but the agent learns to ignore in a context. In this study, using theory a

…feedback model with unsupervised Hebbian learning. Our model predicts a power- law decay in mean population activity over days of odor expo

…strengthens effective connectivity via Hebbian-like plasticity. Linear network models reveal that these temporal dependencies between signal and noise correlations emerge only when Hebbian learning balances stochastic synaptic changes, preventing functional degradation

Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are inspired by our understanding of how the brain processes information. Hence, we can further advance the development of SNNs by improving our current knowledge of how biological neural networks learn through spike-based local plasticity mechanisms. In recent years, many spike-based

Learning in neuronal circuits requires coordinated modifications of synaptic strengths across hierarchical networks to refine computation and improve behavioral outcomes. This coordination requires solving the credit assignment problem. Yet, how neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience [1]. Several modeling studies have suggested putative biological mechanisms for back-propagating error signals through multi-layer networks. These functionally motivated models assume distinct neuronal compartments representing local error signals t

…variability: to facilitate faster learning in feedforward-recurrent networks through a self-stabilizing feedback mechanism. We explore this hypothesis in the framework of learning novel stimulus representations in pre-structured recurren

We can draw on both episodic memories and semantic knowledge to guide behavior in novel situations. Despite growing interest, key questions remain on the interplay between these systems. How do semantic representations guide the retrieval of episodic memories? How are low-level semantic relationships abstracted from everyday observations (episodes)? How are these low-level relationships transformed to higher-order knowledge structures? By bridging insights from memory-neuroscience with findings in memory-based decision-making, we propose that the sequential sampling of related episodic memorie

Biological neuronal networks exhibit hallmark features such as oscillatory dynamics, heterogeneity, modularity, and conduction delays. However, it has remained unclear to what extent these serve computational purposes. Inspired by physiological findings, we simulate recurrent networks of damped harmonic oscillators (DHO) in which one network node represents the aggregate activity of an underlying E-I circuit such as a cortical column (HORNs, see figure), at the same time reducing single-node oscillatory dynamics to its most basic form. We successively endow the networks with additional biologi

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The r

…(RNNs) with binary neurons and Hebbian learning rules find that they can store at most O(N) memories, where N is the number of neurons. However, under the physiological constraint that neurons are sparsely connected, this capacity is dramatically reduced to O(K), where K is the average number of presynaptic neurons (estimated to be O(10^3 ∼ 10^4) in m

…by hand or fitted by complex machine learning algorithms to yield stable activity and computations. Here we present a method to effic

The learning rules implemented by neural networks in the brain are currently unknown. Understanding how candidate learning rules influence learned representations and prediction dynamics in neural networks could provide a link between experimentally accessible neural response measurements and possible underlying learning rules. To gain insight into how learning rules alter representation dynamics, we provide theoretical analysis of a broad class of learning rules which include exact gradient descent (GD), Feedback-Alignment (FA), Direct-Feedback-Alignment (DFA), Hebbian learning (Hebb) and gat

Insects demonstrate a remarkable ability to navigate the world -- foraging for food and returning to their nests over large distances. Simple neural circuits have been shown to underlie insect navigation behaviours, encoding the animal's head direction and velocity vector as a sinusoidal activity pattern\cite{heinze2007maplike,lyuBuildingAllocentricTravelling2022}. We offer novel mathematical insights into the benefits of this sinusoidal encoding, suggesting that it might offer an evolutionary advantage in terms of noise resilience. Previous work has recognised that encoding vectors as sinusoi

Cognitive maps are hypothesized memory systems that organize sensory experience into knowledge that can be used flexibly in novel scenarios, such as finding new routes during navigation. Evidence for this hypothesis has been largely limited to experiments with fully observable sensory information. However, to serve as a genuine internal source of knowledge, cognitive maps should also be able to support mental computations in the absence of external cues. To test this hypothesis, we developed a mental navigation task in which monkeys must produce vectors that correspond to moving between pairs

…Hebbian Learning (HL), a local form of plasticity that uses pre- and postsynaptic activity to change synaptic efficacy, has been used extensively in receptive field models, sparse coding, independent and principal component analysis, an

Juvenile songbirds learn to imitate adult vocalizations. Song acquisition and production is governed by a dedicated neural circuitry that involves two parallel pathways: a cortical pathway required for production and a basal ganglia-thalamo-cortical (BG) pathway necessary for plasticity. The BG pathway induces variability in production during vocal exploration, receives a performance signal via midbrain dopaminergic projections and drives a motor bias that corrects vocal errors. This dopamine-modulated change in vocal output, induced by the BG is gradually consolidated within the cortical path

Understanding the neural code for vision remains a major challenge for neuroscience, with recent efforts influenced by the success of convolutional neural networks. However, spiking neurons make biological vision fundamentally different from the firing rate model implicit in these artificial neural networks. In region V1 of the primate visual cortex, a flashed image is processed in tens of milliseconds, allowing time for only a few action potentials, typically across a small subset of neurons. Moreover, persistent eye motion (ocular drift) changes the location of retinal responses, and thus

…and efficiency of reinforcement learning algorithms operating over these representations and is supported by behavioural and electrophysiological evidence. However, it is unclear how the SR might be learnt in the brain. Temporal difference learning, commonly used to learn SRs in artificial agents, is not known to be impl

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes th

…to task acquisition (“lazy” and “rich” learning) which trade off learning speed for robustness, and depend on the initial weights scale and network sparsity. I will chart the predictions of these two schemes for a context-dependent decision-making task, showing that the rich solution is