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Samuel J Gershman

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4 papers

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NeurIPS Conference 2025 Conference Paper

Blending Complementary Memory Systems in Hybrid Quadratic-Linear Transformers

Kazuki Irie
Morris Yau
Samuel J Gershman

We develop hybrid memory architectures for general-purpose sequence processing neural networks, that combine key-value memory using softmax attention (KV-memory) with fast weight memory through dynamic synaptic modulation (FW-memory)---the core principles of quadratic and linear transformers, respectively. These two memory systems have complementary but individually limited properties: KV-memory offers precise retrieval but is constrained by quadratic complexity in sequence length, while FW-memory supports arbitrarily long sequences and enables more expressive computation but sacrifices precise recall. We propose and compare three methods to blend these two systems into a single memory system, differing in how and when input information is delivered to each system, to leverage the strengths of both. We conduct experiments on general language modeling and retrieval tasks by training 340M- and 1. 3B-parameter models from scratch, as well as on synthetic algorithmic tasks designed to precisely illustrate the benefits of certain hybrid methods over others. We also evaluate our hybrid memory systems on reinforcement learning in partially observable environments. Overall, we demonstrate how a well-designed hybrid can overcome the limitations of its individual components, offering new insights into the design principle of neural memory systems.

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NeurIPS Conference 2025 Conference Paper

Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules

John Vastola
Samuel J Gershman
Kanaka Rajan

Learning rules—prescriptions for updating model parameters to improve performance—are typically assumed rather than derived. Why do some learning rules work better than others, and under what assumptions can a given rule be considered optimal? We propose a theoretical framework that casts learning rules as policies for navigating (partially observable) loss landscapes, and identifies optimal rules as solutions to an associated optimal control problem. A range of well-known rules emerge naturally within this framework under different assumptions: gradient descent from short-horizon optimization, momentum from longer-horizon planning, natural gradients from accounting for parameter space geometry, non-gradient rules from partial controllability, and adaptive optimizers like Adam from online Bayesian inference of loss landscape shape. We further show that continual learning strategies like weight resetting can be understood as optimal responses to task uncertainty. By unifying these phenomena under a single objective, our framework clarifies the computational structure of learning and offers a principled foundation for designing adaptive algorithms.

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NeurIPS Conference 2023 Conference Paper

Successor-Predecessor Intrinsic Exploration

Changmin Yu
Neil Burgess
Maneesh Sahani
Samuel J Gershman

Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards. Although the study of intrinsic rewards has a long history, existing methods focus on composing the intrinsic reward based on measures of future prospects of states, ignoring the information contained in the retrospective structure of transition sequences. Here we argue that the agent can utilise retrospective information to generate explorative behaviour with structure-awareness, facilitating efficient exploration based on global instead of local information. We propose Successor-Predecessor Intrinsic Exploration (SPIE), an exploration algorithm based on a novel intrinsic reward combining prospective and retrospective information. We show that SPIE yields more efficient and ethologically plausible exploratory behaviour in environments with sparse rewards and bottleneck states than competing methods. We also implement SPIE in deep reinforcement learning agents, and show that the resulting agent achieves stronger empirical performance than existing methods on sparse-reward Atari games.

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NeurIPS Conference 2021 Conference Paper

CCNLab: A Benchmarking Framework for Computational Cognitive Neuroscience

Nikhil Bhattasali
Momchil Tomov
Samuel J Gershman

CCNLab is a benchmark for evaluating computational cognitive neuroscience models on empirical data. As a starting point, its focus is classical conditioning, which studies how animals predict reward and punishment in the environment. CCNLab includes a collection of simulations of seminal experiments expressed under a common API, as wells as tools for visualizing and comparing simulated data with empirical data. CCNLab is broad, incorporating representative experiments from different categories of phenomena; flexible, allowing the straightforward addition of new experiments; and easy-to-use, so researchers can focus on developing better models. We envision CCNLab as a testbed for unifying computational theories of learning in the brain. We also hope that it can broadly accelerate neuroscience research and facilitate interaction between the fields of neuroscience, psychology, and artificial intelligence.

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