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Kenneth Miller

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4 papers

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NeurIPS Conference 2025 Conference Paper

Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets

Ji Xia
Yizi Zhang
Shuqi Wang
Genevera Allen
Liam Paninski
Cole Hurwitz
Kenneth Miller

Characterizing interactions between brain areas is a fundamental goal of systems neuroscience. While such analyses are possible when areas are recorded simultaneously, it is rare to observe all combinations of areas of interest within a single animal or recording session. How can we leverage multi-animal datasets to better understand multi-area interactions? Building on recent progress in large-scale, multi-animal models, we introduce NeuroPaint, a masked autoencoding approach for inferring the dynamics of unobserved brain areas. By training across animals with overlapping subsets of recorded areas, NeuroPaint learns to reconstruct activity in missing areas based on shared structure across individuals. We train and evaluate our approach on both synthetic data and two multi-animal, multi-area Neuropixels datasets. Our results demonstrate that models trained across animals with partial observations can successfully in-paint the dynamics of unrecorded areas, enabling multi-area analyses that transcend the limitations of any single experiment.

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NeurIPS Conference 2002 Conference Paper

Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule

Michael Eisele
Kenneth Miller

Cortical synaptic plasticity depends on the relative timing of pre- and postsynaptic spikes and also on the temporal pattern of presynaptic spikes and of postsynaptic spikes. We study the hypothesis that cortical synap- tic plasticity does not associate individual spikes, but rather whole ﬁr- ing episodes, and depends only on when these episodes start and how long they last, but as little as possible on the timing of individual spikes. Here we present the mathematical background for such a study. Stan- dard methods from hidden Markov models are used to deﬁne what “ﬁr- ing episodes” are. Estimating the probability of being in such an episode requires not only the knowledge of past spikes, but also of future spikes. We show how to construct a causal learning rule, which depends only on past spikes, but associates pre- and postsynaptic ﬁring episodes as if it also knew future spikes. We also show that this learning rule agrees with some features of synaptic plasticity in superﬁcial layers of rat visual cortex (Froemke and Dan, Nature 416: 433, 2002).

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NeurIPS Conference 1989 Conference Paper

Analysis of Linsker's Simulations of Hebbian Rules

David MacKay
Kenneth Miller

Linsker has reported the development of centre---surround receptive fields and oriented receptive fields in simulations of a Hebb-type equation in a linear network. The dynamics of the learning rule are analysed in terms of the eigenvectors of the covariance matrix of cell activities. Analytic and computational results for Linsker's covariance matrices, and some general theorems, lead to an expla(cid: 173) nation of the emergence of centre---surround and certain oriented structures. Linsker [Linsker, 1986, Linsker, 1988] has studied by simulation the evolution of weight vectors under a Hebb-type teacherless learning rule in a feed-forward linear network. The equation for the evolution of the weight vector w of a single neuron, derived by ensemble averaging the Hebbian rule over the statistics of the input patterns, is: ! a at Wi = k! + L(Qij + k 2 )wj subject to -Wmax ~ Wi < Wmax

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NeurIPS Conference 1988 Conference Paper

Models of Ocular Dominance Column Formation: Analytical and Computational Results

Kenneth Miller
Joseph Keller
Michael Stryker

We have previously developed a simple mathemati(cid: 173) cal model for formation of ocular dominance columns in mammalian visual cortex. The model provides a com(cid: 173) mon framework in which a variety of activity-dependent biological machanisms can be studied. Analytic and com(cid: 173) putational results together now reveal the following: if inputs specific to each eye are locally correlated in their firing, and are not anticorrelated within an arbor radius, monocular cells will robustly form and be organized by intra-cortical interactions into columns. Broader corre(cid: 173) lations withln each eye, or anti-correlations between the eyes, create a more purely monocular cortex; positive cor(cid: 173) relation over an arbor radius yields an almost perfectly monocular cortex. Most features of the model can be un(cid: 173) derstood analytically through decomposition into eigen(cid: 173) functions and linear stability analysis. This allows predic(cid: 173) tion of the widths of the columns and other features from measurable biological parameters.

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