Benjamin Cowley

Benjamin Cowley

Assistant Professor

Ph.D., Carnegie Mellon University, 2018

cowley@cshl.edu |

Cowley Lab Website   Faculty Profile

How do we identify and describe the step-by-step computations of the brain? The Cowley group identifies data-driven models of neural responses and behavior by coupling data collection with model training during closed-loop experiments. We condense these models into compact, interpretable forms—allowing us to describe the complicated computations of the brain in a clear and concise way.

The brain is as fascinating as it is complex: When an image lands on the retina, our brain picks out relevant objects and salient details by using its massive network of interconnected neurons. What are the computations lurking within this network?

To understand the brain’s computations, one often seeks a model to capture the step-by-step computations of neurons. For example, a model can take an image as input and output a visual neuron’s response. The most predictive models in computational neuroscience typically have millions of parameters and need large amounts of training data—making it difficult to obtain and interpret such models.

The Cowley research group takes a two-pronged approach to address these problems. First, we design adaptive stimulus selection techniques to efficiently train models (e.g., deep neural networks) with as little recording time as possible. We work hand-in-hand with experimentalists to deploy these systems in closed-loop experiments. Second, we develop machine learning techniques to identify highly-predictive models that have as few parameters as possible. We then analyze these “compact” models to determine the computations necessary and sufficient to explain a neuron’s response. Our approach of identifying highly-predictive, interpretable models will shed light on computations otherwise hidden by the scale and complexities of the brain.

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All Publications

One-to-one mapping between deep network units and real neurons uncovers a visual population code for social behavior

20 Jul 2022 | Cold Spring Harbor Laboratory
Cowley, Benjamin, Calhoun, Adam, Rangarajan, Nivedita, Turner, Maxwell, Pillow, Jonathan, Murthy, Mala

Bridging neuronal correlations and dimensionality reduction.

1 Sep 2021 | Neuron | 109(17):2740-2754.e12
Umakantha, Akash, Morina, Rudina, Cowley, Benjamin, Snyder, Adam, Smith, Matthew, Yu, Byron

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex.

11 Nov 2020 | Neuron | 108(3):551-567.e8
Cowley, Benjamin, Snyder, Adam, Acar, Katerina, Williamson, Ryan, Yu, Byron, Smith, Matthew

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

Dec 2016 | PLoS Computational Biology | 12(12):e1005141
Williamson, Ryan, Cowley, Benjamin, Litwin-Kumar, Ashok, Doiron, Brent, Kohn, Adam, Smith, Matthew, Yu, Byron

Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex.

Dec 2016 | PLoS Computational Biology | 12(12):e1005185
Cowley, Benjamin, Smith, Matthew, Kohn, Adam, Yu, Byron

DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

Dec 2013 | Journal of Neural Engineering | 10(6)
Cowley, B, Kaufman, M, Butler, Z, Churchland, M, Ryu, S, Shenoy, K, Yu, B

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