Research map/Stanford University/Stephen A. Baccus

Professor research guide

Stephen A. Baccus

How retinal circuits transform visual information, adapt and compute; models that connect predictions to mechanisms.

Adaptation Computational neuroscience Electrophysiology Interpretable machine learning Multielectrode arrays Neural circuits Neural coding Neural network models Object motion Predictive coding Retina Ultrasound neurostimulation Visual neuroscience

Independent educational guide. Not affiliated with or endorsed by the universities, professors or laboratories described here. This collection reflects the material currently mapped on Socratic Learn.

Associated labs and groups

Stanford University

Stephen A. Baccus Lab

Stephen A. Baccus

How retinal circuits transform visual information, adapt and compute; models that connect predictions to mechanisms.

Retina Adaptation Computational neuroscience Electrophysiology Interpretable machine learning

6 lessons · ~45 minutes

Explore the lab

Your recommended path

Learn this research

Research primer

Neural Circuits, Vision, and Computation

6 lessons · ~45 minutes

An independent one-hour primer on retinal computation, neural coding, adaptation, experimental neuroscience, interpretable models, and neural perturbation, designed around research themes relevant to the Stephen A. Baccus Lab.

Watch videos

Key concepts

Electrophysiology and neural population recording

Interpret spike trains, raster plots, and firing rates from electrophysiology and imaging.

Function, computation, and mechanism

Distinguish the function, computation, and mechanism of a circuit.

Interpretable models and mechanistic hypotheses

Explain why predictive accuracy does not by itself establish biological mechanism.

Linking neural representation to behavior

Explain how researchers connect stimulus, neural activity, and behavior.

Neural adaptation and sensitization

Explain adaptation and sensitization as responses to changing stimulus statistics.

Neural computation as input-output transformation

Describe a neural computation as a transformation from input to output.

Neural perturbation and ultrasound neurostimulation

Describe focused ultrasound as an experimental tool for neural perturbation and its current limits.

Observation, perturbation, and causal evidence

Explain why perturbation provides stronger mechanistic evidence than recording alone.

Prediction and object-motion computation

Describe how retinal circuits use regularities to anticipate motion and separate object from global motion.

Predictive models of neural responses

Describe a linear-nonlinear model r(t) = f(k * s(t)) and how it is evaluated on held-out data.

Receptive fields and visual feature extraction

Predict ganglion-cell responses from center-surround receptive fields.

Retinal circuitry and ganglion-cell output

Trace visual signals from photoreceptors to ganglion cells and the optic nerve.

Important papers

J Neurosci 28:6807–6817 · 2008

A retinal circuit that computes object motion

Baccus SA, Ölveczky BP, Manu M, Meister M

Why this matters: Connects a retinal motion computation to a candidate inhibitory circuit.

DOI: 10.1523/JNEUROSCI.4206-07.2008

Nature 436:71–77 · 2005

Dynamic predictive coding by the retina

Hosoya T, Baccus SA, Meister M

Why this matters: Shows how retinal encoding changes with stimulus statistics.

DOI: 10.1038/nature03689

Neuron 111:2742–2755 · 2023

Interpreting the retinal neural code for natural scenes: from computations to neurons

Maheswaranathan N, McIntosh LT, Tanaka H, Grant S, Kastner DB, Melander JB, Nayebi A, Brezovec LE, Wang JH, Ganguli S, Baccus SA

Why this matters: Connects predictive retinal models to interpretable computations.

DOI: 10.1016/j.neuron.2023.06.007