Ramanujan Srinath

Post-doctoral Scholar
Vision and Cognitive Neuroscientist

About Me

I study how we see and interact with objects in the world. Every time we look at an object in front of us — a cup, a pen, a lamp, our mobile phones — our eyes are bombarded with the light reflected from that object. This is the information that our eyes forward to the brain for processing. When we move our eyes to look at other objects, the information changes. Our brain is tasked with making sense of the ever-changing visual information about those objects. Our brain processes — assembles, sorts, prunes, and combines — information to make inferences about them. Those inferences could be about the objects' identities (cup, pen, table), their shapes (hollow, cylindrical, planar, curved), their materials (glass, plastic, wood), their affordances (stiff, smooth, malleable), or their places in the environment (balanced, unstable, squished, bent, about to collide). Or those inferences could be about how we can interact with the objects — how we would reach out and grasp them, what would happen if we drop them, what size box they would it fit in, how we would assemble them to create new objects, etc.

I am interested in the neural processes that do all of the above. I use a combination of computational modelling, psychophysics, electrophysiology, and optical/two-photon imaging techniques to understand the coding transformations along the visual hierarchy that lead to inferences.

In my PhD, I studied how single neurons in an area in the brain called V4 processes objects. I am currently extending this work to find out how populations of neurons represent multiple visual parameters and how we use this information to guide our decisions. This requires the recording and analysis of multiple neurons simultaneously. Doing that gives me a precise idea of the information available while we make decisions about objects at any given moment. In fact, recording neural activity from many areas in the brain gives me clues about (a) what sensory information is available, (b) how it is processed in multiple stages, and (c) how it leads to perceptual decisions.

I am a postdoctoral fellow in Dr. Marlene Cohen's lab in the Department of Neurobiology and the Neuroscience Institute at the University of Chicago. I did my graduate work in Drs. Kristina Nielsen and Charles Connor's labs at the Mind/Brain Institute and the Solomon Snyder Department of Neuroscience at Johns Hopkins University. I am a Bachelor of Engineering in Electronics and Communication Engineering from Manipal Institute of Technology, India.

Post-doc Lab: Cohen Lab.
Ph.D. Labs: Connor and Nielsen Labs at MBI

Curriculum Vitae


A selection of interesting projects I have been involved in over the years.
(I am currently uploading projects gradually; more code and ideas coming soon.)


Closed-loop B-Spline Shapes

Creating 2D shapes with morphable contour elements

Random Shapes vs Real Shapes

How real are these randomly generated shapes

Photorealistic Shape Stimuli

Using Blender to create shape stimuli in naturalistic environments

Stereograms and Kinematograms

Rendering solid and flat shape stimuli using stereo and motion cues

Interactive Shape Morphs

Creating shape you can interact with for human psychophysics

Hopkins Neuroscience T-Shirt


Ferret Vectors


Scientist Trading Cards



cnbc tshirt

CNBC T-Shirt

uppda logo



Solid Shape Representation in V4

Adding a Dimension to V4 Shape Processing

Amplitude Correlations in Coherence

How Amplitude Correlations Affect LFP Coherence

Spatial Reach of the ECoG Electrode

Using NEURON and pooling simulations to find the spatial reach of ECoG

AlexNet Responses to Image Phase Scrambling

Are AlexNet units responding to 3D shape parts or texture patterns

Attention improves information flow

Using linear regression and dimensionality reduction techniques to assess how attention affects the communication subspace between MT and SC

Image Distance Viewer


Full list at Google Scholar.

Attention Improves Information Flow

Attention improves information flow between neuronal populations without changing the communication subspace
Srinath et al, Current Biology, 2020

Solid Shape Representation in V4

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision
Srinath et al, Current Biology, 2020

Amplitude Correlations in LFP Coherence

Effect of amplitude correlations on coherence in the local field potential
Srinath and Ray, JNeuroPhys, 2014

Contact Me

ramsrinath [at] uchicago [dot] edu or mastodon