# CRCNS: Linking Synaptic Populations and Computation Using Statistical Mechanics

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2024 · $237,297

## Abstract

Computations performed by single neurons result from integration of a large myriad of synaptic inputs 
distributed throughout complex dendritic topology. Synaptic inputs vary in substantial ways; sensory-driven 
activity patterns, probability of activation, synapse location within the dendritic topology, local dendritic 
organization, and ultrastructural characteristics are all thought to be critical for determining how synaptic 
inputs influence the spiking output of a neuron. Populations of synaptic inputs, ultimately, determine the 
coding capacity and computations single neurons can perform. Despite this fact, studies largely overlook the 
synaptic input population within a single neuron, instead focusing on the activity of cellular populations, using 
biophysical models or constructing models that create hypothetical weights or synapses (e.g. deep-neural 
networks). Thus, a critical question in neuroscience remains how ensemble synaptic activity is integrated in 
vivo and what are the fundamental principles of synaptic organization which describe neural computation. 
To address this issue, we present a tightly integrated experiment-theory approach. We propose to (1) 
measure the sensory-driven activity patterns of large populations of dendritic spines on layer 2/3 visual 
cortical neurons in ferret visual cortex in vivo, and (2) use a statistical physics approach to characterize the 
structure and computing of synaptic populations in multiple contexts. Thus, this project will provide
fundamental knowledge about the synaptic architecture of neurons in the brain. The

## Key facts

- **NIH application ID:** 10893013
- **Project number:** 5R01NS135763-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Krishnan Padmanabhan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $237,297
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893013

## Citation

> US National Institutes of Health, RePORTER application 10893013, CRCNS: Linking Synaptic Populations and Computation Using Statistical Mechanics (5R01NS135763-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10893013. Licensed CC0.

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