Stimulus representation in single neurons and neuronal populations
Role of tuning shapes on minimal decoding times, and input-output functions under in-vivo-like inputs
Time: Tue 2025-05-20 14.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Stockholm
Language: English
Subject area: Electrical Engineering
Doctoral student: Movitz Lenninger , Teknisk informationsvetenskap
Opponent: Professor Peter Latham, University College London, London, UK
Supervisor: Professor Mikael Skoglund, Teknisk informationsvetenskap; Associate professor Pawel Herman, Beräkningsvetenskap och beräkningsteknik (CST); Associate professor Arvind Kumar, Beräkningsvetenskap och beräkningsteknik (CST)
QC 20250423
Abstract
In this thesis, we explore different topics related to information processing in neuronal circuits. Understanding information processing in biological networks requires not only understanding the information-theoretic consequences of neuronal activity but also understanding the network and single-cell dynamics and transformations underlying those responses. Therefore, we take on two different perspectives on information processing in the brain. First, we study the information-theoretic consequences of different shapes of tuning curves. Here, we depart from the traditional method of studying information through asymptotical measures such as Fisher information or mutual information and instead focus on the impact of tuning shapes and decoding times on rare but large estimation errors. We show that studying the role of decoding time reveals new interesting constraints on the "neural code." We argue that these constraints might explain the tuning organization found in early sensory systems. Second, we explore the role of single-cell and network dynamics on the input-output transfer function of spike trains. Understanding how single cells and networks of cells transform external signals is a key component in understanding the constraints and possibilities to encode information in neuronal circuits. In particular, we study the role of NMDA in expanding the post-synaptic range of sensitivity to pre-synaptic activity into states of high conductance using modeling of a morphologically reconstructed cell. We show that the NMDA-AMPA ratio can be an important mechanism to control the excitability of a cell given its natural range of inputs. Lastly, we study the joint input-output transformation of firing rate and synchrony in networks of point neurons. Synchronous inputs have been proposed to increase the pre-synaptic efficacy in electing post-synaptic responses but can also induce strong synchronization in the downstream networks. We show that feedforward inhibition, if tightly correlated with the feedforward excitation, can reduce post-synaptic synchronization at the expense of input synchrony no longer driving increased post-synaptic activity but, if anything, hinders it.