Topic: mutual information

In decision-making under risk (DMR) participants' choices are based on probability values systematically different from those that are objectively correct. Similar systematic distortions are found in tasks involving relative frequency judgments (JRF). These distortions limit performance in a wide variety of tasks and an evident question is, why do we systematically fail in our use of probability and relative frequency information? We propose a Bounded Log-Odds Model (BLO) of probability and relative frequency distortion based on three assumptions: (1) log-odds: probability and relative frequen

To understand how the brain can perform complex tasks such as perception, we have to understand how information enters the brain, how it is transformed and how it is transferred. But, how do we measure information transfer in the brain? This presentation will start with a general introduction of what mutual information is and how to measure it in an experimental setup. Next, the talk will focus on how this can be used to develop brain models at different (spatial) levels, from the microscopic single neuron level to the macroscopic network and behavioural level. How can we incorporate the knowl

The natural world is full of noise, but the brain’s capacity for information transmission is severely limited. Therefore, discarding irrelevant information contained in sensory inputs while retaining salient features that are related to the input label, is key to survival. What are the salient features? And what are the underlying feature selection mechanisms? It is thought that the brain may implement information bottlenecks, which aim to optimise the trade-off between compression and preservation of salient information. However, information bottlenecks are notoriously difficult to implemen

Hippocampal granule cell (GC) models exhibit degeneracy with different ion channel parameters resulting in comparable functional behaviour [1,2]. However, it is unknown how the degenerate models are further constrained by energetic considerations and known computational functions of GCs. Here, we use Pareto optimality to investigate this question. The Pareto perspective is particularly useful for multi-objective optimisation with multiple trade-offs [3]. We assume that evolution constrains and simplifies the ion channel parameter space by favouring Pareto optimal neurons, which cannot improve

The role of feedback in sensory systems remains a major puzzle in neuroscience. Classical computational theories such as hierarchical predictive coding[1] or hierarchical Bayesian inference[2] postulate abstract normative goals for the role of feedback connections. However, predictions derived from these theories are typically specified at the level of individual neurons. Whether feedback shapes collective properties of neural populations and how such changes may impact encoding of sensory signals remain unknown. Here we tackle these questions directly by studying the population coding of na

A major goal of sensory neuroscience is to understand the system's response to individual stimuli. Although mutual information (MI) continues to be a useful tool when applying Information theory to neural systems, it does not suffice when trying to understand how informative individual stimuli are. In general such knowledge may be crucial for understanding the functional organization of sensory systems. To investigate this, we focus on ON-OFF Direction Selective Retinal Ganglion Cells (dsRGC). These cells respond robustly to motion in their preferred direction, and sparsely to motion in the op

Different stimuli elicit different transient neural responses in the brain. How is the information represented in the parallel neuronal activity of a neuronal population? We investigate this question in Neuropixel recordings of awake behaving mice by inferring the stimulus class (e.g., visual or tactile) from the activity. To quantify the separability, we use an optimally trained linear readout, which can be understood through the framework of Bayesian inference. We find that separability only depends on three geometric properties of the neuronal population vector, described by cones within th

Brains are energy-hungry and evolution has driven them to work in an energy-efficient manner. In food restriction experiments, Padamsey et al. [1] have recently observed that individual neurons trade off energy consumption and information transmission. Specifically, neurons appear to reduce energy consumption by changing their integration properties through the adaptation of membrane resistance, resting potential, and synaptic efficacies, which comes at the cost of reduced information transmission. Furthermore, they found that individual neurons assume a broad range of values of these paramete

The vast majority of neurons in the vertebrate nervous systems transmits information in a digital manner via action potentials. Yet, a detailed understanding of the advantages of digital communication in the brain is still lacking. In our study, we investigate synaptic transmission in terms of an optimization problem, where the space of solutions comprises the digital and the analog regime. We analyze a binomial synapse model connecting two neurons, where the probability of transmitter release, modeled by a transfer function, depends on the axonal membrane potential. We optimize the transfer f