Information theory

Methods in Computational Neuroscience (MCN) is a four-week Marine Biological Laboratory course that introduces the computational and mathematical techniques used to address how the brain solves problems at levels of neural organisation ranging from single membrane channels to operations of the entire brain. In each of the first three weeks the course moves up a level of complexity, from molecular and cellular to network to cognitive and behavioural, asking whether biologically plausible mechanisms can explain how nervous systems solve specific computational problems, how such problems can be d

Research in Taro Toyoizumi’s laboratory connects synaptic plasticity, information processing and adaptive neural circuits. This role leads computational work on sleep within a multiscale CREST BioDX collaboration with Kaoru Inokuchi and Tatsuya Haga. Analytical approaches draw on statistical physics and information theory, alongside mathematical models of learning and memory. The postdoctoral category is for researchers within five years of their doctorate; the research-scientist category is for more experienced doctorate holders. The appointment is at RIKEN’s Wako campus, with annual renewal

Gašper Tkačik explores whether the language of information in biology can become a predictive scientific theory. The lecture connects information transfer from DNA to proteins, positional signals that guide cell fate during development, neural information processing, and the storage and inheritance of information in evolving genomes. It brings physics, information theory and quantitative biology together to examine these processes across biological scales. Tkačik is Professor at the Institute of Science and Technology Austria. Shinya Kuroda provides commentary; Arisa Ema moderates. Online via

…Information theory and machine learning can incorporate improved calculations into simulations, quantify uncertainty, choose informative observables and advance hadronization theory. Geometry of effective-theory field space combines infinite operator families into finite physical quantities,

Information theory provides a powerful theoretical framework for a quantitative understanding of the structure and function of neural circuits. Despite this, the application of these ideas to neuroscience and machine learning has been hampered by the fact that the computation and maximization of mutual information is only analytically tractable for simple coding problems, under the assumption of small network sizes and low dimensional stimuli. Using tools from the theory of spin-glasses we present a novel approach to estimating the entropy of large neural populations in response to arbitrary s

…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

…cortex supports the processing of information about external stimuli and its use in forming accurate perceptual decisions. In particular, it is unclear how activity distributed across cortical areas and frequency bands encodes information synergistically or redundantly, and how these emergent properties of interactions across areas relate to perception. Here, we aim to measure synergy and redundancy during an audiovisual task at the mesoscale level in auditory, visual

The large diversity of behaviors even among closely related species indicates the evolvability of the underlying neural circuit. At the same time, the behaviors must be functionally robust, but how systems can be both robust and evolvable is still an open question in neuroscience and evolution. Studying robustness and evolvability requires the mapping between genotypes and phenotypes, which is challenging to obtain experimentally. However, models of neural circuits that generate behavior can be used as a proxy of the biological system, and the mapping between model parameters and model output

Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inp

…Employing information theory, we present a simple computational account of how such abstract spatial representations may emerge from interactions with high-dimensional stimuli: We show that learning world models in which the dynamics are parsimonious and apply (approximately) independently of the state in an abstract state sp

To understand how the brain is wired calls for investigating how the brain's wiring information (the connectome) is encoded in the genome. From small insects to humans, the apparent complexity of biological neural networks, in particular the information amount required to describe all connections in the connectome, far exceeds the genomic storage capacity. To explain the discrepancy in information volume between a direct description of the brain network and the coding genome's size, an emerging hypothesis, coined the genomic bottleneck principle, proposes that the wiring information must be co

…perspective in which we integrate information theory, dynamical systems and statistical physics to extract understan

In recent years it has become increasingly clear that (Shannon) information is a central resource for organisms, akin in importance to energy. Any decision that an organism or a subsystem of an organism takes involves the acquisition, selection, and processing of information and ultimately its concentration and enaction. It is the consequences of this balance that will occupy us in this talk. This perception-action loop picture of an agent's life cycle is well established and expounded especially in the context of Fuster's sensorimotor hierarchies. Nevertheless, the information-theoretic persp