Reimagining the neuron as a controller: A novel model for Neuroscience and AI
We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.
A Better Method to Quantify Perceptual Thresholds : Parameter-free, Model-free, Adaptive procedures
The ‘quantification’ of perception is arguably both one of the most important and most difficult aspects of perception study. This is particularly true in visual perception, in which the evaluation of the perceptual threshold is a pillar of the experimental process. The choice of the correct adaptive psychometric procedure, as well as the selection of the proper parameters, is a difficult but key aspect of the experimental protocol. For instance, Bayesian methods such as QUEST, require the a priori choice of a family of functions (e.g. Gaussian), which is rarely known before the experiment, as well as the specification of multiple parameters. Importantly, the choice of an ill-fitted function or parameters will induce costly mistakes and errors in the experimental process. In this talk we discuss the existing methods and introduce a new adaptive procedure to solve this problem, named, ZOOM (Zooming Optimistic Optimization of Models), based on recent advances in optimization and statistical learning. Compared to existing approaches, ZOOM is completely parameter free and model-free, i.e. can be applied on any arbitrary psychometric problem. Moreover, ZOOM parameters are self-tuned, thus do not need to be manually chosen using heuristics (eg. step size in the Staircase method), preventing further errors. Finally, ZOOM is based on state-of-the-art optimization theory, providing strong mathematical guarantees that are missing from many of its alternatives, while being the most accurate and robust in real life conditions. In our experiments and simulations, ZOOM was found to be significantly better than its alternative, in particular for difficult psychometric functions or when the parameters when not properly chosen. ZOOM is open source, and its implementation is freely available on the web. Given these advantages and its ease of use, we argue that ZOOM can improve the process of many psychophysics experiments.
How do we find what we are looking for? The Guided Search 6.0 model
The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of Guided Search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid serial and parallel model. If a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Part 2 elaborates on the five sources of Guidance that are combined into a spatial “priority map” to guide the deployment of attention (hence “guided search”). These are (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g. priming), (4) reward, and (5) scene syntax and semantics. In GS6, the priority map is a dynamic attentional landscape that evolves over the course of search. In part, this is because the visual field is inhomogeneous. Part 3: That inhomogeneity imposes spatial constraints on search that described by three types of “functional visual field” (FVFs): (1) a resolution FVF, (2) an FVF governing exploratory eye movements, and (3) an FVF governing covert deployments of attention. Finally, in Part 4, we will consider that the internal representation of the search target, the “search template” is really two templates: a guiding template and a target template. Put these pieces together and you have GS6.