Theory of cortical function

Abstract

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.computational neuroscience | neural net | inference | prediction | vision P erception is an unconscious inference (1). Sensory stimuli are inherently ambiguous so there are multiple (often infinite) possible interpretations of a sensory stimulus (Fig. 1). People usually report a single interpretation, based on priors and expectations that have been learned through development and/or instantiated through evolution. For example, the image in Fig. 1A is unrecognizable if you have never seen it before. However, it is readily identifiable once you have been told that it is an image of a Dalmatian sniffing the ground near the base of a tree. Perception has been hypothesized, consequently, to be akin to Bayesian inference, which combines sensory input (the likelihood of a perceptual interpretation given the noisy and uncertain sensory input) with a prior or expectation (2-5).Our brains explore alternative possible interpretations of a sensory stimulus, in an attempt to find an interpretation that best explains the sensory stimulus. This process of exploration happens unconsciously but can be revealed by multistable sensory stimuli (e.g., Fig. 1B), for which one's percept changes over time. Other examples of bistable or multistable perceptual phenomena include binocular rivalry, motion-induced blindness, the Necker cube, and Rubin's face/vase figure (6). Models of perceptual multistability posit that vari...

Bounded Rationality in C. elegans

“…This might suggest that IIA violations are a result of neural constraints carved by evolution in order to maximize information under limited time and resources. This hypothesis is in line with previous studies showing that IIA violations can be explained by a divisive normalization framework (K. Louie, Grattan, & Glimcher, 2011;Webb et al, 2014), which is a general and robust rule of cortical computation (Heeger, 2016).…”

Attention model of binocular rivalry

“…Sensory stimuli are inherently ambiguous so there are multiple (often infinite) possible interpretations of a sensory stimulus. Multistable phenomena (e.g., binocular rivalry) can be used to probe the intrinsic neural dynamics of cortical processing and the neural processes underlying perceptual inference (88)(89)(90), and neural networks with mutual inhibition as the main ingredient have been designed to perform perceptual inference (89). The present model adds attentional modulation as a critical component, not only to explain a large body of literature on the phenomenon of binocular rivalry but also toward developing a neural-based computational theory of perceptual inference.…”

Laminar fMRI: Applications for cognitive neuroscience

“…However, inter-area and inter-laminar communication is a general principle of human brain function (Friston, 2005;Heeger, 2017), and as such all domains of cognitive neuroscience may benefit from lamina-resolved fMRI. Animal studies have measured laminar responses from other areas such as V4 (Nandy et al, 2017) and temporal regions (Koyano et al, 2016), but at present further research is required to extend the application of laminar fMRI beyond primary cortices.…”

“…Influential neurocomputational models of cortical function posit that feedforward filtering operations are complemented by feedback processes that carry a generative model (or prediction) of expected input (Friston, 2005;Heeger, 2017;Lee & Mumford, 2003). Two recent studies examined the laminar profile of activity patterns in V1 under conditions of expected but absent bottom-up input.…”

“…Neural activity in each brain area depends on a combination of bottom up drive and feedback modulatory processes (Bastos et al, 2012;Heeger, 2017;Muckli, 2010;Roelfsema & De Lange, 2016). Higher-level cognitive processes modulate responses in lower-level, sensory brain regions, facilitating the analysis of incoming sensory data.…”