Oxford Centre for Computational Neuroscience

Professor Edmund T. Rolls

Discoveries on the Neuroscience of Vision

Brain Computations

Cerebral Cortex

Memory, Attention, and Decision-Making

Computational Neuroscience of Vision

Face-selective neurons (in the amygdala (38, 91, 97), inferior temporal visual cortex (38A, 73, 91, 96, 162), and orbitofrontal cortex (397)) (see 412, 451501, B11, B12). 


Face expression selective neurons in the cortex in the superior temporal sulcus (114, 126) and orbitofrontal cortex (397). Reduced connectivity in this system in autism (541, 609).


Visual neurons in the inferior temporal visual cortex implement translation, view, and size invariant representations of faces and objects (91, 108, 127, 191, 248, B12).


In natural scenes, the receptive fields of inferior temporal cortex neurons shrink to approximately the size of objects, revealing a mechanism that simplifies object recognition (320, 516, B12).


Top-down attentional control of visual processing by inferior temporal cortex neurons in complex natural scenes (445).


In natural scenes, inferior temporal visual cortex neurons encode information about the locations of objects relative to the fovea, thus encoding information useful in scene representations (395, 455, 516).


The inferior temporal visual cortex encodes information about the identity of objects, but not about their reward value, as shown by reversal and devaluation investigations (32, 320, B11). This provides a foundation for a key principle in primates including humans that the reward value and emotional valence of visual stimuli are represented in the orbitofrontal cortex as shown by one-trial reversal learning and devaluation investigations (79, 212, 216) (and to some extent in the amygdala 38, 383, B11), whereas before that in visual cortical areas, the representations are about objects and stimuli independently of value (B11, B13, B14). This provides for the separation of emotion from perception.

Information encoding using a sparse distributed graded representation with independent information encoded by neurons (at least up to tens) (172, 196, 204, 225, 227, 321, 255, 419, 474, 508, 553561, B12). (These discoveries argue against ‘grandmother cells’.) The representation is decodable by neuronally plausible dot product decoding, and is thus suitable for associative computations performed in the brain (231, B12). Quantitatively relatively little information is encoded and transmitted by stimulus-dependent cross-correlations between neurons (265, 329, 348, 351, 369, 517). Much of the information is available from the firing rates very rapidly, in 20-50 ms (193, 197, 257, 407). All these discoveries are important in our understanding of computation and information transmission in the brain (B12).


A theory and model of invariant visual object recognition in the ventral visual system closely related to empirical discoveries (162, 179, 192, 226, 245, 275, 277, 280, 283, 290, 304, 312, 396, 406, 414, 446, 455, 473, 485, 516, 535, 536, 554, B12, 589, B15).

A theory and model of coordinate transforms in the dorsal visual system using a combination of gain modulation and slow or trace rule competitive learning. The theory starts with retinal position inputs gain modulated by eye position to produce a head centred representation, followed by gain modulation by head direction, followed by gain modulation by place, to produce an allocentric representation in spatial view coordinates useful for the idiothetic update of hippocampal spatial view cells (612). These coordinate transforms are used for self-motion update in the theory of navigation using hippocampal spatial view cells (633).

Binaural sound recording to allow 3-dimensional sound localization (11A, UK provisional patent, Binaural sound recording).