The foundation is currently supporting a computer modelling centre within the Oxford University Department of Experimental Psychology

Computer modellers within the university research centre are exploring various aspects of visual processing in the brain, including motion detection, face recognition in natural scenes, and the recognition of objects from novel views. Over successive stages, the primate visual system develops neurons that respond with view, size and position invariance to objects or faces. Our models explain how such neurons may develop their firing properties, and hence allow the visual system to recognise objects in natural environments.

This research has direct bearing on understanding disorders of visual perception such as amblyopia, in which one eye suffers reduced vision due to interference during early visual development. Amblyopia is the leading cause of vision loss in persons under 40 years of age. Other disabilities include prosopagnosia where subjects have difficulty recognising faces, or spatial neglect where patients ignore part of their field of vision.

Recently, our computer simulations have revealed a powerful new algorithm, Continuous Transformation Learning, that may account for how the brain learns to recognise objects and faces from different viewpoints. This discovery represents a major breakthrough in understanding the operation of the visual system, and should help to guide the treatment of visual disorders arising from developmental problems.

In addition to potential medical benefits, possible engineering applications of this research range from visual control and quality inspection in manufacturing to automated CCTV monitoring.The new Continuous Transformation Learning algorithm may help robots to operate more flexibly in real-world environments by enabling them to recognise objects from different viewpoints.


The ability of the visual brain to analyse and recognize objects under natural viewing conditions is unmatched by today’s computer vision systems. In order to achieve this singular ability, the primate brain develops and utilises a rich tapestry of cells that encode different kinds of visual information. Over successive stages of processing, the primate ventral visual pathway develops neurons that respond selectively to objects of increasingly complex visual form (Kobatake & Tanaka, 1994), going from simple orientated line segments in area V1 (Hubel & Wiesel, 1962) to whole objects or faces in the anterior inferotemporal cortex (TE) (Perrett et al., 1982; Tsao et al., 2003). In addition, in higher layers of the ventral pathway, the responses of neurons to objects and faces show invariance to retinal location, size, and orientation (Tanaka et al., 1991; Rolls et al., 1992; Rolls, 2000; Perrett & Oram, 1993; Rolls & Deco, 2002). These later stages of processing carry out object recognition by integrating information from more elementary visual features represented in earlier layers.

The ventral visual pathway is thus thought to be responsible for transform-invariant visual object and face recognition in the brain. However, it remains a difficult challenge to understand exactly how these neurons develop their response properties during learning. The learning processes will depend on how the neurons interact with each other through successive layers of the ventral visual pathway as they are driven by rich visual input from natural scenes. We aim to investigate this through computer simulations that accurately model the behaviour of individual neurons, how these neurons are linked together in the brain, how the synaptic connections between cells are modified during learning, and the statistical properties of the visual input from the sensory environment.

Over the past twenty years, the Oxford laboratory has investigated a range of problems in this field using a computer model, VisNet of the primate ventral visual pathway. Similar to the actual visual system, this model is composed of a series of competitive networks to represent each stage of the system from the primary visual cortex (V1) to the anterior inferior temporal cortex (TE). There are convergent feedforward connections to each neuron from a topologically corresponding region of the preceding layer, with the synaptic weights adjusted during learning according to associative learning rules (Wallis & Rolls, 1997). This biologically plausible model has solved problems like invariant object recognition (Stringer, Perry, Rolls & Proske, 2006), segmentation of simultaneously presented objects (Stringer & Rolls, 2008), recognition with partially occluded objects (Tromans, Higgins, & Stringer, 2012).

We are currently investigating a number of further issues, such as a potential role of backprojections, Self-Organising Map, spiking neural networks, subliminal learning, and etc., which are all related to visually guided learning and visual processing through successive stages of the ventral visual pathway.

Case study

Experimental studies have shown that neurons at an intermediate stage of the primate ventral visual pathway encode the conformation and spatial relations of facial features [1], while neurons in the later stages are selective to the full face [2]. In this study, we investigate how these cell firing properties may develop through visually-guided learning.
A hierarchical neural network model of the primate’s ventral visual pathway is trained by presenting many randomly generated faces to the hierarchical competitive neural network while a local learning rule modifies the strengths of the synaptic connections between successive layers [3] (Figure 1A).

After training, the model is found to have developed the experimentally observed cell firing properties. In particular, we have demonstrated how the primate brain learns to represent facial expression independently of facial identity as reported in [4] (Figure 1B). We have also shown how the visual system forms separate representations of facial features such as the eyes, nose and mouth (Figure 1C) as well as representations of spatial relationships between these facial features, as have been reported in single unit recording studies [1].
Therefore, this research makes an important contribution to understanding visual processing in the primate brain.


Part of our research in vision focuses on understanding the role of the dorsal stream in the sensorimotor transformations required for visually guided actions.

Visual targets are initially encoded in a retinal reference frame. However, this information is transformed in later stages of processing into different supra-retinal coordinate frames that are more suitable to guide our behavior. A number of neurophysiological studies in the posterior parietal cortex and premotor areas of the primate brain have reported a continuum of complex reference frames, ranging from eye-centred, head-centred, hand-centred, body-centred as well as intermediate and gain modulated representations.

The modellers within our research centre have been producing self-organizing neural network models that provide a theoretical framework to explain the development of cells that encode the location of visual targets in different reference frames.

Eye and Head centred Hand-centred

Case study Recognising objects from novel views

One of the major challenges in computer vision is how to recognise objects from different viewpoints which have not been encountered during training. Our neural network model of the ventral visual system is able to accomplish this by first learning how elemental features in the environment transform across different viewpoints during early visual development (Stringer, S.M. and Rolls, E.T. (2002). Neural Computation, 14: 2585-2596).

Multiple diagrams

Architecture of a 4-layer hierarchical neural network model of the ventral visual processing stream. Convergence through the network is designed to provide fourth-layer neurons with information across the entire input retina. (Right) Convergence through successive layers of the visual system.

Six visual stimuli with three surface features that occur in three relative positions.

Six visual stimuli with three surface features that occur in three relative positions. Each row shows one of the stimuli rotated through the five different rotational views, in which the stimulus is presented to the network. From left to right, the rotational views shown are -60 degrees, -30 degrees, 0 degree (central position), 30 degrees, and 60 degrees. To simulate early visual development, layers 1 and 2 are trained on pairs of surface features across all five views.Then layers 3 and 4 are trained on the complete stimuli at only four out of the five views.

Results from the neural network simulation after training.

Results from the neural network simulation after training. The figure shows the response profiles of a top layer neuron to the 6 stimuli across all 5 views. It can be seen that this cell has learned to respond invariantly to one of the stimuli across all views.The network has learned to discriminate between the 6 objects from all views, including the novel view not encountered during training.

Vision Publications Date Authors Journal Volume/Pages Download
The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes2016Galeazzi, J.M., Navajas, J., Mender, B.M.W., Quiroga, R.Q., Minini, L. and Stringer. S.M.Network: Computation in Neural Systems27(1):29-51 Download
A Computational Exploration of Complementary Learning Mechanisms in the Primate Ventral Visual Pathway2016Spoerer, C.J., Eguchi, A. and Stringer, S.M.Vision Research119: 16-28 Download
STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli2015Evans, B.D. and StringerBiological Cybernetics109: 215-239 Download
A self-organizing model of perisaccadic visual receptive field dynamics in primate visual and oculomotor system2015Mender, B.M.W. and StringerFrontiers in Computational NeuroscienceVol 9, Article 17 Download
Self-organizion of head-centered visual responses under ecological training conditions2014Mender, B.M.W. and StringerNetwork: Computation in Neural Systems25: 116-136 Download
Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity2014Eguchi, A., Neymotin, S.A. and StringerFrontiers in Neural Circuits8:16 Download
A model of self-organizing head-centered visual responses in primate parietal areas2013Mender, B.M.W. and Stringer, S.M.PLoS ONE8(12): e81406 Download
How lateral connections and spiking dynamics may separate multiple objects moving together2013Evans, B.D. and Stringer, S.M. PLoS ONE8(8): e69952 Download
A self-organizing model of the visual development of hand-centred representations2013Galeazzi, J.M., Mender, B.M.W., Paredes, M., Tromans, J.M., Evans, B.D., Minini, L. and Stringer, S.MPLoS ONE8(6): e66272 Download
Transformation-invariant visual representations in self-organizing spiking neural networks2013Evans, B.D. and Stringer, S.M. Frontiers in Computational NeuroscienceVol 6, Article 46 Download
Learning view invariant recognition with partially occluded objects2012Tromans, J.M., Higgins, I. and Stringer, S.M.Frontiers in Computational NeuroscienceVol 6, Article 48, pages 1-19 Download
Learning separate visual representations of independently rotating objects,2012Tromans, J.M., Page, H.J.I. and Stringer, S.M.Network: Computation in Neural Systems23(1-2):1-23 Download
A computational model of the development of separate representations of facial identity and expression in the primate visual system2011Tromans, J.M., Harris, M. and Stringer, S.M.PLoS ONE6(10):e25616 Download
The role of independent motion in object segmentation in the ventral visual stream: Learning to recognise the separate parts of the body2011Higgins, I.V. and Stringer, S.M.Vision Research51:553-562 Download
Continuous transformation learning of translation invariant representations2010Perry, G., Rolls, E.T. and Stringer,Experimental Brain Research204:255-270 Download
Invariant visual object recognition: A model, with lighting invariance2006Rolls, E.T. and Stringer, S.M.Journal of Physiology100: 43-62 Download
Effective size of receptive fields of inferior temporal cortex neurons in natural scenes2002Trappenberg, T.P., Rolls, E.T. and Stringer, S.M. Advances in Neural Information Processing Systems14: 293-300 Download
Invariant object recognition in the visual system with novel views of 3D objects2002Stringer, S.M. and Rolls, E.T.Neural Computation14: 2585-2596 Download
Invariant object recognition in the visual system with error correction and temporal difference learning2001Rolls, E.T. and Stringer, S. M.Network: Computation in Neural Systems12: 111-129 Download
Position invariant recognition in the visual system with cluttered environments2000Stringer, S. M. and Rolls, E.T.Neural Networks13: 305-315 Download