Representing Where along with What Information in a Model of a Cortical Patch

Citation: Roudi Y, Treves A ( Representing Where along with What Information in a Model of a Cortical Patch Yasser Roudi 0 Alessandro Treves 0 Karl J. Friston, University College London, United Kingdom 0 1 Gatsby Computational Neuroscience Unit, UCL, United Kingdom , 2 Cognitive Neuroscience Sector, SISSA , Italy , 3 Centre for the Biology of Memory, NTNU , Norway Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects. - Visual object perception, which is often effortless despite partial occlusion or changes in view, shading, size, etc., has been associated to attractor dynamics in local cortical circuits [15]. A single pattern of neuronal activity would be associated with an object, and retrieved when an input cue engages the corresponding basin of attraction. This would lead to a distribution of activity over a cortical patch that can be read out by other areas and can persist even after the object is removed. Attractor dynamics can be realised in neuronal networks by Hebbian modifications of synaptic weights on the recurrent connections of a local population of cortical neurons [6]. The experimental observation of persistent activity in monkey prefrontal cortex (PFC) [79] and inferior temporal cortex (IT) [1012] during memory related tasks supports the idea that attractor dynamics is involved in such tasks. The above-mentioned paradigm is conceptually very successful in explaining how information about the identity of an object can be retrieved from noisy input and maintained in working memory, even when the input is transient. However, in day to day life, the identity of an object is hardly the only type of information that one needs to retrieve and maintain about it. If you look at a scene for a short time and then turn your head away, you will still remember details about what objects were present in the scene and where they were located. You can even do this if many of the objects in the scene were occluded. These abilities allow us to maintain a coherent representation of our surrounding environment and are crucial for most real world visually guided behaviours. Visually guided behaviour often requires extracting information about identity of objects (what information) from noisy sensory input, and combining this what information with information about the position of objects (where information). It also requires maintaining this combined representation of position and identity of objects in working memory after the visual input is removed. The underlying neural mechanisms for these abilities are, however, unknown. In this paper, we analyse a network model of how this may be accomplished in the brain. A great deal of experimental work has been focused on understanding this issue [1318]. Single cell recordings from PFC during the delay period of a delay match to sample task show that neurons in this area can maintain information about the conjunction of position and identity [13,14]. Rao and colleagues [13] also found that some PFC neurons can change their selectivity from conveying what information to conveying where information when the type of information that is required by the task is changed. Selectivity for object-position pairs is further supported by the presence of retinotopically organised maps in PFC regions that are involved in identity working memory tasks [16]. Furthermore, a recent neuroimaging study by Sala and Courtney [17] shows that dorsal and ventral PFC can maintain an integrated representation of position and identity when it is relevant to the task, but represent position or identity when only one of them is Forming a coherent picture of our surrounding environment requires combining visual information about the position of objects (where information) with information about their identity (what information). It also requires the ability to maintain this combined information for short periods of time after the stimulus is removed. Here, we propose a theoretical model of how this is accomplished in the brain, particularly when sensory input is incomplete, and missing what information should be supplied from what is stored in memory. The main idea is that local connectivity in cortical networks can allow the formation of localised states of activity. Where information can then be represented by the position of such bumps, and what information by the fine structure of the neuronal activity within them. We show that there is a difficulty with implementing this idea: noise and heterogeneity in connectivity cause bumps to drift, thereby losing where information. This problem can be solved by incorporating a localised increase in neuronal gain; this, however, interferes with retrieving what information and maintaining it in working memory. We quantify this interference via theoretical analysis of the model and show that, despite the interference, the proposed mechanism is an efficient one in retrieving what information while representing where information. task relevant. Although most studies that address the issue of combining what and where information have focused on PFC, similar observations have been reported in IT. While some studies report a considerable position invariance in the response of IT neurons [1921], this view has been challenged by others. More recent studies show that IT neurons can have small receptive fields and can convey detailed information about the position as well as the iden (...truncated)