Vector-based Integration of Local and Long-range Information in Visual Cortex

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we propose a new analytic framework for understanding integration within cortical neuronal receptive elds. Based on the synaptic organization of cortex [1, 2, 3, 4, 5, 6], we argue that neuronal integration is better understood in terms of local cortical circuitry than at the level of single neurons, and we present a method for constructing self-contained modules which capture (nonlinear) local circuit interactions. In this framework, receptive eld elements naturally have dual (rather than the traditional unitary [7, 8, 9, 10, 11]) in uence since they drive both excitatory and inhibitory cortical neurons. This vector-based analysis, in contrast to scalar approaches, greatly simpli es integration by permitting linear summation of inputs from both \classical" and \extraclassical" receptive eld regions. We illustrate this by explaining two complex visual cortical phenomena, which are incompatible with scalar notions of neuronal integration. Copyright c Massachusetts Institute of Technology, 1995. This report describes research done partly at the Arti cial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for this research is provided in part by the Advanced Research Projects Agency of the Department of Defense under O ce of Naval Research contract N00014-92-J-4097 and by the National Science Foundation under grant number MIP-9001651. Central to an understanding of cortical function is the question of how cortical neurons integrate inputs to produce outputs. In sensory cortex, the unit of integration is a neuron's receptive eld (RF)[7, 9]. Experiments in primary visual cortex (V1) demonstrate that the spatial extent of integration includes not only \classical" regions, where visual stimuli elicit responses (presumably through thalamocortical axons), but also \extraclassical" regions, where stimuli largely modulate responses evoked by other stimuli (presumably via long-range intracortical or inter-areal axons)[5, 12, 13, 14, 15]. The traditional view of integration holds that each portion of a neuron's receptive eld in response to a given stimulus element has either an excitatory or an inhibitory (i.e., a scalar) in uence[7, 8, 9, 10, 11] (see g 1a). Although this approach has substantial explanatory power, it cannot account for phenomena in which the net e ect of a stimulus element in a given portion of the receptive eld appears to switch between excitatory and inhibitory as global stimulus conditions change [16, 17, 18, 19, 20]. Two such phenomena, involving local and long-range integration respectively, are paradigmatic. First, increasing the luminance contrast of an oriented visual stimulus causes responses in primary visual cortex to initially increase, but subsequently saturate and even decrease (\supersaturate")[16, 22, 23, 24] (see g 2a). Second, adding a distal stimulus facilitates responses to a weak central stimulus, but suppresses responses to a strong stimulus[17, 18, 19, 20] (see g 2b). Our goal is to develop an expanded notion of the visual cortical receptive eld which can explain stimulus-dependent responses such as these. Three basic features of cortical anatomy, which are overlooked by the traditional receptive eld view, are central to the expanded view (see g 1b): i) receptive eld regions (via either thalamocortical or long-range intracortical axons) drive both excitatory and inhibitory cortical neurons [1, 2, 6]; ii) these neurons form dense, recurrent local connections [1, 3, 4, 5]; and iii) di erent portions of the receptive eld provide converging inputs to a shared population of cortical neurons[5, 13, 14, 15, 21]. Based on this anatomy, we propose that: i) each RF region in response to a given stimulus has both excitatory and inhibitory in uences on neuronal responses which in general cannot be reduced to a scalar quantity but rather should be considered separately (i.e., RF input is a vector); ii) receptive eld inputs are integrated by the local cortical circuitry; and iii) the net e ect of a receptive eld input depends both on the excitatory-inhibitory bias of the a erent inputs and on how other receptive eld regions activate the local cortical circuitry. First we will demonstrate this approach by capturing the paradoxical local and long-range phenomena within a large-scale visual cortical model, and later we will present an analytic explanation. In contrast, prior computational investigations of local circuit in uences either have captured anatomical details only in simulations with little formal analysis [25, 26, 27] or have oversimpli ed local cortical excitatory and inhibitory interactions in order to obtain closedform (scalar) analysis[28, 29, 30]. Recently, we and others [25, 26, 31] have demonstrated that consideration of short-range recurrent cortical excitatory (and inhibitory) connections yields a concise account of a broad range of experimental data on orientation and direction selectivity. Here, we extend our model to incorporate longrange intracortical excitation (see g 3 and [25] for model description). Physiological responses to oriented grating stimuli of di ering contrasts within the classical RF are captured by the model (see g 4a). The responses shown here and below are for the excitatory subpopulation. Responses saturate at contrast levels below which thalamic responses saturate [22], can decline for high contrasts (supersaturation) [16, 22, 23, 24], and have ring rates well below maximal cellular ring rates [32]. Inhibitory neurons, on average, saturate at higher contrasts than do excitatory neurons (not shown). While preserving classical RF properties, our model also captures paradoxical 1