how does the brain solve visual object recognition?

how does the brain solve visual object recognition?

Sparseness of the Neuronal Representation of Stimuli in the Primate Temporal Visual Cortex. Figure 1. While response magnitude is not preserved, the rank-order object identity preference is maintained along the entire tested range of tested positions. Supporting: 71, Contrasting: 4, Mentioning: 1142 - Mounting evidence suggests that "core object recognition," the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. First, spike counts in ~50 ms IT decoding windows convey information about visual object identity. JJ DiCarlo, D Zoccolan, NC Rust. CVI: Visual curiosity and incidental learning Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Effects of attention on the reliability of individual neurons in monkey visual cortex [In Process Citation]. Li N, Cox DD, Zoccolan D, DiCarlo JJ. Hinton GE, Dayan P, Frey BJ, Neal RM. We argue that an iterative, canonical population processing motif provides a useful intermediate level of abstraction. While behavioral state effects, task effects, and plasticity have all been found in IT, such effects are typically (but not always) small relative to responses changes driven by changes in visual images (Koida and Komatsu, 2007; Op de Beeck and Baker, 2010; Suzuki et al., 2006; Vogels et al., 1995). 4A). For example, by uncovering the neuronal circuitry underlying object recognition, we might ultimately repair that circuitry in brain disorders that impact our perceptual systems (e.g. Complete retinotopic maps have been revealed for most of the visual field (at least 40 degrees eccentricity from the fovea) for areas V1, V2 and V4 (Felleman and Van Essen, 1991) and thus each area can be thought of as conveying a population-based re-representation of each visually presented image. Foundations and Trends in Computer Graphics and Vision. Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision. PubMed. To understand how the brain computes this solution, we must consider the problem at different levels of abstraction and the links between those levels. The first step is to clearly define the question itself. For example, while the algorithms of (Fukushima, 1980; Riesenhuber and Poggio, 1999b; Serre et al., 2007a) represent a great start, we also know that they are insufficient in that they perform only slightly better than baseline V1-like benchmark algorithms (Pinto et al., 2011), they fail to explain human performance for 100 ms or longer image presentations (Pinto et al., 2010), and their patterns of confusion do not match those found in the monkey IT representation (Kayaert et al., 2005; Kiani et al., 2007; Kriegeskorte et al., 2008). Neuron 73, 415-434 (2012). Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. Most models focus on superordinate categories (e.g., animals, tools) which do not capture the richness of conceptual knowledge. Spectral receptive field properties explain shape selectivity in area V4. How inferior temporal cortex became a visual area. 1). Olshausen BA, Field DJ. Horel JA. In this analogy, the mid-line workers are abstracted away from the job description of the early line workers. A neural code for three-dimensional object shape in macaque inferotemporal cortex. Hinton et al., 1995; see above) and could act to refine the transfer function of each local sub-population. (A) The response pattern of a population of visual neurons (e.g., retinal ganglion cells) to each image (three images shown) is a point in a very high dimensional space where each axis is the response level of each neuron. Similarly, recognition tasks that involve extensive visual clutter (e.g. Credit: Salk Institute. Functional connectivity in the retina at the resolution of photoreceptors. Thus just ~100 ms after image photons impinge on the retina, a first wave of image-selective neuronal activity is present throughout much of IT (e.g. That is, single IT neurons do not appear to act as sparsely active, invariant detectors of specific objects, but, rather, as elements of a population that, as a whole, supports object recognition. Understanding the dorsal stream The dorsal stream helps us size up a visual scene in conjunction with other senses, like hearing. Before In practice, such an operational definition requires agreed-upon sets of images, tasks, and measures, and these benchmark decisions cannot be taken lightly (Pinto et al., 2008a; see below). In: Dickinson, editor. This possibility is not only conceptually simplifying to us as scientists, but it is extremely likely that an evolving system would exploit this type of computational unit because the same instruction set (e.g., genetic encoding of that meta job description) could simply be replicated laterally (to tile the sensory field) and stacked vertically (to gain necessary algorithmic complexity, see above). While these deficits are not always severe, and sometimes not found at all (Huxlin et al., 2000), this variability likely depends on the type of object recognition task (and thus the alternative visual strategies available). How position dependent is visual object recognition? Reducing the dimensionality of data with neural networks. Maunsell JH, Treue S. Feature-based attention in visual cortex. Comparison of shape encoding in primate dorsal and ventral visual pathways. Dynamic shape synthesis in posterior inferotemporal cortex. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. 1685: 2012: . Keysers C, Xiao DK, Foldiak P, Perrett DI. (Desimone et al., 1984; DiCarlo and Maunsell, 2000; Hung et al., 2005; Kobatake and Tanaka, 1994a; Logothetis and Sheinberg, 1996; Tanaka, 1996). In the real world, each encounter with an object is almost entirely unique, because of identity-preserving image transformations. Fortunately, the foundations and tools are now available to make it so. object categorization) (Kriegeskorte et al., 2008; Naselaris et al., 2009) many be a better spatial organizing principle than retinotopic maps. In other words, building an encoding model that describes the transformation from an image to a firing rate response is not the problem that (e.g.) sharing sensitive information, make sure youre on a federal Visual adaptation: physiology, mechanisms, and functional benefits. DiCarlo JJ, Cox DD. The ventral visual stream has been parsed into distinct visual areas based on: anatomical connectivity patterns, distinctive anatomical structure, and retinotopic mapping (Felleman and Van Essen, 1991). After a first feedforward sweep of information (150 ms), recurrent processes reconnecting higher to lower areas of this . One operational denition of ''understanding'' object recognition is the ability to construct an articial system that performs as well as our own visual system (similar in spirit to computer-science tests of intelligence advocated by Turing (1950). Indeed, a nearly complete accounting of early level neuronal response patterns can be achieved with extensions to the simple LN model framework -- most notably, by divisive normalization schemes in which the output of each LN neuron is normalized (e.g. Olshausen BA, Field DJ. Mountcastle VB. simple cells in V1), and the OR-like operation constructs some tolerance to changes in (e.g.) Our visual perception starts in the eye with light and dark pixels. At the neuronal population level, the population activity patterns in early sensory structures that correspond to different objects are tangled together, but they are gradually untangled as information is re-represented along the ventral stream and in IT. How Does the Brain Solve Visual Object Recognition? Bengio et al., 1995; Pinto et al., 2009; Serre et al., 2007b), which is one reason that neuronal evidence of unsupervised tolerance learning is of great interest to us (Section 3). 4A). Visual object recognition - PubMed Indeed, the field has implicitly adopted this view with attempts to apply cascaded NLN-like models deeper into the ventral stream (e.g. Lehky SR, Sereno AB. We propose that each processing motif has the same functional goal with respect to the patterns of activity arriving at its small input window; that is to use normalization architecture and unsupervised learning to factorize identity-preserving variables (e.g., position, scale, pose) from other variation (i.e., changes in object identity) in its input basis. In: Rockland K, Kaas J, Peters A, editors. The diversity of tasks that any biological recognition system must solve suggests that object recognition is not a single, general purpose process. edge, object) tend to be nearby in time (gray arrows). Decades of evidence argue that the primate ventral visual processing stream -- a set of cortical areas arranged along the occipital and temporal lobes (Fig. Breaking position-invariant object recognition. The computational crux of visual object recogn "Fata morgana" is the name given to optical illusions created by a three-layer temperature effect: a cold body of water or landmass, a layer of cooler air immediately above . Serre T, Oliva A, Poggio T. A feedforward architecture accounts for rapid categorization. FOIA Abstract. Indeed, some computational models adopt the notion of common processing motif, and make the same argument we reiterate here -- that an iterated application of a sub-algorithm is the correct way to think about the entire ventral stream (e.g., Fukushima, 1980; Kouh and Poggio, 2008; Riesenhuber and Poggio, 1999b; Serre et al., 2007a; see Fig. (Fukushima, 1980; Riesenhuber and Poggio, 1999b; Serre et al., 2007a), and they have been formalized into the linear-nonlinear (LN) class of encoding models in which each neuron adds and subtract its inputs, followed by a static nonlinearity (e.g., a threshold) to produce a firing rate response (Adelson and Bergen, 1985; Carandini et al., 2005; Heeger et al., 1996; Rosenblatt, 1958). Thus, object manifolds are thought to be gradually untangled through nonlinear selectivity and invariance computations applied at each stage of the ventral pathway (DiCarlo and Cox, 2007). Machine Recognition and the Brain Authors: P. Perry D. Keller Daniela Dsentrieb Carinthian Tech Research AG Abstract In order to progress further, machine recognition needs to break new. In sum, our view is that the output of the ventral stream is reflexively expressed in neuronal firing rates across a short interval of time (~50 ms), is an explicit object representation (i.e., object identity is easily decodable), and the rapid production of this representation is consistent with a largely feedforward, non-linear processing of the visual input. While we cannot review all the computer vision or neural network models that have relevance to object recognition in primates here, we refer the reader to reviews by (Bengio, 2009; Edelman, 1999; Riesenhuber and Poggio, 2000; Zhu and Mumford, 2006). Effect of lesion in visual cortical area V4 on the recognition of transformed objects. In sum, while all spike-timing codes cannot easily (if ever) be ruled out, rate codes over ~50 ms intervals are not only easy to decode by downstream neurons, but appear to be sufficient to support recognition behavior (see below). Zoccolan D, Cox DD, DiCarlo JJ. 2B), so that a simple hyperplane is all that is needed to separate them. What happens as each image is processed beyond V1 via the successive stages of the ventral stream anatomical hierarchy (V2, V4, pIT, aIT; Fig. Specific and columnar projection from area TEO to TE in the macaque inferotemporal cortex. Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J, Esteky H, Tanaka K, Bandettini PA. Simoncelli EP, Olshausen BA. Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. CORnet: Modeling the Neural Mechanisms of Core Object Recognition bottom row) without any object-specific or location-specific pre-cuing (e.g. Instead, most IT neurons are broadly tuned and the typical IT neuron responds to many different images and objects (Brincat and Connor, 2004; Freedman et al., 2006; Kreiman et al., 2006; Logothetis et al., 1995; Op de Beeck et al., 2001; Rolls, 1995, 2000; Rolls and Tovee, 1995; Vogels, 1999; Zoccolan et al., 2007); see Fig. Most studies have investigated the response properties of neurons in the ventral pathway by assuming a firing rate (or, equivalently, a spike count) code, i.e., by counting how many spikes each neuron fires over several tens or hundreds of milliseconds following the presentation of a visual image, adjusted for latency (e.g., see Fig. Similarly, while neuronal activity that provides some discriminative information about object shape has also been found in dorsal stream visual areas at similar hierarchical levels (Sereno and Maunsell, 1998), a direct comparison shows that it is not nearly as powerful as IT for object discrimination (Lehky and Sereno, 2007). 2A). position). Coding of color and form in the geniculostriate visual pathway (invited review). 10. The basic uniformity in structure of the neocortex. These conceptual models are central to current encoding models of biological object recognition (e.g. Put simply, we must synergize the fields of psychophysics, systems neuroscience and computer vision around the problem of object recognition. Engel TA, Wang XJ. Unfortunately, the approach requires exponentially more stimulus-response data to try to constrain an exponentially expanding set of possible cascaded NLN models, and thus we cannot yet distinguish between a principled inadequacy of the cascaded NLN model class and a failure to obtain enough data. Instead, the key single-unit property is called neuronal tolerance: the ability of each IT neuron to maintain its preferences among objects, even if only over a limited transformation range (e.g., position changes; see Fig. However, we are missing a clear level of abstraction and linking hypotheses that can connect mechanistic, NLN-like models to the resulting data reformatting that takes place in large neuronal populations (Fig. Olshausen BA, Field DJ. Later we outline how we aim to test that hypothesis. The hypothesized sub-population of neurons is also intermediate in its algorithmic complexity. Human-like levels of performance do not appear to require extensive recurrent communication, attention, task dependency or complex coding schemes that incorporate precise spike timing or synchrony. (PDF) Machine Recognition and the Brain - ResearchGate Importantly, IT neuronal populations are demonstrably better at object identification and categorization than populations at earlier stages of the ventral pathway (Freiwald and Tsao, 2010; Hung et al., 2005; Li et al., 2006; Rust and DiCarlo, 2010). ), algorithmic strategies (how might it carry out that job? Neurobiology. and transmitted securely. Each sub-population embeds mechanisms that tune the synaptic weights to concentrate its dynamic response range to span regions of its input space where images are typically found (e.g., do not bother encoding things you never see). Pinto N, Barhomi Y, Cox DD, DiCarlo JJ. NR was supported by the US National Eye Institute (NIH NEI) and a fellowship from the Alfred P. Sloan Foundation. Lecun Y, Huang F-J, Bottou L. Learning Methods for generic object recognition with invariance to pose and lighting. One line will use high-throughput computer simulations to systematically explore the very large space of possible sub-network algorithms, implementing each possibility as a cascaded, full scale algorithm, and measuring performance in carefully considered benchmark object recognition tasks. Notably, these mechanisms are themselves phenomena, that also require mechanistic explanations at an even lower level of abstraction (e.g., neuronal connectivity, intracellular events). The reason is that, while neuroscience has pointed to properties of the ventral stream that are likely critical to building explicit object representation (outlined above), there are many possible ways to instantiate such ideas as specific algorithms. Our hypothesis is that each ventral stream cortical sub-population uses at least three common, genetically encoded mechanisms (described below) to carry out that meta job description and that together, those mechanisms direct it to choose a set of input weights, a normalization pool, and a static nonlinearity that lead to improved subspace untangling. divided) by a weighted sum of a pool of nearby neurons (reviewed by Carandini and Heeger, 2011). 8600 Rockville Pike Unlike NLN models, the canonical processing motif is a multi-input, multi-output circuit, with multiple afferents to layer 4 and multiple efferents from layer 2/3 and where the number of outputs is approximately the same as the number of inputs, thereby preserving the dimensionality of the local representation. Performance magnitude approaches ceiling level with only a few hundred neurons (left panel), and the same population decode gives nearly perfect generalization across moderate changes in position (1.5 deg and 3 deg shifts), scale (0.5x/2x and 0.33x/3x), and context (right panel), which is consistent with previous work (Hung et al., 2005); right bar) and with the simulations in (D). How Does the Brain Solve Visual Object Recognition? - Cell Press ), and thus our survival, depends on our accurate and rapid extraction of object identity from the patterns of photons on our retinae. Operationally, we mean that object identity will be easier to linearly decode on the output space than the input space, and we have some recent progress in that direction (Rust and DiCarlo, 2010). Historically, mechanistic insights into the computations performed by local cortical circuits have derived from bottom up approaches that aim to quantitatively describe the encoding functions that map image features to the firing rate responses of individual neurons. Potter MC. These very large, instantiated algorithm spaces are now being used to design large-scale neurophysiological recording experiments that aim to winnow out progressively more accurate models of the ventral visual stream. Approximate total number of neuron (both hemispheres) is shown in the corner of each area (M = million). The wake-sleep algorithm for unsupervised neural networks. a hyperplane, see black dashed line; see (DiCarlo and Cox, 2007). Information flow and temporal coding in primate pattern vision. Responses of macaque inferior temporal neurons to overlapping shapes. Lecun et al., 2004; Mel, 1997; Riesenhuber and Poggio, 1999b; Serre et al., 2007a). (Keysers et al., 2001; Potter, 1976). (Ikkai et al., 2011; Noudoost et al., 2010; Valyear et al., 2006) and to shape the hand to manipulate an object (e.g. However, no specific algorithm has yet achieved the performance of humans or explained the population behavior of IT (Pinto et al., 2011; Pinto et al., 2010). One framework postulates that each successive visual area serially adds more processing power so as to solve increasingly complex tasks, such as the untangling of object identity manifolds (DiCarlo and Cox, 2007; Marr, 1982; Riesenhuber and Poggio, 1999b).

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