20,501 research outputs found
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
The primate brain contains a hierarchy of visual areas, dubbed the ventral
stream, which rapidly computes object representations that are both specific
for object identity and relatively robust against identity-preserving
transformations like depth-rotations. Current computational models of object
recognition, including recent deep learning networks, generate these properties
through a hierarchy of alternating selectivity-increasing filtering and
tolerance-increasing pooling operations, similar to simple-complex cells
operations. While simulations of these models recapitulate the ventral stream's
progression from early view-specific to late view-tolerant representations,
they fail to generate the most salient property of the intermediate
representation for faces found in the brain: mirror-symmetric tuning of the
neural population to head orientation. Here we prove that a class of
hierarchical architectures and a broad set of biologically plausible learning
rules can provide approximate invariance at the top level of the network. While
most of the learning rules do not yield mirror-symmetry in the mid-level
representations, we characterize a specific biologically-plausible Hebb-type
learning rule that is guaranteed to generate mirror-symmetric tuning to faces
tuning at intermediate levels of the architecture
A Minimal Architecture for General Cognition
A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International
Joint Conference on Neural Network
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the
response of a sensory system to a particular functional dimension, such as the
eye movement following the motion of a visual scene. Here, we describe a
framework to generate random texture movies with controlled information
content, i.e., Motion Clouds. These stimuli are defined using a generative
model that is based on controlled experimental parametrization. We show that
Motion Clouds correspond to dense mixing of localized moving gratings with
random positions. Their global envelope is similar to natural-like stimulation
with an approximate full-field translation corresponding to a retinal slip. We
describe the construction of these stimuli mathematically and propose an
open-source Python-based implementation. Examples of the use of this framework
are shown. We also propose extensions to other modalities such as color vision,
touch, and audition
A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection
A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624
Culture in Mind - An Enactivist Account: Not Cognitive Penetration But Cultural Permeation
Advancing a radically enactive account of cognition, we provide arguments in favour of the possibility that cultural factors permeate rather than penetrate cognition, such that cognition extensively and transactionally incorporates cultural factors rather than there being any question of cultural factors having to break into the restricted confines of cognition. The paper reviews the limitations of two classical cognitivist, modularist accounts of cognition and a revisionary, new order variant of cognitivism – a Predictive Processing account of Cognition, or PPC. It argues that the cognitivist interpretation of PPC is conservatively and problematically attached to the idea of inner models and stored knowledge. In abandoning that way of understanding PPC, it offers a radically enactive alternative account of how cultural factors matter to cognition – one that abandons all vestiges of the idea that cultural factors might contentfully communicate with basic forms of cognition. In place of that idea, the possibility that culture permeates cognition is promoted
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