@article {15, title = {Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals.}, journal = {eLife}, volume = {7}, year = {2018}, month = {11/2018}, abstract = {

Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably,~30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their responses to typical configurations of their preferred face parts relative to their response for atypical configurations even while neurons in higher areas achieved and maintained a preference for typical configurations. These hierarchical neural dynamics were inconsistent with standard feedforward circuits. Rather, recurrent models computing prediction errors between stages captured the observed temporal signatures. This model of neural dynamics, which simply augments the standard feedforward model of online vision, suggests that neural responses to static images may encode top-down prediction errors in addition to bottom-up feature estimates.

}, keywords = {Animals, Brain Mapping, Face, Humans, Macaca mulatta, Models, Neurological, Neurons, Pattern Recognition, Photic Stimulation, Reaction Time, Visual, Visual Cortex, Visual Perception}, issn = {2050-084X}, doi = {10.7554/eLife.42870}, url = {https://elifesciences.org/articles/42870https://cdn.elifesciences.org/articles/42870/elife-42870-v2.pdf}, author = {Issa, Elias B and Cadieu, Charles F and DiCarlo, James J} } @article {119, title = {Stimulus configuration, classical conditioning, and hippocampal function.}, journal = {Psychological Review}, volume = {99}, year = {1992}, month = {04/1992}, pages = {268 - 305}, abstract = {

Hippocampal participation in classical conditioning is described in terms of a multilayer network that portrays stimulus configuration. The network (a) describes behavior in real time, (b) incorporates a layer of \"hidden\" units positioned between input and output units, (c) includes inputs that are connected to the output directly as well as indirectly through the hidden-unit layer, and (d) uses a biologically plausible backpropagation procedure to train the hidden-unit layer. Nodes and connections in the neural network are mapped onto regional cerebellar, cortical, and hippocampal circuits, and the effect of lesions of different brain regions is formally studied. Computer simulations of the following classical conditioning paradigms are presented: acquisition of delay and trace conditioning, extinction, acquisition-extinction series of delay conditioning, blocking, over-shadowing, discrimination acquisition, discrimination reversal, feature-positive discrimination, conditioned inhibition, negative patterning, positive patterning, and generalization. The model correctly describes the effect of hippocampal and cortical lesions in many of these paradigms, as well as neural activity in hippocampus and medial septum during classical conditioning. Some of these results might be extended to the description of anterograde amnesia in human patients.

}, keywords = {Animals, Association Learning, Brain Mapping, Cerebellum, Cerebral Cortex, Classical, Computer Simulation, Conditioning, Hippocampus, Humans, Models, Neural Pathways, Neurological}, issn = {0033-295X}, doi = {10.1037/0033-295X.99.2.268}, url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0033-295X.99.2.268}, author = {Schmajuk, Nestor A. and DiCarlo, James J.} }