%0 Journal Article %J eLife %D 2018 %T Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. %A Issa, Elias B %A Cadieu, Charles F %A DiCarlo, James J %K Animals %K Brain Mapping %K Face %K Humans %K Macaca mulatta %K Models %K Neurological %K Neurons %K Pattern Recognition %K Photic Stimulation %K Reaction Time %K Visual %K Visual Cortex %K Visual Perception %X

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.

%B eLife %V 7 %8 11/2018 %G eng %U https://elifesciences.org/articles/42870https://cdn.elifesciences.org/articles/42870/elife-42870-v2.pdf %R 10.7554/eLife.42870 %0 Journal Article %J Journal of Neurophysiology %D 2004 %T Using Neuronal Latency to Determine Sensory–Motor Processing Pathways in Reaction Time Tasks %A DiCarlo, James J. %A Maunsell, John H. R. %K Action Potentials %K Afferent %K Animal %K Animals %K Behavior %K Macaca mulatta %K Male %K Models %K Motor Neurons %K Neural Pathways %K Neurological %K Neurons %K Photic Stimulation %K Psychomotor Performance %K Reaction Time %K Task Performance and Analysis %K Temporal Lobe %K Time Factors %K Visual Fields %X

We describe a new technique that uses the timing of neuronal and behavioral responses to explore the contributions of individual neurons to specific behaviors. The approach uses both the mean neuronal latency and the trial-by-trial covariance between neuronal latency and behavioral response. Reliable measurements of these values were obtained from single-unit recordings made from anterior inferotemporal (AIT) cortex and the frontal eye fields (FEF) in monkeys while they performed a choice reaction time task. These neurophysiological data show that the responses of AIT neurons and some FEF neurons have little covariance with behavioral response, consistent with a largely "sensory" response. The responses of another group of FEF neurons with longer mean latency covary tightly with behavioral response, consistent with a largely "motor" response. A very small fraction of FEF neurons had responses consistent with an intermediate position in the sensory-motor pathway. These results suggest that this technique is a valuable tool for exploring the functional organization of neuronal circuits that underlie specific behaviors.

 

%B Journal of Neurophysiology %V 93 %P 2974 - 2986 %8 11/2004 %G eng %U https://www.physiology.org/doi/10.1152/jn.00508.2004 %N 5 %! Journal of Neurophysiology %R 10.1152/jn.00508.2004 %0 Journal Article %J Behavioural Brain Research %D 2002 %T Receptive field structure in cortical area 3b of the alert monkey %A DiCarlo, James J %A Johnson, Kenneth O %K Action Potentials %K Afferent %K Animals %K Brain Mapping %K Evoked Potentials %K Haplorhini %K Models %K Neurological %K Neurons %K Orientation %K Reproducibility of Results %K Skin %K Somatosensory %K Somatosensory Cortex %X

More than 350 neurons with fingerpad receptive fields (RFs) were studied in cortical area 3b of three alert monkeys. Random dot patterns, which contain all stimulus patterns with equal probability, were scanned across these RFs at three velocities and eight directions to reveal the RFs’ spatial and temporal structure. Area 3b RFs are characterized by three components: (1) a single, central excitatory region of short duration, (2) one or more inhibitory regions, also of short duration, that are adjacent to and nearly synchronous with the excitation, and (3) a region of inhibition that overlaps the excitation partially or totally and is temporally delayed with respect to the first two components. As a result of these properties, RF spatial structure depends on scanning direction but is virtually unaffected by changes in scanning velocity. This RF characterization, which is derived solely from responses to scanned random-dot patterns, predicts a neuron's responses to random patterns accurately, as expected, but it also predicts orientation sensitivity and preferred orientation measured with a scanned bar. Both orientation sensitivity and the ratio of coincident inhibition (number 2 above) to excitation are stronger in the supra- and infragranular layers than in layer IV.

%B Behavioural Brain Research %V 135 %P 167 - 178 %8 01/2002 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0166432802001626 %N 1-2 %! Behavioural Brain Research %R 10.1016/S0166-4328(02)00162-6 %0 Journal Article %J Psychological Review %D 1992 %T Stimulus configuration, classical conditioning, and hippocampal function. %A Schmajuk, Nestor A. %A DiCarlo, James J. %K Animals %K Association Learning %K Brain Mapping %K Cerebellum %K Cerebral Cortex %K Classical %K Computer Simulation %K Conditioning %K Hippocampus %K Humans %K Models %K Neural Pathways %K Neurological %X

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.

 

%B Psychological Review %V 99 %P 268 - 305 %8 04/1992 %G eng %U http://doi.apa.org/getdoi.cfm?doi=10.1037/0033-295X.99.2.268 %N 2 %! Psychological Review %R 10.1037/0033-295X.99.2.268 %0 Journal Article %J Behavioral Neuroscience %D 1991 %T A neural network approach to hippocampal function in classical conditioning. %A Schmajuk, Nestor A. %A DiCarlo, James J. %K Animals %K Cerebellum %K Classical %K Computer Simulation %K Conditioning %K Extinction %K Eyelid %K Hippocampus %K Models %K Nerve Net %K Neurological %K Neurons %K Psychological %K Rabbits %K Reaction Time %X

Hippocampal participation in classical conditioning in terms of S. Grossberg's (1975) attentional theory is described. According to the theory, pairing of a conditioned stimulus/stimuli (CS) with an unconditioned stimulus/stimuli (UCS) causes both an association of the sensory representation of the CS with the UCS (conditioned reinforcement learning) and an association of the sensory representation of the CS with the drive representation of the UCS (incentive motivation learning). Sensory representations compete for a limited-capacity short-term memory (STM). The STM regulation hypothesis, which proposes that the hippocampus controls incentive motivation, self-excitation, and competition among sensory representations thereby regulating the contents of a limited capacity STM, is introduced. Under the STM regulation hypothesis, nodes and connections in Grossberg's neural network are mapped onto regional hippocampal-cerebellar circuits. The resulting neural model provides (a) a framework for understanding the dynamics of information processing and storage in the hippocampus and cerebellum during classical conditioning of the rabbit's nictitating membrane, (b) principles for understanding the effect of different hippocampal manipulations on classical conditioning, and (c) novel and testable predictions. 

 

%B Behavioral Neuroscience %V 105 %P 82 - 110 %8 01/1991 %G eng %U http://doi.apa.org/getdoi.cfm?doi=10.1037/0735-7044.105.1.82 %N 1 %! Behavioral Neuroscience %R 10.1037/0735-7044.105.1.82