%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 Science %D 2005 %T Fast Readout of Object Identity from Macaque Inferior Temporal Cortex %A Hung, Chou P. %A Kreiman, Gabriel %A Poggio, Tomaso %A DiCarlo, James J. %K Action Potentials %K Animals %K Brain Mapping %K Macaca mulatta %K Neurons %K Psychology %K Psychomotor Performance %K Recognition %K Temporal Lobe %K Time Factors %K Visual Perception %X

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object "identity" and "category." This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.

 

%B Science %V 310 %P 863 - 866 %8 04/2005 %G eng %U https://www.sciencemag.org/lookup/doi/10.1126/science.1117593 %! Science %R 10.1126/science.1117593 %0 Journal Article %J Journal of Neuroscience %D 2005 %T Multiple Object Response Normalization in Monkey Inferotemporal Cortex %A Zoccolan, D. %A Cox, David D. %A DiCarlo, James J. %K Animals %K Brain Mapping %K Macaca mulatta %K Male %K Photic Stimulation %K Posture %K Psychology %K Recognition %K Temporal Lobe %K Visual Pathways %K Visual Perception %X

The highest stages of the visual ventral pathway are commonly assumed to provide robust representation of object identity by disregarding confounding factors such as object position, size, illumination, and the presence of other objects (clutter). However, whereas neuronal responses in monkey inferotemporal cortex (IT) can show robust tolerance to position and size changes, previous work shows that responses to preferred objects are usually reduced by the presence of nonpreferred objects. More broadly, we do not yet understand multiple object representation in IT. In this study, we systematically examined IT responses to pairs and triplets of objects in three passively viewing monkeys across a broad range of object effectiveness. We found that, at least under these limited clutter conditions, a large fraction of the response of each IT neuron to multiple objects is reliably predicted as the average of its responses to the constituent objects in isolation. That is, multiple object responses depend primarily on the relative effectiveness of the constituent objects, regardless of object identity. This average effect becomes virtually perfect when populations of IT neurons are pooled. Furthermore, the average effect cannot simply be explained by attentional shifts but behaves as a primarily feedforward response property. Together, our observations are most consistent with mechanistic models in which IT neuronal outputs are normalized by summed synaptic drive into IT or spiking activity within IT and suggest that normalization mechanisms previously revealed at earlier visual areas are operating throughout the ventral visual stream.

%B Journal of Neuroscience %V 25 %P 8150 - 8164 %8 07/2005 %G eng %U http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2058-05.2005 %N 36 %! Journal of Neuroscience %R 10.1523/JNEUROSCI.2058-05.2005 %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 The Journal of Neuroscience %D 1999 %T Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey %A DiCarlo, James J. %A Johnson, Kenneth O. %K Adaptation %K Animals %K Brain Mapping %K Cortical Synchronization %K Evoked Potentials %K Female %K Macaca mulatta %K Male %K Neural Inhibition %K Physiological %K Somatosensory Cortex %K Visual Fields %X

This is the second in a series of studies of the neural representation of tactile spatial form in cortical area 3b of the alert monkey. We previously studied the spatial structure of 330 area 3b neuronal receptive fields (RFs) on the fingerpad with random dot patterns scanned at one velocity (40 mm/sec; DiCarlo et al., 1998). Here, we analyze the temporal structure of 84 neuronal RFs by studying their spatial structure at three scanning velocities (20, 40, and 80 mm/sec). As in the previous study, most RFs contained a single, central, excitatory region and one or more surrounding or flanking inhibitory regions. The mean time delay between skin stimulation and its excitatory effect was 15.5 msec. Except for differences in mean rate, each neuron’s response and the spatial structure of its RF were essentially unaffected by scanning velocity. This is the expected outcome when excitatory and inhibitory effects are brief and synchronous. However, that interpretation is consistent neither with the reported timing of excitation and inhibition in somatosensory cortex nor with the third study in this series, which investigates the effect of scanning direction and shows that one component of inhibition lags behind excitation. We reconcile these observations by showing that overlapping (in-field) inhibition delayed relative to excitation can produce RF spatial structure that is unaffected by changes in scanning velocity. Regardless of the mechanisms, the velocity invariance of area 3b RF structure is consistent with the velocity invariance of tactile spatial perception (e.g., roughness estimation and form recognition).

%B The Journal of Neuroscience %V 19 %P 401 - 419 %8 01/1999 %G eng %U http://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.19-01-00401.1999 %N 1 %! J. Neurosci. %R 10.1523/JNEUROSCI.19-01-00401.1999 %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