Finding and recognizing objects in natural scenes: complementary computations in the dorsal and ventral visual systems.

Searching for and recognizing objects in complex natural scenes is implemented by multiple saccades until the eyes reach within the reduced receptive field sizes of inferior temporal cortex (IT) neurons. We analyze and model how the dorsal and ventral visual streams both contribute to this. Saliency detection in the dorsal visual system including area LIP is modeled by graph-based visual saliency, and allows the eyes to fixate potential objects within several degrees.

Deformation-specific and deformation-invariant visual object recognition: pose vs. identity recognition of people and deforming objects.

When we see a human sitting down, standing up, or walking, we can recognize one of these poses independently of the individual, or we can recognize the individual person, independently of the pose. The same issues arise for deforming objects. For example, if we see a flag deformed by the wind, either blowing out or hanging languidly, we can usually recognize the flag, independently of its deformation; or we can recognize the deformation independently of the identity of the flag.

The mechanisms for pattern completion and pattern separation in the hippocampus.

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 together with a large number of independent memories stored in CA3 include a sparse distributed representation which is enhanced by the graded firing rates of CA3 neurons, representations that are independent due to the randomizing effect of the mossy fibers, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization.

A quantitative theory of the functions of the hippocampal CA3 network in memory.

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory.

A biased activation theory of the cognitive and attentional modulation of emotion.

Cognition can influence emotion by biasing neural activity in the first cortical region in which the reward value and subjective pleasantness of stimuli is made explicit in the representation, the orbitofrontal cortex (OFC). The same effect occurs in a second cortical tier for emotion, the anterior cingulate cortex (ACC). Similar effects are found for selective attention, to for example the pleasantness vs.

Willed action, free will, and the stochastic neurodynamics of decision-making.

It is shown that the randomness of the firing times of neurons in decision-making attractor neuronal networks that is present before the decision cues are applied can cause statistical fluctuations that influence the decision that will be taken. In this rigorous sense, it is possible to partially predict decisions before they are made. This raises issues about free will and determinism.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described.

Chemosensory learning in the cortex.

Taste is a primary reinforcer. Olfactory-taste and visual-taste association learning takes place in the primate including human orbitofrontal cortex to build representations of flavor. Rapid reversal of this learning can occur using a rule-based learning system that can be reset when an expected taste or flavor reward is not obtained, that is by negative reward prediction error, to which a population of neurons in the orbitofrontal cortex responds.

Prediction of Decisions from Noise in the Brain before the Evidence is Provided.

Can decisions be predicted from brain activity? It is frequently difficult in neuroimaging studies to determine this, because it is not easy to establish when the decision has been taken. In a rigorous approach to this issue, we show that in a neurally plausible integrate-and-fire attractor-based model of decision-making, the noise generated by the randomness in the spiking times of neurons can be used to predict a decision for 0.5 s or more before the decision cues are applied.