Narihisa Matsumoto

Neuronal Mechanisms Encoding Global-to-Fine Information in Inferior-Temporal Cortex

Sugase et al. found that global information is represented at the initial transient firing of a single face-responsive neuron in inferior-temporal (IT) cortex, and that finer information is represented at the subsequent sustained firing. A feed-forward model and an attractor network are conceivable models to reproduce this dynamics. The attractor network, specifically an associative memory model, is employed to elucidate the neuronal mechanisms producing the dynamics. The results obtained by computer simulations show that a state of neuronal population initially approaches to a mean state of similar memory patterns, and that it finally converges to a memory pattern. This dynamics qualitatively coincides with that of face-responsive neurons. The dynamics of a single neuron in the model also coincides with that of a single face-responsive neuron. Furthermore, we propose two physiological experiments and predict the results from our model. Both predicted results are not explainable by the feed-forward model. Therefore, if the results obtained by actual physiological experiments coincide with our predicted results, the attractor network might be the neuronal mechanisms producing the dynamics of face-responsive neurons.

Retrieval property of attractor network with synaptic depression

Synaptic connections are known to change dynamically. High-frequency presynaptic inputs induce decrease of synaptic weights. This process is known as short-term synaptic depression. The synaptic depression controls a gain for presynaptic inputs. However, it remains a controversial issue what are functional roles of this gain control. We propose a new hypothesis that one of the functional roles is to enlarge basins of attraction. To verify this hypothesis, we employ a binary discrete-time associative memory model which consists of excitatory and inhibitory neurons. It is known that the excitatory–inhibitory balance controls an overall activity of the network. The synaptic depression might incorporate an activity control mechanism. Using a mean-field theory and computer simulations, we find that the synaptic depression enlarges the basins at a small loading rate while the excitatory–inhibitory balance enlarges them at a large loading rate. Furthermore the synaptic depression does not affect the steady state...

Stimulus-Related Activity during Conditional Associations in Monkey Perirhinal Cortex Neurons Depends on Upcoming Reward Outcome

Acquiring the significance of events based on reward-related information is critical for animals to survive and to conduct social activities. The importance of the perirhinal cortex for reward-related information processing has been suggested. To examine whether or not neurons in this cortex represent reward information flexibly when a visual stimulus indicates either a rewarded or unrewarded outcome, neuronal activity in the macaque perirhinal cortex was examined using a conditional-association cued-reward task. The task design allowed us to study how the neuronal responses depended on the animals prediction of whether it would or would not be rewarded. Two visual stimuli, a color stimulus as Cue1 followed by a pattern stimulus as Cue2, were sequentially presented. Each pattern stimulus was conditionally associated with both rewarded and unrewarded outcomes depending on the preceding color stimulus. We found an activity depending upon the two reward conditions during Cue2, i.e., pattern stimulus presentation. The response appeared after the response dependent upon the image identity of Cue2. The response delineating a specific cue sequence also appeared between the responses dependent upon the identity of Cue2 and reward conditions. Thus, when Cue1 sets the context for whether or not Cue2 indicates a reward, this region represents the meaning of Cue2, i.e., the reward conditions, independent of the identity of Cue2. These results suggest that neurons in the perirhinal cortex do more than associate a single stimulus with a reward to achieve flexible representations of reward information.

Face Inversion Decreased Information about Facial Identity and Expression in Face-Responsive Neurons in Macaque Area TE

To investigate the effect of face inversion and thatcherization (eye inversion) on temporal processing stages of facial information, single neuron activities in the temporal cortex (area TE) of two rhesus monkeys were recorded. Test stimuli were colored pictures of monkey faces (four with four different expressions), human faces (three with four different expressions), and geometric shapes. Modifications were made in each face-picture, and its four variations were used as stimuli: upright original, inverted original, upright thatcherized, and inverted thatcherized faces. A total of 119 neurons responded to at least one of the upright original facial stimuli. A majority of the neurons (71%) showed activity modulations depending on upright and inverted presentations, and a lesser number of neurons (13%) showed activity modulations depending on original and thatcherized face conditions. In the case of face inversion, information about the fine category (facial identity and expression) decreased, whereas information about the global category (monkey vs human vs shape) was retained for both the original and thatcherized faces. Principal component analysis on the neuronal population responses revealed that the global categorization occurred regardless of the face inversion and that the inverted faces were represented near the upright faces in the principal component analysis space. By contrast, the face inversion decreased the ability to represent human facial identity and monkey facial expression. Thus, the neuronal population represented inverted faces as faces but failed to represent the identity and expression of the inverted faces, indicating that the neuronal representation in area TE cause the perceptual effect of face inversion.

Role of Temporal Processing Stages by Inferior Temporal Neurons in Facial Recognition

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition.

Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of the pre- and postsynaptic spikes. This determines whether long-term potentiation (LTP) or long-term depression (LTD) is induced. This synaptic plasticity has been called temporally asymmetric Hebbian plasticity (TAH). Many authors have numerically demonstrated that neural networks are capable of storing spatiotemporal patterns. However, the mathematical mechanism of the storage of spatiotemporal patterns is still unknown, and the effect of LTD is particularly unknown. In this article, we employ a simple neural network model and show that interference between LTP and LTD disappears in a sparse coding scheme. On the other hand, the covariance learning rule is known to be indispensable for the storage of sparse patterns. We also show that TAH has the same qualitative effect as the covariance rule when spatiotemporal patterns are embedded in the network.

Synaptic depression enlarges basin of attraction

Neurophysiological experiments show that synaptic depression controls a gain for presynaptic inputs. However, the functional roles of this gain control remain unknown. We propose that one of the functional roles is to enlarge basins of attraction. To verify this, we employ an associative memory model. An activity control is requisite for the stable retrieval of sparse patterns. We investigate a storage capacity and the basins of attraction. Consequently, the basins of attraction are enlarged while the storage capacity does not change. Thus, the synaptic depression might incorporate the activity control mechanism.

Robustness of retrieval properties against imbalance between long-term potentiation and depression of spike-timing-dependent plasticity.

Spike-timing-dependent plasticity (STDP) has recently been shown in some physiological studies. STDP depends on the precise temporal relationship of presynaptic and postsynaptic spikes. Many authors have indicated that a precise balance between long-term potentiation (LTP) and long-term depression (LTD) of STDP is significant for a stable learning. However, a situation in which the balance is maintained precisely is inconceivable in the brain. Using a method of the statistical neurodynamics, we show robust retrieval properties of spatiotemporal patterns in an associative memory model against the imbalance between LTP and LTD. When the fluctuation of LTD is assumed to obey a Gaussian distribution with mean 0 and variance delta(2), the storage capacity takes a finite value even at large delta. This means that the balance between LTP and LTD of STDP need not be maintained precisely, but must be maintained on average. Furthermore, we found that the basin of attraction becomes smaller as delta increases while an initial critical overlap remains unchanged.

Doubly sparse factor models for unifying feature transformation and feature selection

A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

Impact of deviation from precise balance of spike-timing-dependent plasticity

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of pre- and post-synaptic spikes and this is called spike-timing-dependent plasticity (STDP). Many authors have claimed that a precise balance between long-term potentiation (LTP) and long-term depression (LTD) of STDP is crucial in the storage of spatio-temporal patterns. Some authors have numerically investigated the impact of an imbalance between LTP and LTD on the network properties. However, the mathematical mechanism remains unknown. We analytically show that an associative memory network has the robust retrieval properties of spatio-temporal patterns, and these properties make the network less vulnerable to any deviation from a precise balance between LTP and LTD when the network contains a finite number of neurons.