Past events

The human ventral stream consists of regions in the lateral and ventral aspects of the occipital and temporal lobes and is involved in visual recognition. One robust characteristic of selectivity in the adult human ventral stream is category selectivity. Category selectivity is manifested by both a regional preference to particular object categories, such as faces, places and bodyparts, as well as in specific (and reproducible) distributed response patterns across the ventral stream for different object categories. However, it is not well understood how these representations come about throughout development and how experience modifies these representations and how do. I will describe two sets of experiments in which we addressed these important questions. First, I will describe experiments in which we examined changes in category selectivity throughout development from middle childhood (7-11 years), through adolescence (12-16) into adulthood. Surprisingly, we find that it takes more than a decade for the development of adult-like face and place-selective regions. In contrast, the lateral occipital object-selective region showed an adult-like profile by age 7. Further, recent findings from our research indicate that face-selective regions have a particularly prolonged development as they continue develop through adolescence in correlation with improved face, but not object or scene recognition memory. Development manifests as increases in the size of face-selective regions, increases in face-selectivity as well as increases in the distinctiveness of distributed response patterns to faces compared to nonfaces. Second, I will describe experiments in adults in which we examined the effect of repetition on categorical responses in the ventral stream. Repeating objects decreases responses in the human ventral stream. Repetition in lateral ventral regions manifests as a proportional effects in which responses to repeated objects are a constant fraction of nonrepeating stimuli with no change in selectivity. In contrast in medial ventral temporal cortex, we find differential effects across time scales whereby immediate repetitions produce proportional effects, but long-lagged repetitions sharpen responses, increasing category selectivity. Finally, I will discuss the implications of these results on plasticity in the ventral stream and our theoretical models linking between fMRI measurements and the underlying neural mechanisms.

The human brain consists of a vast number of neurons interconnected by specialized communication devices known as synapses. It is widely believed that activity-dependent modifications to synaptic connections - synaptic plasticity - represents a fundamental mechanism for altering network function, giving rise to emergent phenomena commonly referred to as learning and memory. This belief also implies, however, that synapses, when not driven to change their properties by physiologically relevant stimuli, should retain these properties over time. Otherwise, physiologically relevant modifications would be gradually lost amidst spurious changes and spontaneous drift. We refer to the expected default tendency of synapses to hold onto their properties as "synaptic tenacity". We have begun to examine the degree to which synaptic structures are indeed tenacious. To that end we have developed unique, long-term imaging technologies that allow us to record the remodeling of individual synaptic specializations in networks of dissociated cortical neurons over many days and even weeks at temporal resolutions of 10-30 minutes, and at the same time record and manipulate the levels of activity in the same networks. These approaches have allowed us to uncover intriguing relationships between network activity, synaptic tenacity and synaptic remodeling. These experiments and the insights they have provided will be described.

To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale (_5 _ 105) conductancebased, integrate-and-fire neuronal network model of an _16-mm2 patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.In addition, we address the functional significance of the IDS cortical operating points by investigating our model cortex response to the Hikosaka linemotion illusion (LMI) stimulus—a cue of a quickly flashed stationary square followed a few milliseconds later by a stationary bar. As revealed by voltage-sensitive dye imaging, there is an intriguing similarity between the cortical spatiotemporal activity in response to (i) the Hikosaka LMI stimulus and (ii) a small moving square. This similarity is believed to be associated with the preattentive illusory motion perception. Our numerical cortex produces similar spatiotemporal patterns in response to the two stimuli above, which are both in very good agreement with experimental results. The essential network mechanisms underpinning the LMI phenomenon in our model are (i) the spatiotemporal structure of the LMI input as sculpted by the lateral geniculate nucleus, (ii) a priming effect of the long-range NMDA-type cortical coupling, and (iii) the NMDA conductance–voltage correlation manifested in the IDS state. This mechanism in our model cortex, in turn, suggests a physiological underpinning for the LMI-associated patterns in the visual cortex of anaesthetized cat.

Memory contents are believed to be stored in the efficiency of synapses in recurrent networks of the brain. In prefrontal cortex it was found that short and long term memory is accompanied with persistent spike rates [1,2] indicating that reentrant activities in recurrent networks reflect the content of synaptically encoded memories [3]. It is, however, not clear which mechanisms enable synapses to sequentially accumulate information from the stream of patterned inputs which under natural conditions enter as perturbations of the ongoing neuronal activities. For successful incremental learning only novel input should alter specific synaptic efficacies while previous memories should be preserved as long as network capacity is not exhausted. In other words, synaptic learning should realise a palimpsest property with erasing the oldest memories first. Here we demonstrate that synaptic modifications which sensitively depend on /temporal changes /of pre- and the post-synaptic neural activity can enable such incremental learning in recurrent neuronal networks. We investigated a realistic rate based model and found that for robust incremental learning in a setting with sequentially presented input patterns specific adaptation mechanisms of STDP are required that go beyond the observed synaptic changes for sequences of pre- and post-synaptic spikes [4]. Our predicted pre- and post-synaptic adaptation of synaptic changes in response to respective rate changes are experimentally testable and --if confirmed-- would suggest that STDP provides an unsupervised learning mechanism particularly well suited for incremental memory acquisition by circumventing the stability-plasticity dilemma.