Past events

Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space, and have been hypothesized to form the neural substrate for dead-reckoning. I will address two theoretical questions that arise from this remarkable experimental discovery: First, how is grid cell dynamics generated in the brain. Second, what information is conveyed in grid cell activity. In discussing the first question, I will focus on continuous-attractor models of grid cell activity, and ask whether such models can generate regular triangular grid responses based on inputs that encode only the rat's velocity and heading direction. In a recent work, we provided a proof of concept that such models can accurately integrate velocity inputs, along trajectories spanning 10-100 meters in length and lasting 1-10 minutes. The range of accurate integration depends on various properties of the continous-attractor network. After presenting these results, I will discuss possible experiments that may differentiate the continuous-attractor model from other proposed models, where activity arises independently in each cell. In the second part of the talk, I will examine the relationship between grid cell firing and rat location, asking what information is present in grid-cell activity about the rat's position. I will argue that, although the periodic response of grid cells may appear wasteful, the grid-cell code is in fact combinatorial in capacity, and allows for unambiguous position representations over ranges vastly larger than the ~0.5-10m periods of individual lattices. Further, the grid cell representation has properties that could facilitate the arithmetic computation involved in position updating during path integration. I will conclude by mentioning some of the implications for downstream readouts, and possible experimental tests.

How long-term memories are stored as physical traces in the brain is a fundamental question in neuroscience. Most molecular work on LTP, a widely studied physiological model of memory, has focused on the early signaling events regulating new protein synthesis that mediates initial LTP induction. But what are the newly synthesized proteins that function in LTP maintenance, how do they sustain synaptic potentiation, and do they store long-term memory? Recent studies have identified a brain-specific, autonomously active, atypical PKC isoform, PKMzeta, that is central to the mechanism maintaining the late phase of LTP. In behavioral experiments, the persistent activity of PKMzeta maintains spatial memories in hippocampus, fear-motivated memories in amygdala, and, in work performed in the Dudai lab, elementary associative memories in neocortex. This is because 1-day to several month-old memories appear to be rapidly erased after local intracranial PKMzeta inhibition. PKMzeta, a persistently active enzyme, is thus the first identified molecular component of the long-term memory trace.

What is the temporal precision of cortical activity? It is clear that the wide range of coding schemes occur on different time scales: Millisecond scale characterizes direct sensory events, tens to hundreds of milliseconds scale characterizes attention processes, while different states of alertness can last many seconds. It seems that there is a direct relationship between the time scale and the spatial resolution in cortical activity. The activity involved in a direct sensory process is well defined in small areas; for example an orientation column. On the other hand an attention process involves huge populations and maybe even the whole cortex. In my talk I will try to bridge the gap between the recordings of single neurons (intracellular and extracellular recordings) and the recordings of large populations of neurons (EEG, LFP,VSD or fMRI) in order to understand the spatio-temporal organization underlying the function of cortical neuronal population and it's relation to brain connectivity. I will relate to the following topics: - What is the size of the neuronal population that contributes to the population activity in different cognitive states? - What is the degree of synchronization within this population? - What is the relationship between the population activity and the activity of single cortical neurons? - The dynamic of coherent activity in neuronal assemblies - ongoing & evoked activity

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.