Kresimir Josic

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  Measuring and interpreting correlated neuronal responses
  Kresimir Josic

  Populations of neurons jointly drive behavior. Thus, understanding how population activity is coordinated is a key challenge. Novel recording techniques allow for the simultaneous recording from many cells revealing the joint activity of neuronal population during sensory, motor, and cognitive tasks. This has prompted widespread measurement of pairwise correlations. However, the magnitude, the interpretation, and the underlying neural mechanisms of such neural correlations are being vigorously debated. I will start by reviewing our current understanding of the biological mechanisms that control the correlation between the spiking activity of cortical neurons. In particular, I will discuss the potential pitfalls in simple mechanistic explanations of modulations in the coherence in network activity.

  
  

  In the second part of the talk I will discuss the role of correlations in neural coding. I will first examine the role of coupling between the neurons of the Vertical System (VS) in the lobula plate of the fly. These 20 non-spiking neurons code for the azimuth of the axis of rotation of the fly during flight. The electrical coupling between the cells is relatively large, and the activity of VS cells is strongly correlated. I will discuss the potential role this coupling plays in the processing of optical flow information. I will end with a comment on the impact of noise correlation in models used in psychophysics.

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  How good are mathematical models of genetic signaling networks
  Kresimir Josic

  Synthetic biology holds the promise of allowing us to engineer living beings. I will start by reviewing some examples where mathematical models lead to the development of synthetic organisms with particular properties: One such example is a synthetic gene oscillator in Escherichia coli that exhibits robust temperature compensation -- it maintains a constant period over a range of ambient temperatures. A mathematical model predicted and experiments confirmed the particular mechanisms that lead to temperature compensation despite Arrhenius scaling of the biochemical reaction rates.

  
  

  Such successes are encouraging. But how far can our theoretical models take us? I will argue that our models are still fairly coarse, and do not adequately describe all the important properties of genetic signaling networks. For instance, "transcriptional delay" - the delay between the start of protein production and the time a mature protein finds a downstream target - can have a significant impact on the dynamics of gene circuits. Such delay can inhibit transitions between states of bistable genetic networks, as well as destabilize steady states in other networks. I will show how these effects can be described by reduced, non-Markovian models that are quite different from established models. I will also discuss work with experimental collaborators to characterize the distribution of this delay.

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  The structure and mechanisms of state dependent neural correlations
  Kresimir Josic
  Simultaneous recordings from large neural populations are becoming increasingly common. Correlations in neural activity measured in such recordings can reveal important aspects of neural network organization and function. However, estimating and interpreting large correlation matrices is challenging. Moreover, the network mechanisms that modulate these changes are also not fully understood. I will discuss how estimation of correlations can be improved by regularization, i.e. by imposing a structure on the estimate. I will illustrate this approach by analyzing the activity of 150–350 cells in mouse visual cortex. I will show that activity in this network is best explained by a combination of a sparse graph of pairwise partial correlations representing local interactions, and a low-rank component representing common fluctuations and external inputs.
  Correlated activity can also be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. I will review recent theoretical results that identify three separate biophysical mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations, and the transfer function of single neurons. Along with the statistical approaches discussed in the first part of the talk, such mechanistic constraints on the modulation of population activity will be important in analyses of high dimensional neural data.