[Probcogsci] Time correction! Wed (03/04) 4:30 PM, Natural Computation Lab Seminar Room (SSRB floor 2)

[Angela J. Yu]

Sorry, the speaker just let me know that he'll be getting in later  
than expected.

The talk will start at 4:30 PM.

Angela

> A Probabilistic Framework to go from Function to Circuitry: A View  
> from Change Detection
> Nabil Bouaouli
> Group for Neural Theory
> ENS-Collège de France
>
> The brain continuously processes the signals that emerge from the  
> dynamic
> sensory environment. A well admitted view about the strategy applied  
> by the
> nervous system is that it analyses the “sensory scene” as an  
> ensemble of basis
> features which constitute the signal (e.g. colors, orientations,  
> tones).  Each
> feature, then, activates specific sensory neurons which are  
> sensitive to its
> appearance. On the other hand, theses neurons are known to respond  
> more
> strongly to transient than to steady stimuli.  In this work, we  
> hypothesize
> that they are suited to efficiently detect changes in the  
> environment (i.e. the
> appearance of their preferred features) as soon as these changes  
> occur and the
> question we want to answer is how are sensory stimuli detected and  
> coded and
> what are the mechanisms involved  in this process.
> To address this issue, we provide a probabilistic framework of  
> change detection
> that goes from functional level to cellular level. Namely, requiring  
> the
> detection of fast and unpredictable changes in the environment, the  
> model
> predicts the likely sensory circuits needed to achieve this goal.  
> This (micro)
> circuit involves parallel feedforward excitatory and “delayed”  
> inhibitory
> pathways and predicts the short-term temporal dynamics of the synapses
> involved. Moreover, it shows how the biophysical properties of these  
> synapses
> could be shaped by the statistics of the environment. In terms of  
> sensory
> coding, our model exhibits naturally adaptive behavior as a built-in  
> process.
> In response to changes in the level of stimulus fluctuations, the  
> model adapts
> its sensitivity in qualitatively the same way as retinal and LGN  
> cells do.  As
> a key feature of this model is a “bounded” and delayed inhibition  
> that depends
> on the temporal statistics of the environment, we propose that  
> feedforward
> inhibition may play a general role in sensory processing over many  
> timescales
> and argue that the balance between excitation and inhibition may  
> have direct
> implications on the efficiency of early sensory processing.
>