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News
13.01.2009: TICS accepted a review paper regarding statistical inference and learning in behaviour and neural activity.
21.12.2009: We are organizing a workshop at Cosyne about the hypothesis that neural activity represents samples from the posterior distribution of an internal model of the environment. I'll be posting a web page soon.
10.09.2009: Our paper showing that the visual cortex does not seem to be optimizing for sparseness has been accepted to NIPS. I'm looking forward to discussing this one!
10.09.2009: PLoS Computational Biology published the paper about a structured model of the representation in V1 that I wrote in collaboration with Rich Turner and Maneesh Sahani.

Pietro Berkes

Fiser lab
Brandeis University
Volen Center for Complex Systems 206 / MS 013
415 South Street
Waltham, MA 02454-9110
phone: +1 781 736 3290
email: berkes_AT_brandeis_edu
photo

Curriculum Vitae

I'm a postdoc research fellow at Fiser lab. My main research interest concerns the way the brain forms a high-level representation of the environment from raw sensory input and without supervision. I approach this problem at a computational level in the framework of statistical inference and learning. I like to work from different perspectives, using machine learning models, and analysis of electrophysiological data. My goal is to understand how neuronal populations support these probabilistic computations, what are the principles that guide their adaptation to the statistics of the environment, and how their final representation of the world is organized.

Publications - Software

Upcoming meetings:

(Past meetings)
  1. Cosyne workshop on "The sampling hypothesis: relating neural variability to perception and learning"
  2. Advanced scientific programming in Python, a g-Node summer school

Research projects

  1. No evidence for active sparsification in primary visual cortex
  2. Evidence for statistically optimal inference and learning in the visual cortex
  3. Characterizing neural dependencies with Poisson copula models
  4. On sparsity and overcompleteness in image models
  5. Structured representations in the visual cortex
  6. Relation between Slow Feature Analysis and Independent Component Analysis
  7. Analysis and interpretation of inhomogeneous quadratic forms as receptive fields
  8. Slowness as a computational principle for the visual cortex

Bonus tracks:

  1. Promoting transparency in science
  2. A bibliography of slowness
  3. masterbaboon.com: Artificial Life, Artificial Intelligence, and games



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