Attention
2.3.2006Peggy Seriès Rao (2005): Bayesian inference and attentional modulation in the visual cortex [pdf]
Audition
13.5.2004Misha Ahrens Miller et al. (2001): Functional Convergence of Response Properties in the Auditory Thalamocortical System [pdf] See also Reid RC and Alonso JM (1995): Specificity of monosynaptic connections from thalamus to visual cortex. Nature 378:281-284. [pdf]
22.4.2005Misha Ahrens Fishbach, Nelken and Yeshurun (2001): Auditory edge detection: a neural model for physiological and psychoacoustical responses to amplitude transients. [pdf]
Decision theory
29.6.2005Nathaniel Daw Nathaniel will present the very recent Science paper by Sugrue et al. (2004): Matching behaviour and the representation of value in the posterior parietal cortex.
27.4.2004Boris Gutkin Boris will present Barraclough et al (2004): Prefrontal cortex and decision making in a mixed-strategy game. There is also a news & views by M. Platt on this paper.
20.4.2004Quentin Huys Quentin will present Ratcliff and Smith (2004): A comparison of sequential sampling models for two-choice reaction time. In case there is time, I'll also talk about similar work they did with monkey superior colliculus: Ratcliff et al. (2003): A comparison of macaque behaviour and superior colliculus neuronal activity to predictions from models of two-choise decisions. I'll mainly focus on the first one though. Here are the slides from the talk.
13.4.2005Peter Dayan Peter Dayan will present Platt and Glimcher (1999): Neural correlates of decision variables in parietal cortex.
6.4.2005Angela Yu Angela will present a paper on decision theory and diffusion models. She suggest reading Smith and Ratcliff (2004): Psychology and neurobiology of simple decisions.
16.3.2005Máté Lengyel, Nathaniel Daw Máté will first present the two chaptes on decision theory in E. T. Jaynes' book Probability Theory: The logic of Science (chapters 13 and 14) and then Nathaniel will present Gold and Shadlen 2003: Banburismus in the Brain
Dynamics
21.10.05Máté Lengyel Máté will talk about phase plane reductions of the HH equations.A good introduction to phase plane methods is chapter 3 of Gerstner and Kistler: Spiking Neuron Models.
21.10.05 Máté will go on to phase response curves. Main reading: chapter 10 of Eugene Izhikevich's new book "Dynamical Systems in Neuroscience".
24.2-9.3.Peter Latham "What I'm going to do in the first hour is derive the Fokker Planck equation for several different systems, and then show one way to solve it. The derivation for a system driven by white noise will follow the writeup ( -except that I'll do it in 1-D first)." The second week Peter talked about their connections to HMM's, their applications to spiking LIF neurons and the third week on their solutions with boundary conditions.
Hippocampus
27.4.06Máté Lengyel

I'm going to present a couple of papers that try to get to the bottom of differences between CA3 and CA1 by recording (or gene-imaging) from behaving animals (rats, that is). It's an especially interesting issue in the light of strong theoretical predictions about what CA3 should do, and some maybe less well-defined ideas about the possible role of CA1. The papers presented will include:

Vazdarjanova A, Guzowski JF. Differences in hippocampal neuronal population responses to modifications of an environmental context: evidence for distinct, yet complementary, functions of CA3 and CA1 ensembles. J Neurosci 24(29):6489-96, 2004. [doi / pdf]

Leutgeb S, Leutgeb JK, Treves A, Moser MB, Moser EI. Distinct ensemble codes in hippocampal areas CA3 and CA1. Science 305(5688):1295-8, 2004. [doi / pdf]

Lee I, Yoganarasimha D, Rao G, Knierim JJ. Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature 430(6998):456-9, 2004. [doi / pdf]

Althought it's already too much for a session, chances are that I'll also say something about this paper:

Lee I, Rao G, Knierim JJ. A double dissociation between hippocampal subfields: differential time course of CA3 and CA1 place cells for processing changed environments. Neuron 42(5):803-15, 2004. [doi / pdf]

Learning
23.2.2006Quentin Huys Quentin went over a paper that uses a very similar technique to this one to estimate learning curves from success / failure observations in learning experiments: Smith et al. (2005): Analysis and design of behavioral experiments to characterize population learning [pdf] and also Smith et al. (2004): Dynamic analysis of learning in behavioral experiments [pdf].
fMRI methods
18.5.2006Nathaniel Daw

K.J. Friston and W.D. Penny. Posterior probability maps and SPMs. NeuroImage, 19(3):1240-1249, 2003. [pdf]

which looks like it basically summarizes this longer one: K.J. Friston, W.D. Penny, C. Phillips, S.J. Kiebel, G. Hinton, and J. Ashburner. Classical and Bayesian Inference in Neuroimaging: Theory. NeuroImage, 16:465-483, 2002. [pdf]

And he might possibly push forward to this more recent application: W.D. Penny, N. Trujillo-Barreto, and K.J. Friston. Bayesian fMRI time series analysis with spatial priors. NeuroImage, 24(2):350-362, 2005 [pdf]

Natural statistics
5.5.2005Richard Turner Wainwright and Simoncelli (2000): Scale Mixtures of Gaussians and the Statistics of Natural Images [pdf] or [ps.gz]   Rich's slides [pdf]
29.4.2005Richard Turner Schwartz and Simoncelli (2001): Natural signal statistics and sensory gain control [pdf]   Rich's slides [pdf]
2.3.2006Richard Turner Richard Turner will review Lewicki and Karklin's Gaussian scale mixtures story, concentrating on their paper at NIPS this year: First I'll fill in some background to their work: ICA, and the tacit assumptions which underlie people's pre-occupation with bow-ties. Then I'll present their model and relate it to Simon Osindero and Hinton's work, and Odelia's stuff, as well as the model's we are interested in. We'll then cover the various forms of (hacky) approximate learning in their model, which have evolved over the past 3yrs. Finally we'll look at their results and draw comparisons with experimental work.

Rich prepared some very helpful slides which are here

The main reference is:

Y. Karklin and M. S. Lewicki (2006): Is Early Vision Optimized for Extracting Higher-order Dependencies? NIPS 18.[pdf]

With additional material in:

Y. Karklin and M. S. Lewicki (2005): A hierarchical Bayesian model for learning non-linear statistical regularities in non-stationary natural signals, Neural Computation, 17 (2): 397-423. [pdf]

Y. Karklin and M. S. Lewicki (2003): Learning higher-order structures in natural images, Network: Computation in Neural Systems, 14: 483-499. [pdf]
Neural coding
30.11.2004Quentin Huys Bethge et al. (2004): Optimal short-term population coding: when Fisher information fails.
22.10.2004Peter Latham Peter L and Quentin will present: Brunel and Nadal (1998): Mutual information, Fisher information and population coding.
3&17.2.2005Liam Paninski Paninski et al. 2004: Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. submitted to Neural Computation pdf and
Simoncelli et al. 2003: Characterization of neural responses with stochastic stimuli. pdf
I'll give a brief overview of some recent work on estimating models of neural encoding. The framework is likelihood-based and can be cast elegantly in continuous time, allowing the application of methods from the theory of point processes. Time permitting, I'll show some preliminary applications to real physiological data.
"I'll continue last meeting's overview on estimating models of neural encoding. I'll start by showing more applications to real data (retinal light responses and in vitro cortical current responses), and then finish by giving some of the mathematical details behind the concepts I discussed last time."
26.5.2005Quentin Huys Series of papers by Schultz, Panzeri, Treves and Rolls on the information present in spike trains in short time windows. The recommended (short) papers to read are:
  • Panzeri, Schultz, Treves and Rolls (1999): Correlations and the encoding of information in the nervous system. [pdf]
  • Bezzi, Diamond and Treves (2002): Redundancy and synergy arising from pairwise correlations in neuronal ensembles [pdf]
However, we will also discuss bits from these (long) papers which contain more details:
  • Panzeri, Treves, Schultz and Rolls (1999): On decoding the responses of a population of neurons from short time windows. [pdf]
  • Panzeri and Schultz (2001): A unified approach to the study of temporal correlational and rate coding. [pdf]
19.1.2006Anne Hsu Brown, Joseph and Stopfer (2005): Encoding a temporally structured stimulus with a temporally structured neural representation [pdf]
26.1.2006Yasser Roudi Yasser will present Wu and Amari (2005): Computing with Continuous Attractors: Stability and Online Aspects [ingenta]
16.2.2006Quentin Huys Smith and Brown (2003): Estimating a state-space model from point-process observations [pdf]. See also here
4.5.2006Louise Whiteley Jazayeri, M., & Movshon, J.A. (2006). Optimal representation of sensory information by neural populations. Nature Neuroscience, 9(5), 690-696 (plus supplementary methods) [pdf / doi]
Other
11.5.2004Tom Bennett Tom will present Liu (2004): Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites.
21.6.2005Peter Dayan The editor of Nature reviews neuroscience has asked Peter D three simple (:-)) questions:
  • What are the current 'hot topics' in systems/computational neuorscience research?
  • Which recent advances or emerging trends have attracted your attention?
  • Are there any particular topics -- areas of research or issues of current interest -- that you feel we should cover in Nature Reviews Neuroscience?
We'd like to have a discussion about these questions, which Peter will moderate. Please spend a few minutes on them before Thursday. You might want to have a peek at last weeks' issue of Science
Prefrontal
12.1.2006Peter Dayan Peter Dayan will present: Egner and Hirsch (2005): Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information [pdf]
20.4.2006Kai Krüger Rougier et al. (2005): Prefrontal cortex and flexible cognitive control: Rules without symbols. [pdf]
Reinforcement learning
6.4.2006Misha Ahrens Baker, Tenenbaum and Saxe (2006): Bayesian models of human action understanding [pdf] and quickly go over Ng and Russell (2000): Algorithms for Inverse Reinforcement Learning [pdf]
Sensory plasticity
25.5.2005Maneesh Sahani Maneesh will present Recanzone, Schreiner and Merzenich (1993): Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys.
15.6.2005Máté Lengyel Máté Lengyel will present Kentros et al. 2004: Increased attention to spatial context increases both place field stability and spatial memory.
22.6.2004Sophie Deneve Sophie Deneve will present two papers from the Merzenich lab: Kilgard and Merzenich 1998: Cortical map reorganization enabled by nucleus basalis activity and Bao et al. 2001: Cortical remodelling induced by activity of VTA dopamine neurons.
Vision / Recognition
30.11.2005Pietro Berkes Pietro will present: Grimes and Rao (2005): Bilinear sparse coding for invariant vision [pdf]
9.12.2005Jörg Lücke Correspondence Based Visual Object Recognition and Its Neural Implementations

Following suggestions from a group of the Gatsby people here at the moment, Jörg will talk about correspondence based isual object recognition systems and competing feature based recognition systems. Advantages and disadvantages of the two approaches will be compared and neural implementations will be discussed. Simulations will illustrate the functioning of correspondence based recognition.

Anderson, Van Essen and Olshausen (2005): Directed Visual Attention and the Dynamic Control of Information Flow in Neurobiology of Attention [pdf]

Zhu and von der Malsburg (2004): Maplets for correspondence-based object recognition [pdf]

Olshausen, Anderson and, Van Essen (1993): A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information [pdf]

Anderson and van Essen (1987): Shifter circuits: a computational strategy for dynamic aspects of visual processing; PNAS

2-9.2.2006Jonathan Pillow Hinton, Osindero and Teh (2006): A fast learning algorithm for deep belief nets [pdf] Ricardo pointed out that this work is related to dependency networks (Heckerman et al. (2000): Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization. [pdf]
13.4.2006Reza Moazzezi Felsen et al. (2002): Dynamic Modification of Cortical Orientation Tuning Mediated by Recurrent Connections [pdf]