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Brief Notes on Theoretical Neuroscience

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April 14, 2010

Gabriel Kreiman

http://klab.tch.harvard.edu

- Why theoretical neuroscience?
- Single neuron models
- Network models
- Algorithms and methods for data analysis
- A sample of a few computational studies (visual recognition)

This will NOT be an exhaustive presentation of work in Theoretical Neuroscience…

- Abbott and Dayan. Theoretical Neuroscience - Computational and Mathematical Modeling of Neural Systems [2001] (ISBN 0-262-04199-5). MIT Press.
- Koch. Biophysics of computation [1999] (ISBN 0-19-510491-9). Oxford University Press.
- Hertz, Krogh, and Palmer, Introduction to the theory of neural computation. [1991] (ISBN 0-20151560-1). Santa Fe Institute Studies in the Sciences of Complexity.

- Quantitative models force us to think about and formalize hypotheses and assumptions
- Models can integrate and summarize observations across experiments, resolutions and laboratories
- A good model can lead to (non-intuitive) experimental predictions
- A quantitative model, implemented through simulations, can be useful from an engineering viewpoint (e.g. face recognition)
- A model can point to important missing data, critical information and decisive experiments

- No!
- There are plenty of excellent computational papers written by experimentalists…
- A very brief sample:
- Laurent G (2002) Olfactory Network Dynamics and the coding of multidimensional signals. Nature Reviews Neuroscience 3:884-895.- Carandini M, Heeger DJ, Movshon JA (1997) Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17:8621-8644.- Brincat S, Connor C (2006) Dynamic Shape Synthesis in Posterior Inferior Temporal Cortex. Neuron 49:17-24- Blake R (1989) A neural theory of binocular rivalry. Psychological Review 96:145-167.- Hubel, D.H. and T.N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 1962. 160:106-54.

- Scientists always build models (even if the models are not quantitative and are not implemented through computational simulations). There is no such thing as a “model-free” experiment…

A feed-forward model for orientation selectivity in V1

(by no means the only model)

Hubel and Wiesel. J. Physiology (1962)

Douglas and Martin 2004

Felleman and Van Essen 1991

- Why theoretical neuroscience?
- Single neuron models
- Network models
- Algorithms and methods
- A sample of a few computational studies

Filter operations

Integrate-and-fire circuit

Hodgkin-Huxley units

Multi-compartmental models

Biological accuracy

Lack of analytical solutions

Computational complexity

Spines, channels

A central question in Theoretical Neuroscience:

What is the “right” level of abstraction?

- Lapicque 1907
- Below threshold, the voltage is governed by:
- A spike is fired when V(t)>Vthr (and V(t) is reset)
- A refractory period tref is imposed after a spike.
- Simple and fast.
- Does not consider spike-rate adaptation, multiple compartments, sub-ms biophysics, neuronal geometry

Vrest=-65 mV

Vth =-50 mV

Τm = 10 ms

Rm = 10 MΩ

Line = I&F model

Circles = cortex

first 2

spikes

adapted

where:

im = membrane current

V = voltage

L = leak channel

K = potassium channel

Na = sodium channel

g = conductances (e.g. gNa=120 mS/cm2; gK=36 mS/cm2; gL=0.3 mS/cm2)

E = reversal potentials (e.g. ENa=115mV, EK=-12 mV, EL = 10.6 mV)

n, m, h = “gating variables”, n=n(t), m=m(t), h=h(t)

Hodgkin, A. L., and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 500-544.

- Why theoretical neuroscience?
- Single neuron models
- Network models
- Algorithms and methods
- A sample of a few computational studies

From neurons to circuits

Single neurons can perform many interesting and important computations (e.g. Gabbiani et al (2002). Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320-324)

Neurons are not isolated. They are part of circuits. A typical cortical neuron receives input from ~104 other neurons.

It is not always trivial to predict circuit-level properties from single neuron properties. There could be interesting properties emerging at the network level.

Notes:

Connectivity does not need to be all-to-all

There are excitatory neurons and inhibitory neurons (and many types of inhibitory neurons)

Most models assume balance between excitation and inhibition

Most models do not include layers and the anatomical separation of forward and back pathways

There are many more recurrent+feedback connections than feed-forward connections (the opposite is true about models…)

- Time scales > ~ 1 ms
- Analytic calculations in some cases
- Fewer free parameters than spiking models
- Easier/faster to simulate

Is = total synaptic currentN = total number of inputswb = synaptic weightsKs(t) = synaptic kernelub = input firing rates

if

F can be a sigmoid function

Or a threshold linear function:

Imagine that we want to classify the inputs u into two groups “+1” and “-1”

Training examples: {um,vm}

Perceptron learning rule

Linear separability: can attain zero error

Cross-validation: use separate training and test data

There are several more sophisticated learning algorithms

Now imagine that v is a real value (as opposed to binary)

We want to choose the weights so that the output approximates some function h(s)

Move along the gradient of the error between the desired output and the current output

- Why theoretical neuroscience?
- Single neuron models
- Network models
- Algorithms and methods
- A sample of a few computational studies

Different techniques for time-frequency analysis of neural signals (e.g. Pesaran et al 2002, Fries et al 2001)

Spike sorting (e.g. Lewicki 1998, Quian Quiroga et al 2005)

Machine learning approaches to decoding neuronal responses (e.g. Hung et al 2005, Wilson et al 1993, Musallam et al 2004)

Information theory (e.g. Abbott et al 1996, Bialek et al 1991)

Neural coding (e.g. Gabbiani et al 1998, Bialek et al 1991)

Definition of spatio-temporal receptive fields, phenomenological models, measures of neuronal synchrony, spike train statistics

- Why theoretical neuroscience?
- Single neuron models
- Network models
- Algorithms and methods
- A sample of a few computational studies

- s(t) = visual stimulus
- F(t) = linear filter
- “*” = convolution
- g(t)= “generator” potential
- = threshold
- r(t) = firing event (1 when g(t)>)

Keat J, Reinagel P, Reid RC, Meister M (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30:803-817.

h(t)=g(t)+P(t)

P(t)=Bexp(-t/t)

Keat J, Reinagel P, Reid RC, Meister M (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30:803-817.

Two “noise” sources are added to account for trial-to-trial variability:

a(t) = gaussian noise at the generator potential

b(t) = random fluctuations in the feedback potential P(t)

Keat J, Reinagel P, Reid RC, Meister M (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30:803-817.

- http://bluebrain.epfl.ch
- IBM’s Blue gene supercomputer
- “Reverse engineer” the brain in a “biologically accurate” way
- November 2007 milestone: 30 million synapses in “precise” locations to model a neocortical column
- Compartmental simulations for neurons
- Needs another supercomputer for visualization (10,000 neurons, high quality mesh, 1 billion triangles, 100 Gb)

QUESTION: What is the “right” level of abstraction needed to understand the function of cortical circuitry?

An object can cast an infinite number of projections on the retina

Task: Recognize the handwritten “A”

A “brute force” solution:

- Use templates for each letter

- Use multiple scales for each template

- Use multiple positions for each template

- Use multiple rotations for each template

- Etc.

Problems with this approach:

- Large amount of storage for each object

- No extrapolation, no intelligent learning

- Need to learn about each object under each condition

- Some common themes across multiple models:
- Hierarchical structure
- Increased “receptive field” size
- Increased complexity in shape preferences
- Increased invariance

K. Fukushima, Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 1980. 36: 193-202.

Y. LeCun, L. Bottou, Y. Bengioand P. Haffner, Gradient-based learning applied to document recognition. Proc of the IEEE, 1998. 86: 2278-2324.

G. Wallis and E.T. Rolls, Invariant face and object recognition in the visual system. Progress in Neurobiology, 1997. 51: 167-94.

B. Mel, SEEMORE: Combining color, shape and texture histogramming in a neurally inspired approach to visual object recognition. Neural Computation, 1997. 9: 777.

B.A. Olshausen, C.H. Anderson and D.C. Van Essen, A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J Neurosci, 1993. 13: 4700-19.

M. Riesenhuberand T. Poggio, Hierarchical models of object recognition in cortex. Nature Neuroscience, 1999. 2: 1019-1025.

G. Deco and E.T. Rolls, A neurodynamical cortical model of visual attention and invariant object recognition. Vision Res, 2004. 44: 621-42.

P. Foldiak, Learning Invariance from Transformation Sequences. Neural Computation, 1991. 3: 194-200.

Retinotopically arranged connections between layers

Feature extracting “S” cells

C-cells performing a local “OR” operation

Increasing buildup of position tolerance

Unsupervised learning in S layers

Fukushima K. (1980) Neocognitron: a self organizing neural network model for a mechanism fo pattern recognition unaffected by shift in position. Biological Cybernetics 36, 193-202

Biederman (1987) Psychological Review

Prototype

Alignment of 3 points to the prototype (black arrows)

Note: some points may not align (red ellipses)

Ullman (1996) High-level vision

Several computational models for rotation invariance rely on building 3D object models

Here, recognition is based on learning from few perspective views

Generalized radial basis functions

ti = centers

ci = coefficients

G = basis function (e.g. gaussian)

Poggio T, Edelman S (1990) A network that learns to recognize 3D objects. Nature 343:263-266.

Invariance in visual object recognition

Poggio T, Edelman S (1990) A network that learns to recognize 3D objects. Nature 343:263-266.

Wang et al CVPR 2006

- Goal: “generic” object recognition
- Challenge: object transformations, intra-class variability for categorization
- e.g. Caltech 101 dataset, 30-800 exemplars/category

- Learning generative visual models.L. Fei-Fei, R. Fergus, and P. Perona. CVPR 2004Shape Matching and Object Recognition using Low Distortion Correspondence. Alexander C. Berg, Tamara L. Berg, JitendraMalik. CVPR 2005
- The Pyramid Match Kernel:DiscriminativeClassification with Sets of Image Features. K. Graumanand T. Darrell. ICCV) 2005.
- Combining Generative Models and Fisher Kernels Holub, AD. Welling, M. Perona, P. ICCV 2005
- Exploiting Unlabelled Data for Hybrid Object Classification.Holub, AD. Welling, M. Perona, P. NIPS 2005 Workshop in Inter-Class Transfer.
- Object Recognition with Features Inspired by Visual Cortex. T. Serre, L. Wolf and T. Poggio. CVPR 2005.