Artificial Neural Network. (*.in files ... Lock is the network of neurons, consisting of excitatory and ... Spiking if membrane potential is above threshold (Excitatory.
By Milind Zirpe Project Guides: Dr. Rene Doursat, Dr. Philip Goodman University of Nevada, Reno CS 790R, 2/27/2006
Overview • • • • •
Introduction The Problem Complex System Model Exploring the Model Implementation
Introduction • The overall system: Neo Cortical Simulator (NCS)
Artificial Neural Network (*.in files consist the neural network model)
Brainstem
AIBO
Behavior Information
Motor Signals
High level Information
Sensory Input
Pre- and Post processing
Interaction with environment
The Problem • Modeling Neocortical Locks: – Modeling of Neocortical Locks from the Lock and Key model. – Lock is the network of neurons, consisting of excitatory and inhibitory cells. – Key is the external stimulus, ideally consisting of random audio or video stimulus from the environment. – Objective is to find a lock which resonates quite perfectly with a given key (i.e. has same phase as key, when the key is applied). – Later on, let the network learn based on reward and punishment scheme.
Complex System Model • Elements of the complex system: – Network of neurons and external stimulus (like Poisson train of pulses).
• Behavior rules: – Spiking if membrane potential is above threshold (Excitatory neurons) or De-spiking if same as above (Inhibitory neurons).
• Local interactions: – Flow of charge (chemicals) between neurons through axon and synapse.
Complex System Model • Network interactions: – Axons and Dendrites.
• Hierarchy of levels: – Brain, 3D Network of neurons, Neuron, Soma (body of neuron).
Exploring the Model • Model a network of neurons which will have some inherent behavior (Lock). • Introduce a signal as an external stimulus to the postsynaptic cells (Key). • Idea is to adjust the strength of the synaptic conductance from pre-synaptic cells to the post-synaptic cell to get the lock to resonate with the key.
Exploring the Model • Other parameters to adjust are: – – – – – – –
Number of neurons in the network. The inherent behavior of the neurons. Synaptic connections between the two types of neurons. “Threshold” of the “Compartment”. Spike shape, if needed. “Absolute use” of the synapse in case of learning. Type of learning, duration, FSV (Frequency of Sampling Value), and various other parameters.
Exploring the Model • Results: – Expected result is to obtain reproducible unique response from the lock given a particular key. – Further part of project is to develop a network of neurons which learns as it experiences external stimulus.
Implementation • Implementation modules: – The neural network model (*.in files) generating the behavior of the lock. – Programs in Matlab for analyzing the report files from NCS and for interpreting the results.
Implementation • Software and Languages: – Neo Cortical Simulator developed in GBCL. It accepts the *.in files as an input. – Matlab 7.0, mostly for analyzing the results. – Probably, pre-developed Python scripts for automating the generation of *.in file.
• Environment: – Windows XP and Linux (NCS clusters).
4 Cell Simple Neural Network Internal behavior Sine
Sine 900 out of phase
Result
Result of the previous slide spiking network for 100% strength of Pre-Syn1 and 0% strength of Pre-Syn2 applied to PostSyn1 and vice versa for Post-Syn2.
Result
Phase difference plot for Post-Syn1 and 2 cells over a range of 0-100% strength, with step size of 10% (0.002V).
(or personality) and external stimulus, get some output. (respond or ignore) for ... Synaptic connections between the two types of neurons. â âThresholdâ of the ...
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