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MRI brain

Project Overview

  • This project addresses the question of how the brain encodes information, and what information is encoded in the spike train signals.
  • From the spike train analysis project above, we can deduce what the operation principles of the neural network are (using the black-box approach), the next step is to go to the next-level of abstraction, and determine what these signals "mean." Basically, we ask the question of what is represented in the spike train code by a set of neurons.
  • What is important to address is that neurons don't function in isolation, i.e., no single neuron determines the overal function of the system. The output of the central nervous system (CNS) is determined by the collective output of individual neurons. This implies that the function of each neuron is determined by the interrelationships among the neurons within the network. So our objective is to see how the brain encode (and represent) signals not just by a single neurons.
  • Thus, the signal decoding project is to examine what signals (information) are represented/encoded in a network of neuron by its collective propoerties, i.e., by the population dynamics.


  • The question of what signals are being encoded by neurons is actually more evasive than just figuring out what it does. In engineering terms, signal content is relative, i.e., it depends on the "eyes of the beholder." The same signal can be interpreted differently by different observers. What is that? Because the signal content is dependent on both the encoder and the decoder. That is to say, a signal is only meaningful if the encoder and decoder agree on what the signal representation is. If they agree on a common scheme, the signal can be decoded by the decoded; if not, the signal is "meaningless." This is precisely what signal encryption is all about. When a signal is encoded in a form usign a scheme that is unknown to the decoder, the signal is lost (in garbage). To the decoder, the signal is random. But to the encoder, the signal is not random, and is highly meaningful.
  • So, the question becomes addressing the signal relevant to the encoder and decoder. In other words, a signal cannot be considered in isolation independent of the encoder and decoder. What is interesting is that the same signal can be interpreted differently depending on the decoder. In other words, the same signal can have multiple representations, and these multiple interpretations can be extracted by different decoders. This is what engineers called "multiplexing." A multiplexed signal actually convey multiple meanings – it is all up to the decoders to extract the multiple representations within the same signal. This is a very economical and efficent way to represent signals.
  • So the task for this project is to address the encoder and decoder functions so that the meaning (and representation) of the signals embedded in the spike trains can be extracted/decoded.

Research Objectives

  • To decode the signals represented by the spike train based on the relationship between the encoded information and the decoded output in a network of neurons. The neural signals can be encoded by a set of neurons in a network, and different aspects of the signals will then be decoded/extracted by other neurons. This is essentially how the brain processes information – by extracting the different components of the original signals that is meaningful to the subsequent stages of analysis.
  • For instance, the sensory signal of limb position can be decomposed/extracted into displacement (=x), velocity (=dx/dt), acceleration (=d2x/dt2) and jerk ((=d3x/dt3) for subsequent analysis of the components of the movement.
  • That is to say, we need to identify the "meaning" of the signal within context (i.e., relevant to the encoder and decoder). Most often, this means relevant to the physiological context or behavioral context of the signal being processed.

Specific Goals

  • Given a set of spike trains, identify what these signals represent in relation to what is encoded and what is decoded. It is a question is signal representation as well as a question of signal re-representation.

The Challenge

  • Find the representation of pulse-coded timing signal such that the information is embedded in the timing of these signals.

The Solutions

  • See publication: Zouridakis, G. and Tam, D. C. (2000) Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering. Computer Methods and Programs in Biomedicine. 61: 91-98.
  • See publication: Zouridakis, G. and Tam, D. C. (1997) Multi-unit spike discrimination using wavelet transforms. Computers in Biology and Medicine. 27: 9-18.
  • See publication: Zouridakis, G. and Tam, D. C. (1995) Recognition and classification of multineuron activity using a fuzzy-clustering approach. In: Computational Medicine, Public Health and Biotechnology: Building a Man in the Machine. (M. Witten and D. J. Vincent, eds.) Mathematical Biology and Medicine, Vol. 5, pp. 932-947.
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