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Define behavior in terms of synaptic interactions between identified neurons.
Is this sufficient?

Leech Anatomy and Central Nervous System

Swimming Leech
Cross Section of a Leech
Central Nervous System
Segmental Ganglion - ventral surface

Concept of identified neurons

How Does One Identify Neurons Involved in Swimming?

Traditional Approach: Semi-Intact preparation.

Simultaneous intracellular recording from neurons during 'fictive' behavior.

Result: Static connectionist models

- define the functional limits of the network
- dynamic complexity of network is lacking and unexplained

Organization of the Leech Swim Generating System

Neuronal basis of swimming has progressed to the point where swim initiation following body wall stimulation can be traced, neuron to neuron, from the sensory neurons that perceive the stimulus to motor neurons that produce the swimming movements.

Summary Diagram - connectionist model!!

Five functional classes of neurons (at least) comprise a swim-generating pathway.

Motor Neurons
Oscillator Neurons (Central Pattern Generator)
Swim-Gating Interneurons
Trigger Neurons
Mechanosensory Neurons (P Cell)

What's Missing?? - many things

Mechanism underlying activation of swim-gating and oscillator neurons - Trigger Neurons
Swim variability - Trigger Neurons

What do I mean by swim variability?

Source of Variability

Variable Cord Length Experiment
Stimulation of Cell 204
Spontaneous Swimming

Analysis of Head / Tail Ganglia Control of Swimming

Goal: Understand how neuronal activity propagates through the nervous system to produce a given behavior in response to a specific stimulus

Results from Traditional Approach

Inhibitory Control (Swim-Inactivating System) - Cell SIN1; unknown cells

Cell SIN1: Activity pattern during swimming.
Cell SIN1: terminates swimming

Excitatory Control (Swim-activating System) - Cell Tr1, Cell SE1, unknown cells
Model - Parallel Control Pathways

Swimming depends of the interactions between the swim-activating and inactivating systems, and their respective effects on the segmental swim-generating network.

Goal Achieved? - partially!

Time Series Analysis (Signal Processing): A Computational Approach

use mathematical tools to tell us about the dynamics of the data data = sequence of voltage measurements in time (time series) Goal: Use time series analysis to quantify features of the physical system - leech CNS. Compare results with network model generated from traditional approach.

Experimental Set-Up

1) Is there an information pattern or neuronal firing sequence required for initiating swimming?

Quantify pattern of neuronal activity that predicts swimming.
Is it dependent on the manner by which swimming is elicited? (triggered vs. spontaneous)

2) Does the nervous system have to be in a specific state for swimming to be triggered?

Raw Data - single trials

Cell Tr1 - swim
Cell Tr1 - no swim
Spontaneous Swim

Raw Data - multiple trials

Tr1 Stimulation: Swimming vs. Non-Swimming Trials

Anterior Connectives
Posterior Connectives

Time Series Analysis

Post-Stimulus Time Histogram

Tr1 Stimulation: Swimming vs. Non-Swimming Trials
Triggered vs. Spontaneous Swim Trials


1) Systematic analysis (traditional approach) of leech CNS has resulted in a network model that provides significant insights into the neuronal basis of swimming.

2) Just starting to use time series analysis to characterize and quantify the variability observed in the swim generating network.

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