Design event-based behavior paradigms and track training sequences

• Event-driven behavioral states
• Integrated control of the training sequence with behavioral trials and blocks
• Blocked-trials to speed up animal training and better organize experimental paradigms
• Built-in metrics to track animal parameters
  (e.g. number of lever presses, rewards, nose pokes, and punishments)

Pynapse simplifies the design of behavioral control sequences — just right-click to autocomplete states and methods for defined parameters (such as rewards, lever presses, and nose pokes), then select what happens.

Control event parameters (such as duration of nose poke or number of lever presses) that trigger the next state. The built-in metrics track these parameters to deliver a reward (like a pellet or sucrose) or end a trial with no reward or a punishment. Use our online metric analysis to track the animal’s learning.

iCon Integration with Pynapse

Pynapse was designed to integrate seamlessly with the iCon – TDT’s behavioral control interface.

Information from the iCon (inputs, outputs, timers, etc.) is extracted into the Pynapse coding environment. Encoded states will then change based on subject inputs via the iCon, other event outcomes, or timers.

Integrated API with a Python coding environment

Pynapse is an event-based Python programming environment. When coding in Pynapse, there is a built-in autocomplete which provides easy access to all state and method calls and minimizes the need for detailed knowledge of Python coding. Complete the code with a few if-then statements and some math, and that’s all you need.

Direct access to Synapse inputs and outputs, such as triggers or Gizmo parameters, allows for closed-loop control on the order of tens of milliseconds. Use Pynapse to take slow events (such as averaging evoked responses or processing neural firing patterns) and trigger a new stimulus, whether it be visual, auditory, or electrical.

Pynapse accesses Synapse data, such as an evoked response, and generates a running average of the acquisition for real-time visualization of visual or electrically evoked potentials. Users can access neural firing patterns, run cross correlations, and then change on-the-fly stimulation patterns for the next trial.

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