Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis

Area of Science:

• Computational Neuroscience
• Decision Neuroscience
• Cognitive Science

Background:

• The speed-accuracy trade-off (SAT) describes how decision speed and accuracy are inversely related.
• Bounded integration models explain SAT by evidence accumulation to a threshold.
• Neural implementations of decision bounds are debated, with hypotheses involving fixed thresholds or dynamic network changes.

Purpose of the Study:

• To investigate the neural mechanisms underlying the speed-accuracy trade-off (SAT) in decision-making.
• To test the hypothesis that a common input to neural populations controls SAT by modulating firing rate thresholds.
• To determine whether changes in network dynamics or fixed firing rate thresholds underlie SAT.

Main Methods:

• Simulations of a biophysically-based network model for a two-choice decision task.
• Analysis of network dynamics under varying common input strengths.
• Comparison of model predictions with the fixed-threshold hypothesis.

Main Results:

• A common input to the neural network can control the SAT.
• Changes in network dynamics, specifically the effective integration time constant and attractor landscape, were identified as the primary drivers of SAT.
• The SAT is not implemented by changes in the difference between firing rates and a fixed threshold, contrary to previous hypotheses.

Conclusions:

• The speed-accuracy trade-off is primarily governed by alterations in neural network dynamics rather than fixed firing rate thresholds.
• Modulating common input affects decision speed and accuracy by changing the network's integration process.
• Neural implementations of decision bounds may not rely on firing rates per se, suggesting a more dynamic neural basis for decision-making.