CDTM (connection dependent threshold model)

The Connection Dependent Threshold Model (CDTM) is a computational model of decision-making that attempts to explain the neural mechanisms underlying perception, attention, and decision-making. The model was developed by Shadlen and Newsome in 2001 and has since become a prominent framework for understanding how the brain makes decisions.

The CDTM model is based on a simple premise: that neurons in the brain accumulate evidence over time in support of a decision, and that the decision is made when the accumulated evidence reaches a threshold. This threshold is determined by the connection strengths between neurons and is therefore dependent on the history of prior experience.

The model is composed of a network of neurons that are connected by synaptic weights, which determine the strength of the connections between neurons. Each neuron receives input from other neurons in the network, as well as from external stimuli, and computes a weighted sum of these inputs.

The output of each neuron is a sigmoidal function of the weighted sum of its inputs, which represents the probability that the neuron is firing. The sigmoidal function has a steep slope around zero and a shallow slope at the extremes, which allows for graded responses to inputs.

The model assumes that there are two alternative choices available to the organism, and that the task is to determine which of these choices is more likely given the available evidence. The evidence is represented by the firing rates of a population of neurons that are selective for different features of the stimulus. For example, in a visual discrimination task, there may be a population of neurons that respond to the orientation of a visual stimulus.

The CDTM model assumes that the neurons in the network accumulate evidence over time in favor of one of the two choices. The accumulation process is modeled as a random walk, in which the evidence is sampled at discrete time intervals and added to a running total. The rate of accumulation is determined by the firing rates of the input neurons, which reflect the strength of the evidence in favor of each choice.

The decision is made when the accumulated evidence reaches a threshold, which is determined by the synaptic weights between the neurons. The threshold is higher for connections that have been strengthened by prior experience, and lower for connections that have been weakened.

The CDTM model makes several predictions about the behavior of organisms in decision-making tasks. One prediction is that the response time should increase as the strength of the evidence in favor of one of the choices decreases, because it takes longer to accumulate enough evidence to reach the threshold. Another prediction is that the response time should be shorter when the decision is easy, because there is less noise in the evidence and the threshold can be reached more quickly.

The CDTM model has been used to explain a wide range of behavioral phenomena, including reaction time distributions, choice probabilities, and response properties of individual neurons. It has also been extended to account for the effects of attention, reward, and learning on decision-making.

In conclusion, the Connection Dependent Threshold Model (CDTM) is a powerful computational framework for understanding how the brain makes decisions. The model is based on the idea that neurons in the brain accumulate evidence over time in support of a decision, and that the decision is made when the accumulated evidence reaches a threshold. The threshold is determined by the connection strengths between neurons and is therefore dependent on the history of prior experience. The CDTM model has made many successful predictions about the behavior of organisms in decision-making tasks and has been used to explain a wide range of phenomena in perception, attention, and decision-making.