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A decaying record of recent activity that marks a state or connection as eligible for modification by a later teaching signal. An accumulating value-learning trace is $e_t=\gamma \lambda e_{t-1}+x(s_t)$, giving $\Delta w=\alpha {\delta }_t{e}_t$. Other traces record action-policy gradients or pre/post synaptic events. The update and episode-boundary convention must be specified; an eligibility trace is not automatically identical to the visual activity trace used for trace learning.

Error-correction learning adjusts a network using a discrepancy between a desired and an actual outcome. Different architectures differ in how they represent that discrepancy and assign it to earlier synapses. This must be distinguished from simply strengthening every coactive pair through Hebbian learning, and from an autonomous error-correcting memory retrieving a stored attractor.

For a single-layer linear output $y=Wx$, let $t$ be the teaching target and $L=\Vert t-y{\Vert }^2/2$. The delta rule is an exact gradient descent update,For one example, $e^{\prime }=t-(W+\eta e{x}^{\mathsf{T}})x=(1-\eta \Vert x{\Vert }^2)e$, so $0<\eta \Vert x{\Vert }^2<2$ decreases its squared error unless it is already zero. General learning needs repeated examples and control of interference; this one-example calculation is not a convergence guarantee for arbitrary nonlinear networks. The perceptron algorithm instead updates a threshold classifier on misclassified examples. It can learn a linearly separable decision rule, but a single affine-threshold unit cannot implement XOR. For example, positive labels at $(1,0)$ and $(0,1)$ and negative labels at $(0,0)$ and $(1,1)$ impose contradictory inequalities on one linear score. More layers are needed to learn a suitable feature representation.

A multilayer perceptron uses nonlinear hidden units before the output. For squared error, write $h=\sigma (Ux)$ and $y=\sigma (Vh)$. Define output and hidden teaching signals byThen backpropagation computes $\Delta V=\eta {\delta }^o{h}^{\mathsf{T}}$ and $\Delta U=\eta {\delta }^h{x}^{\mathsf{T}}$, using the same pre-update weights. The chain rule makes these exact negative gradients. Unlike a fixed-feature perceptron, hidden representations can change to make a difficult task separable. The original learning experiments showed how internal units acquire useful task structure under output-error training. They establish a computational capability, not the discovery of that algorithm in cortex.^[ref]

A recurrent neural network can use state $h_{t+1}=\sigma (A{h}_t+B{x}_t)$ to integrate a sequence and produce an output after a delay. Error correction must now assign responsibility to earlier states as well as earlier layers. Applying the chain rule through the unrolled recurrence solves the algorithmic problem, but storing the required history and transporting a late teaching signal to an earlier synapse is biologically demanding. Repeated multiplication by the recurrent Jacobian matrix can also create the vanishing gradient problem or the exploding gradient problem. A local eligibility trace can retain a record of recent synaptic participation for modulation by a later error signal, although that is not generally an exact replacement for the full recurrent gradient.

A biologically motivated comparison is the cerebellum. A broad input representation can be expanded by granule cells; parallel fibers carry the resulting activity to Purkinje cells, while a climbing fiber provides a separate instructive input. Such a circuit resembles a supervised adaptive filter: an active input is modified in a manner depending on a teaching signal, with Purkinje output affecting downstream motor control. The correspondence is a circuit hypothesis, not a claim that the climbing-fiber event equals the signed scalar $t-y$ in every task.

An instructive experiment recorded Purkinje cells in monkeys tracking a moving visual target. A change in target direction supplied a learning instruction, and subsequent eye velocity and simple-spike activity measured trial-over-trial adaptation. Longer complex spikes were associated with larger subsequent simple-spike depression and stronger behavioural learning. Comparing trials matched for preceding simple-spike activity helped rule out that activity alone as the explanation. This connects a separable teaching-related event to later plasticity during behaviour, but extracellular recordings do not directly reveal the numerical update at every parallel fiber synapse, and the finding does not demonstrate multilayer backpropagation.^[ref]

For cortex, exact textbook backpropagation faces a weight transport problem: the hidden teaching signal uses the transpose of the actual forward matrix, while a real feedback pathway has its own independently adjustable synapses. It also needs a way to deliver a target or prediction error, keep learning signals distinct from ordinary activation, and convert them to local plasticity under spiking, delays and sign constraints. The mere presence of anatomical feedback does not establish this computation.

Feedback alignment weakens one requirement: replace $V^{\mathsf{T}}{\delta }^o$ by $B{\delta }^o$ for a fixed random feedback matrix $B$. Simulations showed that changing forward weights can make these approximate teaching directions useful, even without exact reciprocal weights. This is evidence that the weight transport problem need not forbid useful deep error correction; it is not evidence that arbitrary feedback always converges, that the updates remain exact gradient descent, or that this precise mechanism has been recorded in the brain. The work explicitly leaves the physiological implementation and the interaction between feedback activation and learning unresolved.^[ref]

Local supervised error correction has a plausible circuit substrate and direct behavioural-physiological support. Multilayer and recurrent error correction are powerful explanatory possibilities, but their teaching signals, temporal credit assignment and feedback pathways must be established experimentally. Different architectures should be compared by held-out learning behaviour, representational changes and pathway-specific intervention, rather than by labelling all feedback as backpropagation.

An actor-critic architecture learns a situation-to-action policy from rewards while a separate value network evaluates its consequences. It is a concrete reinforcement learning architecture: no supervisor supplies the correct action on each trial. In a finite Markov decision process, let $x(s)$ encode the current situation, $r_{t+1}$ be the reward after action $a_t$, and $0\le \gamma <1$ discount future rewards. The objective is expected return, ${\mathbb{E}}_{\pi }{\sum }_{t\ge 0}{\gamma }^t{r}_{t+1}$. For an episodic undiscounted task, termination can instead make the return finite. State representation matters: if the sensory input does not identify the relevant situation, a memory of previous observations must be added.

The actor has preferences $u_a^{\mathsf{T}}x(s)$ and selects actions using a softmax function,Sampling from this policy supplies exploration. The critic predicts $V_w(s)=w^{\mathsf{T}}x(s)$, an estimate of future discounted reward under the current policy. Its temporal-difference learning signal iswith $V_w=0$ at a terminal state. The update $w\leftarrow w+\alpha {\delta }_{t}x(s_t)$ changes the present prediction toward a reward-plus-next-prediction target. It is a semi-gradient update: it does not differentiate through the bootstrapped target. The temporal-difference principle assigns credit using successive predictions rather than waiting for a complete episode to end.^[ref]

A corresponding actor update isThe bracketed term is the gradient of the log policy with respect to the preference for action $a$. A positive reward prediction error increases the probability of the selected action; a negative one decreases it. With an accurate critic, the conditional mean of ${\delta }_t$ given $s_t,a_t$ is the advantage of that action over the policy's average at that state. This motivates the update, while approximate values, changing policies and limited exploration can compromise learning. It is not a guarantee of the globally best behaviour for every choice of features and learning rates.

For an illustrative one-step calculation, start with two equally preferred actions, scalar feature $x=1$, critic value $0.5$, and a chosen first action followed by reward $1$. Then $\delta =0.5$. With actor learning rate $\beta =0.1$, its two preference changes are $+0.025$ and $-0.025$, so the next probability of the rewarded action is $1/(1+e^{-0.05})\approx 0.5125$. These are chosen model values. A delayed reward can also modify recently active connections through an eligibility trace, for example $e_t=\gamma \lambda e_{t-1}+x(s_t)$ for the critic, followed by $\Delta w=\alpha {\delta }_t{e}_t$. The actor needs its own policy-gradient eligibility. Traces must be reset or otherwise handled at episode boundaries.

The key biological hypothesis is a three-factor plasticity rule: presynaptic activity, postsynaptic or action-selection activity, and a modulatory reward prediction error. Cortical inputs to the striatum can provide situation features, action-related populations can supply the actor, and dopamine can modulate plasticity. This is a functional correspondence, not a one-to-one identification of all actor units with one anatomical compartment. The critic itself must be learned; delivering reward directly to every active connection would not explain prediction-dependent changes.

Recordings in monkeys trained through successive delayed-response tasks provide relevant primary evidence. Phasic responses of putative dopamine neurons to liquid reward were more common during learning than after task performance had become established, while significant stimuli continued to elicit responses. This is compatible with a learning signal sensitive to predictability rather than a permanent readout of reward consumption, but the study also emphasizes attentional and motivational interpretations.^[ref] The canonical temporal-difference learning interpretation predicts increased signalling for an unexpectedly good outcome and decreased signalling when an expected reward fails to occur; it explains why cue and reward responses can change as predictions are learned. This is a quantitative interpretation of physiological evidence, not an assertion that all dopamine activity equals one scalar critic error.^[ref]

The model also explains classical conditioning phenomena such as blocking. If an established cue already predicts the reward, adding another cue leaves little positive prediction error, so the new cue acquires little value. More causally informative experiments in rats stimulated dopamine neurons at reward delivery in blocking and extinction procedures. The stimulation, timed to mimic a positive prediction error, produced lasting increases in cue-elicited reward seeking. This supports a teaching role for phasic dopamine, rather than merely a correlation with successful behaviour. It does not identify every synapse or prove that a complete actor-critic algorithm is the only explanation.^[ref]

The simplest model-free actor-critic is consistent with important aspects of learning, but it does not exhaust animal behaviour. It gradually caches action preferences. It cannot immediately change an unvisited action's value after an outcome is devalued unless the state or reward representation supplies an additional route to that change. In an instrumental conditioning experiment in rats, lever pressing trained on interval schedules became insensitive to sucrose devaluation in sham controls, whereas dorsolateral-striatal lesions preserved sensitivity to devaluation. Consumption controls showed that devaluation itself was effective, and testing in extinction limited new reward learning. This dissociation supports a distinction between habitual and goal-directed control, rather than one universal cached policy.^[ref]

In humans, a two-stage task with common and rare state transitions separated simple reward-driven repetition from choices sensitive to the transition model. Behaviour and striatal prediction-error-related imaging signals showed both model-free reinforcement learning and model-based reinforcement learning influences. The imaging signal is not a direct measurement of individual dopamine spikes, and task performance alone does not uniquely identify a circuit, but it argues against treating every striatal value signal as purely model-free.^[ref]

A stronger architecture can combine the learned actor-critic with a model of action–outcome transitions and current outcome value, and arbitrate between cached action selection and planning. The model's strength is its explicit account of exploration, delayed credit and prediction-dependent plasticity; its limits become visible in devaluation, latent learning and rapid replanning.

Temporal-difference learning uses sampled reward-plus-continuation predictions to approach a Bellman expectation equation. Policy evaluation must be distinguished from finding an optimal policy. Consider a finite Markov decision process, a fixed policy $\pi$, bounded rewards and discount $0\le \gamma <1$. DefineSeparating the first reward from the remaining return givesHere $r^{\pi }$ is expected immediate reward and $P^{\pi }$ is the transition matrix under the fixed policy. In matrix form, $V^{\pi }=(I-\gamma P^{\pi }{)}^{-1}{r}^{\pi }$. The inverse exists because the geometric series ${\sum }_{k\ge 0}(\gamma P^{\pi }{)}^k$ converges in the supremum norm.

Define the Bellman expectation operator $T^{\pi }V=r^{\pi }+\gamma P^{\pi }V$. A stochastic matrix averages component differences, soThe Banach fixed-point theorem gives a unique fixed point and convergence of exact repeated backups. This establishes why the equation has a well-defined solution in the stated discounted setting; it does not require the intelligent agent to know its transition matrix.

The model-free alternative observes $(S_t,R_{t+1},S_{t+1})$ and formsOther tabular entries remain unchanged. Under the same policy and Markov assumptions, conditioning on the current value estimates givesThus the sample error has the correct mean direction for correcting the current Bellman mismatch. The defining idea is bootstrapping: a later prediction supplies part of the earlier prediction's target, so learning need not wait for a final outcome.^[ref]

In tabular temporal-difference policy evaluation, convergence requires appropriate sampling and step sizes, not merely writing the update. Standard sufficient conditions include bounded rewards, infinitely many visits to each relevant state and, along visits to each state, ${\sum }_t{\alpha }_t(s)=\infty$ with ${\sum }_t{\alpha }_t(s{)}^2<\infty$. Diminishing steps allow continuing correction while limiting accumulated sampling noise. Constant steps generally track a noisy estimate instead of converging exactly. The target's dependence on the present values also means that this is not ordinary gradient descent on an independent, fixed supervised label. These tabular guarantees should not be transferred automatically to nonlinear function approximation or off-policy training.

For an exact illustrative problem, alternate deterministically between states $A$ and $B$, receive reward $1$ on leaving $A$ and $0$ on leaving $B$, and take $\gamma =1/2$. ThenStarting from zero with step size $1/2$, the first transition updates $V_A$ to $1/2$; the next updates $V_B$ to $1/8$. At the exact solution, both transition errors are zero. These values are a model calculation, not neural measurements.

This solves evaluation of the specified policy. The optimal Bellman equation instead maximizes over possible actions. An intelligent agent can combine policy evaluation with policy iteration or an actor-critic improvement mechanism. Estimating the value of a poor fixed policy does not by itself make that policy optimal. Episodic tasks use terminal-state value zero; undiscounted continuing tasks require additional conditions or a different formulation.

A proposed neural implementation uses a representation of the situation to drive a value-predicting population, with a reward prediction error modulating recently active synapses. An eligibility trace can preserve a record of prior activity so delayed errors influence earlier states. Temporal representation is indispensable: if the population does not distinguish when a reward is expected, it cannot generate the appropriate omission signal at that time.

Primate recordings during acquisition of successive delayed-response tasks found that dopamine-neuron reward responses were more prominent during learning than after performance was established, while significant cues elicited phasic responses. This supports prediction-sensitive signalling but also allows attentional and motivational contributions.^[ref] The temporal-difference interpretation explains how a reward response can diminish as reward becomes predicted and a response can develop at an informative cue; a worse-than-expected omission gives a negative error. The physiological interpretation is a quantitative hypothesis, not an identification of every dopamine signal with a single critic variable.^[ref]

Causal evidence comes from reward-timed stimulation of dopamine neurons in rat blocking and extinction tasks. Artificial activation, intended to supply a positive prediction-error-like event, increased subsequent cue-driven reward seeking. This strengthens the teaching-signal interpretation beyond response correlation. It does not show that all components of the Bellman computation reside in dopamine neurons or that the entire algorithm is unique.^[ref]