stp-stabilizes-goal-conditioned-dynamics

name	stp-stabilizes-goal-conditioned-dynamics
description	Short-Term Synaptic Plasticity (STP) stabilizes goal-conditioned dynamics in PFC-inspired reservoir model for multistep goal-directed action planning. Preserves action-relevant goal information under noise with 89.2% success rate vs 49.5% without STP. Activation: short-term synaptic plasticity, goal-conditioned dynamics, reservoir computing, PFC model, goal-directed planning, dynamic connectivity, facilitation-dominant STP.
category	neuroscience

Context

The prefrontal cortex (PFC) maintains goal information for action planning, but how recurrent circuits preserve it in an action-usable form over behavioral timescales remains unclear. This paper demonstrates that short-term synaptic plasticity (STP) can stabilize goal information as action-usable, goal-conditioned dynamics through dynamic modulation of goal-dependent effective recurrent connectivity.

Paper: arXiv:2606.03481 (Submitted 2 Jun 2026) Authors: Jin Nakamura, Yuichi Katori Categories: Neurons and Cognition (q-bio.NC), Neural and Evolutionary Computing (cs.NE) Journal: Submitted to Neural Networks (68 pages, 33 figures, 3 tables)

Core Methodology

1. STP-Enhanced Reservoir Model: Incorporate STP into PFC-inspired reservoir computing model with basal-ganglia-inspired temporal-difference readout learning
2. Goal-Conditioned Dynamics: Preserve goal information in action-relevant form during delay periods
3. Facilitation-Dominant STP: Use STP time constants in facilitation-dominant range identified by grid search
4. Effective Connectivity Analysis: Analyze goal-specific patterning of effective connectivity during delay period
5. Dynamic Recurrent Modulation: History-dependent synaptic modulation stabilizes goal representations under noise

Implementation Steps

1. Build Reservoir Model: Create PFC-inspired reservoir computing network with recurrent connections
2. Add STP Dynamics: Implement short-term synaptic plasticity with facilitation and depression mechanisms
3. Temporal-Difference Readout: Implement basal-ganglia-inspired learning for action value estimation
4. Goal Representation: Encode goal identity in reservoir state during delay period
5. Evaluate Noise Robustness: Test with state noise comparing models with and without STP
6. Time-Resolved Decoding: Analyze when and how goal information remains decodable
7. Effective Connectivity Mapping: Measure goal-specific connectivity patterns over time

Key Results

• Success Under Noise: With STP: 91.8% (no noise) → 89.2% (noise); Without STP: 75.8% → 49.5% (paired Cohen's dz=1.31)
• Goal Decodability: Goal identity highly decodable during delay even without STP
• Action-Relevance: STP preserves goal information as action-relevant goal-conditioned dynamics
• Dynamic Connectivity: Delay-period goal-specific patterning increases toward later trial phase with STP
• Time-Invariant vs Dynamic: Without STP, effective connectivity is time-invariant; with STP, it's goal-conditioned and task-state-dependent

Facilitation-Dominant Range

• Grid search identified facilitation-dominant range of STP time constants associated with high success rates
• STP state perturbation controls support online, history-dependent synaptic modulation
• Gain-matched controls argue against simple fixed recurrent-scaling explanation

Pitfalls

• STP Parameter Tuning: STP time constants require grid search for facilitation-dominant range
• Reservoir Initialization: 100 independently generated networks needed for statistical significance
• Delay Duration: STP benefits most evident during longer delay periods with noise
• Goal-Task Interaction: Goal-conditioned patterning depends on both goal and task state
• Temporal-Difference Learning: Basal-ganglia-inspired readout requires proper action-value estimation

Verification

• Compare success rates with vs without STP under noise (target: ~90% vs ~50%)
• Perform time-resolved decoding of goal information across delay period
• Analyze state-space separability between goals
• Measure action-value-difference availability at action opportunities
• Conduct STP-state perturbation and gain-matched controls
• Analyze effective connectivity patterns over time
• Grid search for facilitation-dominant STP time constants

Activation

• short-term synaptic plasticity
• goal-conditioned dynamics
• reservoir computing
• PFC model
• goal-directed planning
• dynamic connectivity
• facilitation-dominant STP
• effective connectivity
• delay period
• noise robustness
• action-relevant representation