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EvoSpikeNet — Neuroscience Brain Simulation Brief

[!NOTE] For the latest implementation status, please refer to Functional Implementation Status (Remaining Functionality).

Summary: This brief is a concise summary of all the biomimetic functions, terminology, and cortical correspondence described in docs/BIOMIMETIC_IMPLIMENTATION_PLAN.md along with diagrams (block diagrams, data diagrams, and information transmission paths). Used for design review and summary distribution.

1. List of key terms and functions (excerpt)

  • Intention / Goal Representation (IntentionModule) — PFC/ACC
  • Neuromodulator Gate / PlasticityGate (modulatory.py, neuromodulators.py) — DA/ACh/Oxytocin
  • Reward Circuit / TD (VTA / NAcc) (reward_circuit.py)
  • Hippocampal Episodic Buffer & Replay (hippocampal_memory.py, sleep_consolidation.py)
  • Working Memory (GRU wrapper) (working_memory.py)
  • RhythmSync / Band Power / PLV (rhythm_sync.py)
  • Cortical Topology / Column & Layers (cortical_topology.py)
  • Tsodyks‑Markram STP, PV/SST/VIP suppression (synapses.py)
  • MirrorNeuronSystem (mirror_neurons.py)
  • Creativity Engine / NoveltyEvaluator (creativity_engine.py)
  • Default Mode Network (DMN) (dmn.py)
  • DevelopmentalDynamics / CurriculumScheduler (developmental_dynamics.py)
  • Energy Homeostasis (energy_homeostasis.py)

(See docs/BIOMIMETIC_IMPLIMENTATION_PLAN.md / evospikenet/BIOMIMETIC_FEATURE_STATUS.md for complete list)

2. High-level block diagram

flowchart LR
  Sensory["Sensory / TAS Encoding\n(V1/A1) "] --> Percept["Perceptual Modules\n(Visual/Auditory/Spatial)"]
  Percept --> PFC["PFC / IntentionModule\n(goal vectors, routing)"]
  PFC --> Motor["Motor Planner & Efference\n(M1) "]
  Percept --> Memory["Hippocampus / Episodic Buffer"]
  Reward["Reward (VTA/NAcc)\nTD / Dopamine"] --> PFC
  Emotion["Amygdala / Insula\n(valence/arousal)"] --> PFC
  Sleep["Sleep Consolidation\n(prioritized replay)"] --> Memory
  Neuromod["Neuromodulators\n(DA/ACh/Oxytocin)"] --- PFC
  Middleware["Zenoh Pub/Sub + PTP Sync"] --- Percept

3. Data diagram (events and metadata)

graph TD
  S[SENSOR_EVENTS{spike_events\nptp_ts}] --> E[TAS_ENCODE{spike_packets}]
  E --> Z[ZENOH{protobuf+ptp}]
  Z --> V[VISUAL_MODULE{feature_vectors}]
  Z --> A[AUDITORY_MODULE{feature_vectors}]
  V --> P[PFC{route_probs,intentions}]
  P --> H[HIPPOCAMPAL_BUFFER{episodes}]
  H -->|replay| SLEEP[SLEEP_CONSOLIDATION{replay_batches}]
  REW[REWARD{td_error}] --> P
  NEU[NEUROMOD{DA/ACh/OT}] --- P

Data type: - spike_events: Spike train with timestamp (PTP epoch) - spike_packets: Protobuf payloads delivered via Zenoh - feature_vectors: In-module summary (numpy/torch) - episodes: {timestamp, context, reward, embedding}

4. Information transmission channel (concise)

  • Sensory → Encoding (TAS) → Cognitive module (5–15ms) → PFC (about 30ms) → Motor (25ms).
  • PFC receives signals from amygdala and VTA and modulates learning rate through PlasticityGate.
  • During the sleep phase, SleepConsolidation performs a prioritized replay of HippocampalBuffer and performs hippocampus-to-cortex transfer.

5. Cortical region correspondence (function → region → main implementation)

  • Vision (V1–V5, IT): Vision module of evospikenet/biomimetic/*
  • Hearing (A1): evospikenet/biomimetic/sensory_preprocessing.py
  • PFC/ACC: evospikenet/biomimetic/intention_module.py, evospikenet/biomimetic/modulatory.py
  • Hippocampus: evospikenet/biomimetic/hippocampal_memory.py, evospikenet/biomimetic/sleep_consolidation.py
  • Basal ganglia / NAcc: evospikenet/biomimetic/reward_circuit.py
  • Amygdala: evospikenet/biomimetic/emotion_system.py
  • Cerebellum: evospikenet/cerebellum.py (if present)
  • DMN / Temporal association cortex: evospikenet/biomimetic/dmn.py, creativity_engine.py

6. Terminology and references (for design review)

  • ChronoSpikeAttention — time-dependent attention (scoring with time-distance decay)
  • PlasticityGate — Plasticity gate (NeuromodulatorGate.gated_learning_rate())
  • STDP / Meta-STDP — Online synaptic plasticity/meta-learning term
  • SWR (Sharp‑wave ripple) — hippocampal replay high-frequency bursts (100–200 Hz)
  • PLV — Phase Locking Value

Phase A/B completed: All 11 items, including items 1 to 3 below, were completed on 2026-03-06.
Phase D completed: 4 remaining gaps in distributed node integration resolved on 2026-03-11.
Detailed evaluation: docs-dev/biomimetic_integration_evaluation.md v2.0 (8.7/10)

Completed (Phase A/B)

Priority Item Implementation File
A-1 ✅ biomimetic/__init__.py 37 Symbol public API evospikenet/biomimetic/__init__.py
A-2 ✅ BrainSimulationFramework Gadlayer evospikenet/brain_simulation.py
A-3 ✅ STDP ↔ NeuromodulatorGate wiring evospikenet/plasticity/stdp.py
A-4 ✅ SleepConsolidation.offline_consolidation() STDP replay + stats evospikenet/biomimetic/sleep_consolidation.py
B-1 ✅ NeuromodulatorGate ↔ NeuromodulatorRegistry Bridge modulatory.py, neuromodulators.py
B-2 ✅ IzhikevichNeuron backend (NeuralCircuitModeler) evospikenet/biomimetic/neural_circuits.py
B-3 ✅ CorticalTopologyGenerator → BrainRegionIntegrator.add_cortical_topology() cortical_topology.py, brain_simulation.py
B-4 ✅ gammatone anonymous function modification evospikenet/biomimetic/sensory_preprocessing.py
B-5 ✅ EfferenceCopy.adaptive_gain_update() + reset() evospikenet/biomimetic/sensory_motor.py
B-6 ✅ MirrorNeuronSystem._default_classify() + backwards compatible evospikenet/biomimetic/mirror_neurons.py
B-7 ✅ BrainSimulationFramework.run_idle_phase() DMN idle evospikenet/brain_simulation.py

Completed (Phase D — 2026-03-11)

Priority Item Implementation File
D-1 ✅ BrainSimulation(BrainSimulationFramework) alias (DistributedBrainNode ImportError resolved) evospikenet/brain_simulation.py
D-2 ✅ InstantiatedBrain.apply_weight_delta() — STDP delta → nn.Linear weight application evospikenet/genome_to_brain.py
D-3 ✅ DistributedBrainNode.deploy_genome() + genome-driven forward pass evospikenet/distributed_brain_node.py
D-4 ✅ DistributedEvolutionEngine.deploy_to_nodes() evospikenet/distributed_evolution_engine.py

Remaining tasks (Phase C)

  1. HippocampalBuffer.transfer_to_semantic() — hippocampus → semantic memory transfer path implementation
  2. SleepWakeCycleController — wake/sleep timeline control class
  3. NeuromodulatorGate → neuromod REST / Zenoh endpoint published
  4. CorticalTopologyGenerator ↔ HierarchicalRankDistributedBrainArchitecture connection

Legacy recommendations (still valid)

  1. Automatically fill in Implementation Status and Proof (file/test) in each function line of docs/BIOMIMETIC_IMPLIMENTATION_PLAN.md.
  2. Create tests/integration/test_intention_api_smoke.py (integration smoke of intent API).
  3. Output the diagram as SVG and save it to docs/assets/.

Created: This brief has been compiled with full reference to docs/BIOMIMETIC_IMPLIMENTATION_PLAN.md. Please indicate if additional details are required (specific line number links, more detailed glossary, higher resolution figures).

EvoSpikeNet Brain Simulation Overview

Creation date: 2026-01-12 Last updated: 2026-03-11 🎯 Phase D distributed node integration completed (BrainSimulation alias, deploy_genome(), apply_weight_delta(), deploy_to_nodes() implementation completed) Version: 0.5.0 (Phase D Distributed Node Integration) Auth: Masahiro Aoki (Moonlight Technologies Inc.) 　

1. Scope and target

2. Overall system picture

A layered structure that separates roles for each function and quickly reconnects them when necessary.

System architecture overview: - Control Layer: PFC / Q-PFC is responsible for control and self-modulation - Cognitive Layer: Visual, Auditory, Language, Motor Modules, Hybrid RAG, Spiking LM is cognitive processing - Added: Biomimetic modules (emotion/reward/sleep rhythm, mirror neurons, intention/motivation, cortical topology, creativity/DMN/introspection, dynamic goal selection, developmental schedule/curriculum, sensory-motor preprocessing, etc.) - Memory Layer: Episodic Memory, Semantic Memory, Memory Integrator manages memory - Communication Layer: Zenoh Pub/Sub and PTP Synchronization for distributed communication

Connection relations: - Each module → Spiking LM → Hybrid RAG → PFC → Motor - VIS → SPATIAL → PFC (spatial processing integration) - PFC ↔ Episodic/Semantic Memory -Memory Integrator ↔ RAG - All components → Zenoh (communication), Zenoh ↔ PTP (synchronization)

Features: Zenoh's asynchronous Pub/Sub, PTP time alignment, automatic node detection and reconnection, real-time monitoring visible from your browser.

graph TB
    subgraph "Control Layer"
        PFC["PFC / Q-PFC<br/>Control and Self-Modulation"]
    end
    subgraph "Cognitive Layer"
        VIS["Visual Module"]
        AUD["Auditory Module"]
        LANG["Language Module"]
        SPATIAL["Spatial Module"]
        MOTOR["Motor Module"]
        RAG["Hybrid RAG"]
        SLMS["Spiking LM"]
        BM["Biomimetic Modules"]:::biomim
    end
    subgraph "Memory Layer"
        EPI["Episodic Memory"]
        SEM["Semantic Memory"]
        MINT["Memory Integrator"]
    end
    subgraph "Communication Layer"
        ZENOH["Zenoh Pub/Sub"]
        PTP["PTP Synchronization"]
    end

    VIS --> SLMS
    AUD --> SLMS
    LANG --> SLMS
    VIS --> SPATIAL
    SPATIAL --> PFC
    BM --> PFC
    SLMS --> RAG
    RAG --> PFC
    PFC --> MOTOR
    PFC <--> EPI
    PFC <--> SEM
    EPI <--> MINT
    SEM <--> MINT
    MINT --> RAG

    PFC -.-> ZENOH
    VIS -.-> ZENOH
    AUD -.-> ZENOH
    LANG -.-> ZENOH
    SPATIAL -.-> ZENOH
    MOTOR -.-> ZENOH
    EPI -.-> ZENOH
    SEM -.-> ZENOH
    MINT -.-> ZENOH
    ZENOH --- PTP

2.1 Operation flow of the entire system

We illustrate how EvoSpikeNet works using a typical multimodal task (e.g., "Look at an image, explain it, and decide on an action").

Step 1: Sensing and encoding - Get raw data from camera/mic - Convert to spike train using TAS-Encoding (e.g. image edge → spike timing) - Each module (visual, auditory) processes in parallel

Step 2: Cognitive processing - Spiking LM integrates modalities (visual features + linguistic context) - ChronoSpikeAttention considers temporal dependence (e.g. continuity of movement) - Biomimetic module provides emotional/reward signals and rhythm synchronization to influence learning gains and target sequences - RAG searches and completes relevant information from memory

Step 3: Decision Making (PFC) - PFC aggregates the integrated information and calculates route_probs (allocation probability to each module) - Emotions, motivation, and sleep states bias learning and selection gates via the amygdala and nucleus accumbens/VTA. - Measure cognitive entropy (degree of uncertainty) - Q-PFC self-adjusts with quantum modulation (gating changes depending on confidence) - Distribute decision results to all nodes via Zenoh

Step 4: Memory coordination - PFC searches for episodic memories (past similar experiences) - See Semantic Memory (General Knowledge) - MemoryIntegrator integrates both and feeds back to RAG

Step 5: Take action - Planner breaks down tasks (e.g. "grasp an object" → motor command) - Controller issues motor commands - Safety monitoring real-time check

Step 6: Learn/Adapt - Synapse update with STDP/Meta-STDP - Structural optimization using DNA evolution (if necessary) - Store experiences in episodic memory

Overall Features: - Parallel distributed: Each node works independently, linked with Zenoh - Self-optimization: Q-PFC dynamically adjusts according to cognitive load - Continuous learning: Adapt to environmental changes with Meta-STDP - Real Time: Typical latency 50-200ms

2.2 Correspondence with patent (identification number)

  • MT25-EV001: ChronoSpikeAttention (time series causal/exponential decay attention)
  • MT25-EV003: Quantum modulation type PFC feedback loop (cognitive entropy → quantum modulation → self-gate)
  • MT25-EV005: Hierarchical rank-type distributed brain architecture (rank fixed + cognitive complexity routing)
  • MT25-EV009: EvoGenome runtime structure adaptation engine (DNA representation and generational evolution)
  • MT25-EV010: Brain Language Multimodal higher-order representation system (brain language integration)
  • MT25-EV016: Meta-STDP Real machine continuous learning optimization system (adapting STDP with meta-learning)

2.1 Recognition and acceleration of language processing in the brain

  • Brain Language layer: Converts visual, auditory, and tactile signals into "brain language" representations on the spot, which the PFC uses as context for decision-making.
  • Key to speedup: ChronoSpikeAttention calculates attention without destroying time information, and Q-PFC raises or lowers the gate temperature depending on the uncertainty. Narrow down your search carefully for ambiguous input and quickly for clear input.
  • Visualization: Display route_probs, entropy, and modulation_factor in real time, allowing you to intuitively follow changes in confidence.

3. Neural Computation Core

3.1 Spike neuron model

EvoSpikeNet employs three main neuron models that reproduce the behavior of biological neurons.

3.1.1 LIF (Leaky Integrate-and-Fire) Model

The most basic and computationally efficient model. Expresses leakage and integration of membrane potential.

\[\tau_m \frac{dV(t)}{dt} = -(V(t) - V_{rest}) + R I(t), \quad V(t) \ge V_{th} \Rightarrow \text{spike},\; V \leftarrow V_{reset}\]
  • \(\tau_m\): Membrane time constant (typical: 10-20ms). Determines the speed of response to input.
  • \(V_{rest}\): Resting membrane potential (typical value: -70mV).
  • \(V_{th}\): Firing threshold (typical value: -55mV). A spike occurs when this value is exceeded.
  • \(V_{reset}\): Reset potential (typical value: -75mV). The value at which the membrane potential is reset after a spike.
  • \(R\): Membrane resistance. Conversion factor from input current to potential change.
  • \(I(t)\): Input current. Sum of synaptic input and noise.

Applications: Used in visual/auditory encoders and the base layer of spiking transformers. It has low computational cost and is suitable for real-time processing.

3.1.2 Izhikevich model

A model that balances biological plausibility and computational efficiency. A variety of firing patterns can be reproduced.

\[\begin{aligned} \frac{dv}{dt} &= 0.04v^2 + 5v + 140 - u + I,\\ \frac{du}{dt} &= a(bv - u),\\ v \ge 30\,\text{mV} &\Rightarrow \begin{cases} v \leftarrow c,\\ u \leftarrow u + d. \end{cases} \end{aligned}\]
  • \(v\): Membrane potential (spike generation variable)
  • \(u\): Recovery variables (repolarization and homeostasis after spike)
  • \(a\): Recovery time constant (typical value: 0.02). The smaller the number, the slower the recovery.
  • \(b\): Coupling strength between membrane potential and recovery variable (typical value: 0.2)
  • \(c\): Reset potential (typical value: -65mV)
  • \(d\): Recovery variable jump amount (typical value: 2-8)

Reproducible firing patterns: More than 20 types including regular firing (RS), early adaptation (FS), burst firing (IB), and low threshold spike (LTS).

Applications: Used in areas where complex temporal patterns are important, such as PFC and memory integration nodes.

3.1.3 EntangledSynchronyLayer (Quantum inspired type)

It realizes phase coupling between multiple neurons and stabilizes synchronous firing between distributed nodes.

Mathematical basis: $\(\phi_i(t+1) = \phi_i(t) + \omega_i + K \sum_{j \in N(i)} \sin(\phi_j(t) - \phi_i(t))\)$

  • \(\phi_i\): Phase of neuron \(i\)
  • \(\omega_i\): Natural frequency
  • \(K\): Coupling strength (typical value: 0.1-0.5)
  • \(N(i)\): Neighborhood set of neuron \(i\)

Effect: - Spontaneous synchronization formation between distributed nodes - Improved noise immunity (synchronization improves signal-to-noise ratio) - Flexible synchronization group formation according to tasks

Applications: Coordination between PFC and lower modules, integrated processing of long-term memory, temporal binding of multimodal information.

3.2 Spike encoding and attention

3.2.1 Spike encoding method

We implement multiple methods to convert analog signals into spike trains.

TAS-Encoding (Temporal Analog Spike Encoding): Highly efficient encoding that expresses input strength in both firing timing and firing frequency.

\[t_{spike} = t_0 + \tau \cdot \log\left(\frac{I_{th}}{I(t) - I_{th}}\right), \quad \text{if } I(t) > I_{th}\]
  • Strong input → fast spikes + high frequency
  • Weak input → slow spikes + low frequency
  • Typical time window: 10-50ms

Advantages: - Combines the benefits of time accuracy and rate encoding - High gradient stability with backpropagation - Biological plausibility (close to the response pattern of cerebral cortex area V1)

Latency Encoding: Input strength is expressed only by the timing of spike firing. The simplest and most energy efficient.

\[t_{latency} = T_{max} - k \cdot I(t), \quad k = \frac{T_{max}}{I_{max}}\]
  • Strong input → fast spike
  • Weak input → slow spike
  • Typical time window: 5-20ms

Applications: edge devices, ultra-low power mode.

3.2.2 ChronoSpikeAttention Mechanism

An attention mechanism (patent MT25-EV001) that applies exponentially decaying weights to past information while guaranteeing temporal causality.

Math expression: $\(\begin{aligned} M(t,t') &= \exp\left(-\frac{\max(0, t-t')}{\tau}\right)\\ S'(t,t') &= S(t,t') \cdot M(t,t')\\ P(t,t') &= \sigma(S'(t,t'))\\ O(t) &= \sum_{t' \le t} P(t,t') \cdot V(t') \end{aligned}\)$

Features: - Causality guarantee: Future information of \(t' > t\) is completely blocked (\(M(t,t')=0\)) - Exponential Decay: Weight decays according to time distance (\(\tau\): typical value 10-50 steps) - Softmax not required: Reduces calculation amount by approximately 28% with sigmoid function - Affinity with spikes: Even faster with binary approximation using hard sigmoid

Biological correspondence: Corresponds to theta wave phase encoding in the hippocampus and working memory decay in the prefrontal cortex.

3.3 Plasticity and synaptic learning

3.3.1 STDP (Spike-Timing-Dependent Plasticity)

Biologically valid Hebbian learning law. Adjust the weights by the temporal order of the spikes.

\[\Delta w = \begin{cases} A_+ e^{-\Delta t/\tau_+} & (\Delta t > 0, \text{Pre→Post})\\ -A_- e^{\Delta t/\tau_-} & (\Delta t < 0, \text{Post→Pre}) \end{cases}\]

Parameters: - \(A_+\): Maximum amplitude of LTP (long-term potentiation) (typical value: 0.005-0.01) - \(A_-\): Maximum amplitude of LTD (long-term depression) (typical value: 0.00525-0.0105, \(A_+ \times 1.05\)) - \(\tau_+\): LTP time constant (typical value: 20ms) - \(\tau_-\): LTD time constant (typical value: 20ms) - \(\Delta t = t_{post} - t_{pre}\): Post-synaptic and pre-synaptic spike time difference

Learning window characteristics: - \(\Delta t = 0\)~+20ms: Maximum LTP (strengthening of causality) - \(\Delta t = -20\)~0ms: Maximum LTD (suppression of non-causal relationships) - \(|\Delta t| > 50\)ms: Almost no effect

Biological correspondence: Consistent with measured data of hippocampal CA3-CA1 synapses and excitatory synapses in cerebral cortex layer 2/3.

3.3.2 Meta-STDP (Patent MT25-EV016)

Adaptively adjust STDP parameters according to the environment using meta-learning.

Meta objective function: $\(\mathcal{L}_{meta} = \mathcal{L}_{task}(\theta) + \lambda_E \cdot E(\theta) + \lambda_S \cdot \text{Var}(r)\)$

  • \(\mathcal{L}_{task}\): Task performance (accuracy, F1 score, etc.)
  • \(E(\theta)\): Energy consumption (number of spikes × number of synaptic updates)
  • \(\text{Var}(r)\): Variance of firing rate (stability index)
  • \(\theta = \{A_+, A_-, \tau_+, \tau_-\}\): STDP parameters to be optimized

Inner loop (task adaptation): $\(\theta' = \theta - \alpha \nabla_\theta \mathcal{L}_{task}(\theta)\)$

Outer loop (meta update): $\(\theta \leftarrow \theta - \beta \nabla_\theta \sum_{i=1}^N \mathcal{L}_{task}(\theta'_i)\)$

Effect: - Reduced adaptation time to new tasks by 75% - Reduce energy consumption by 40% - Improved long-term stability by 60%

3.3.3 Metaplasticity and homeostasis

Mechanisms that stabilize synaptic weights and firing rates over time.

Weight Clamp: $\(w(t+1) = \text{clip}(w(t) + \Delta w, w_{min}, w_{max})\)$

Typical value: \(w_{min}=0.001\), \(w_{max}=1.0\)

Firing rate homeostasis: $\(\theta_{adaptive}(t) = \theta_{base} + k \cdot (r_{target} - \langle r \rangle_t)\)$

  • \(\theta_{adaptive}\): Adaptive firing threshold
  • \(\theta_{base}\): Base threshold
  • \(r_{target}\): Target firing rate (typical value: 5-15Hz)
  • \(\langle r \rangle_t\): Average firing rate within the time window
  • \(k\): Adaptive gain (typical value: 0.1-0.5)

Effect: Prevents the firing rate from running out of control and silence even after continuous execution for 24 hours or more. Guarantees long-term stability in a distributed environment.

3.4 Learning optimization and hierarchical aggregation

3.4.1 Principle of hierarchical information aggregation

It imitates hierarchical feature extraction similar to the visual cortex of living things (V1 → V2 → V4 → IT).

Role of each layer:

Genetic representation and evolution system for network structures and parameters based on patent MT25-EV009.

3.5.1 Definition of DNA structure

All parameters of the network are expressed as genotypes.

Example of DNA expression:```yaml genome_id: "gen_042_individual_07" generation: 42 fitness: 0.847

structure: neuron_model: "izhikevich" # or "lif", "entangled_synchrony" layer_sizes: [512, 256, 128, 64] connections: - {from: 0, to: 1, type: "full", sparsity: 0.3} - {from: 1, to: 2, type: "conv", kernel: 3}

parameters: neuron: izhikevich: a: 0.02 # recovery time constant b: 0.2 # bond strength c: -65 # Reset potential d: 8 # jump up

encoder: type: "tas" tau: 15.0 # time constant threshold: 0.5

plasticity: stdp: A_plus: 0.008 A_minus: 0.0084 tau_plus: 20.0 tau_minus: 20.0 meta_learning_rate: 0.001

routing: temperature: 1.5 # ChronoSpikeAttentionTemperature entropy_weight: 0.3 decay_tau: 25.0 

#### 3.5.2 Evolutionary optimization process

**Evaluation function (Fitness)**:
$$F = w_1 \cdot \text{Accuracy} - w_2 \cdot \text{Latency} + w_3 \cdot \text{SpikeEfficiency} + w_4 \cdot \text{EntropyStability}$$

Typical weights: $w_1=0.4, w_2=0.2, w_3=0.2, w_4=0.2$

**Calculation of each indicator**:
- **Accuracy**: Task accuracy (0-1)
- **Latency**: Reciprocal of average response time (normalized)
- **SpikeEfficiency**: Effective spike rate = number of information transfer spikes / total number of spikes
- **EntropyStability**: $1 - \text{Var}(H_t) / \langle H_t \rangle^2$

**Genetic manipulation**:

1. **Selection**: Tournament selection
   - Randomly extract $k$ (typical value: 5) from the population
   - Select the highest fitness individual
   - Elite Preservation: Unconditionally inherit the top 2% to the next generation

2. **Crossover**: Two-point crossover
   - Generate two child DNAs from two parent DNAs
   - Crossover rate: 0.7-0.9
   - Structural part and parameter part can be crossed independently

3. **Mutation**: Gaussian mutation
   - Mutation to each parameter with probability $p_{mut}$ (typical value: 0.05-0.15)
   - $\theta' = \theta + \mathcal{N}(0, \sigma^2)$
   - $\sigma$: 5-10% of parameter range

**Generation update algorithm**:

1. Initial population generation (N=50-200 individuals)
2. For gen = 1 to MAX_GEN:
   a. Evaluation of all individuals (parallel execution)

Self-referential cognitive control mechanism of PFC based on patent MT25-EV003.

### 4.1 Measuring cognitive entropy

PFC quantifies its own "hesitation" from the routing probability to lower-level modules.

**Definition of cognitive entropy**:
$$H_t = -\sum_{i=1}^{N_{modules}} p_i(t) \log_2 p_i(t)$$

- **$p_i(t)$**: Probability of routing to module $i$ at time $t$
- **$N_{modules}$**: Number of available modules (typical: 4-12)

**Interpretation of entropy**:
- **$H_t \approx 0$**: Confidence state (high probability concentrated in one module)
  - Example: $[0.95, 0.02, 0.02, 0.01] \Rightarrow H \approx 0.26$ bit
- **$H_t \approx \log_2 N$**: Maximum uncertainty (evenly distributed over all modules)
  - Example: $[0.25, 0.25, 0.25, 0.25] \Rightarrow H = 2.0$ bit (maximum)
- **$0 < H_t < \log_2 N$**: Intermediate state

**Normalized entropy**:
$$\tilde{H}_t = \frac{H_t}{\log_2 N_{modules}} \in [0, 1]$$

### 4.2 Quantum modulation simulator

Mapping cognitive entropy onto quantum circuits and generating modulation coefficients.

**Quantum circuit configuration**:
1. Initial state: $|0\rangle$ (ground state)
2. Rotating gate: $R_y(\theta) = \exp(-i\theta Y/2)$
   $$\theta = \pi \cdot \tilde{H}_t$$
3. Measurement: Measure in Z basis → state $|0\rangle$ or $|1\rangle$

**Measurement probability**:
$$P(|0\rangle) = \cos^2\left(\frac{\theta}{2}\right) = \cos^2\left(\frac{\pi \tilde{H}_t}{2}\right)$$

**Generation of modulation coefficients**:
$$\alpha_t = \begin{cases}
P(|0\rangle) & \text{Deterministic mode}\\
\text{Bernoulli}(P(|0\rangle)) & \text{Stochastic mode}
\end{cases}$$

**Meaning of $\alpha_t$**:
- $\alpha_t \approx 1$: Confidence state → Exploitation mode (strengthens current strategy)
- $\alpha_t \approx 0$: Uncertain state → Exploration mode (trying new strategies)
- $0.3 < \alpha_t < 0.7$: Balanced state

### 4.3 Self-referential feedback

Dynamically adjust the parameters of the PFC itself using the modulation coefficient $\alpha_t$.

**Adjusting the routing temperature**:
$$T_{routing}(t) = T_{base} \cdot (1 + \gamma \cdot (1 - \alpha_t))$$

- **$T_{base}$**: Base temperature (typical value: 1.0-2.0)
- **$\gamma$**: Adjustment gain (typical value: 0.5-1.5)
- **Effect**: Low $\alpha_t$ → Temperature rise → Flattening of softmax → Exploratory

**Modulation of synaptic plasticity**:
$$\eta_{effective}(t) = \eta_{base} \cdot \left(1 + \lambda \cdot (1 - \alpha_t)^2\right)$$

- **$\eta_{base}$**: Base learning rate (typical value: 0.001)
- **$\lambda$**: Plasticity amplification factor (typical value: 2.0-5.0)
- **Effect**: Accelerate learning during times of uncertainty

**Working memory decay rate adjustment**:
$$\tau_{decay}(t) = \tau_{min} + (\tau_{max} - \tau_{min}) \cdot \alpha_t$$

- **$\tau_{min}$**: Minimum retention time (typical value: 10 steps)
- **$\tau_{max}$**: Maximum holding time (typical value: 50 steps)
- **Effect**: Retains memory for a long time when confident, forgets quickly when uncertain

### 4.4 Delivery via Zenoh

Asynchronously distributes PFC decisions to all nodes.

**Delivery topic**: `pfc/{node_id}/decisions`

**Payload structure**:```json
{
  "node_id": "pfc-0",
  "timestamp": 1737270000.123456,
  "route_probs": [0.45, 0.30, 0.15, 0.10],
  "entropy": 1.52,
  "normalized_entropy": 0.76,
  "alpha_t": 0.38,
  "routing_temp": 2.15,
  "modulation_factor": 1.84,
  "working_memory_decay": 25.6,
  "task_context": "multimodal_inference"
}

Subnode reaction: 1. Check the assignment to yourself from route_probs 2. Determine exploration/utilization mode from alpha_t 3. Adjust internal threshold according to routing_temp 4. Scale processing priority with modulation_factor

Delivery delay: Typical 1-3ms (Zenoh Pub/Sub)

4.5 Biological correspondence and validation

Comparison with human cognition research: - PFC uncertainty response: fMRI study (Volz et al., 2005) and correlation coefficient 0.72 - Exploration/exploitation switching: consistent with behavioral economics models (Daw et al., 2006) - Metacognitive instability: +340% compared to conventional AI, close to human level

Clinical applicability: - Modeling decision-making disorders (schizophrenia, ADHD) - Application to rehabilitation system - Simulation of pharmacological effects (relationship between dopamine levels and exploratory nature)

  • Actual measurement results:
  • Converges in 30-50 generations (approximately 8-12 hours @24 node environment)
  • 15-25% performance improvement compared to manual design
  • Generalization performance for unknown tasks +40%

3.5.3 Sharing and Reproducibility

Save Artifact: - DNA (YAML/JSON) - Learned weights (PyTorch checkpoint) - Evaluation log (TensorBoard format) - Execution environment information (Docker image ID, dependencies)

Distribution protocol: 1. Upload DNA to artifact server 2. Notify all nodes via Zenoh 3. Each node downloads as needed 4. Verification hash confirmation 5. Runtime load (switch in <5 seconds)

Reproducibility guaranteed: - DNA hash: SHA256-based unique identifier - Execution trace: all spike timestamps and routing history - Fixed random number seed: Fully reproducible experiment possible

  1. Encoding layer: Conversion to spike features
  2. Application of TAS-Encoding → Spatial sparse expression (activation rate 5-15%)

  3. Dimension reduction: 2.76M → 512 dimensions (approx. 0.02%)

  4. spiking LM layer: Time series context extraction

  5. Apply ChronoSpikeAttention

  6. Selective integration within a time window (typical window: 10-50 steps)

  7. RAG layer: Matching with memory

  8. Episodic/semantic memory retrieval

  9. Keep only the top k items with relevance scores (typical value: k=5-10)

  10. PFC layer: Final decision making

  11. route_probs calculation: allocation probability to each route
  12. Quantify uncertainty with cognitive entropy \(H = -\sum p_i \log p_i\)
  13. Dynamic gating with Q-PFC modulation

Information compression rate: $\(\text{Compression ratio} = \frac{\text{Output dimension}}{\text{Input dimension}} \approx \frac{128}{2.76 \times 10^6} \approx 4.6 \times 10^{-5}\)$

Retains task-related information while compressing information to approximately 1/20,000 times.

3.4.2 Gradient stabilization technology

A group of techniques to overcome the non-differentiability of spiking neurons.

Surrogate Gradient: Calculate the gradient using a smooth approximation function instead of the actual spike function (step function).

\[\begin{aligned} \text{Forward: } &S = \Theta(V - V_{th})\\ \text{Backward: } &\frac{\partial S}{\partial V} \approx \frac{1}{\beta} \cdot \frac{1}{(1 + |\beta(V - V_{th})|)^2} \end{aligned}\]
  • \(\Theta\): Heaviside step function (actual spike)
  • \(\beta\): Slope parameter (typical value: 10-50)

Gradient preservation effect of TAS-Encoding: - Mitigate vanishing gradient by preserving time information - Enables learning on various time scales (short term: several ms, long term: several hundred ms)

Gradient clipping: $\(\nabla_\theta \leftarrow \begin{cases} \nabla_\theta & \text{if } ||\nabla_\theta|| \le \tau_{clip}\\ \frac{\tau_{clip} \cdot \nabla_\theta}{||\nabla_\theta||} & \text{otherwise} \end{cases}\)$

Typical value: \(\tau_{clip} = 1.0\)

3.4.3 Improving efficiency in a distributed environment

Optimization of calculation and communication costs between multiple nodes.

Load balancing strategy: 1. Minimum response time: Monitors the response time of each node and gives priority to the fastest node. 2. Weighted Round Robin: Distribution proportional to node performance 3. Consistent hash: Identical inputs are routed to the same node (improves cache efficiency)

Attention weight scaling: $\(P_{scaled}(t,t') = P(t,t') \cdot \left(1 - \frac{L_{node}}{L_{max}}\right)\)$

  • \(L_{node}\): Current load of target node
  • \(L_{max}\): Load upper threshold

Automatically reduces attention to high-load nodes and distributes it to low-load nodes.

Communication optimization: - Spike compression: send only non-zero spikes (typical compression ratio: 85-95%) - Asynchronous delivery with Zenoh Pub/Sub (latency: 1-5ms) - Timestamp matching with PTP time synchronization (accuracy: <1μs)

3.5 DNA implementation (structural genetic expression)

  • DNA/Genome metadata: Organize settings such as neuron model, encoder type, plasticity rules, learning rate, temperature, etc. as "DNA" in JSON/YAML.
  • Evolution mechanism: Perform selection, crossover, and mutation using latency, spike efficiency, RAG accuracy, and entropy stability as indicators, and search for the optimal configuration through multiple generations.
  • Share and Reproduce: Leave the DNA as an artifact and distribute it via Zenoh to instantly reproduce the same configuration.
  • Patent support: MT25-EV009 (EvoGenome runtime structure adaptation engine)

4. Q-PFC feedback loop (self-modulation)

PFC measures the routing uncertainty (cognitive entropy) and inputs it to the quantum inspired modulation block. The results are redistributed to all nodes via Zenoh.

  • Cognitive entropy (entropy on routing probabilities route_probs): $\(H_t = -\sum_i p_i(t) \log p_i(t)\)$
  • Public telemetry (topic pfc/{node_id}/decisions): route_probs, entropy, alpha_t, routing_temp, modulation_factor.
  • Modulation concepts: QuantumModulationSimulator generates modulation_factor from \(H_t\) and scales PFC gating/temperature (bounding \(m_t = f(H_t)\) to prevent oscillations).

Q-PFC feedback sequence: 1. PFC: Calculate route_probs 2. PFC: Calculate H_t = entropy(route_probs) 3. PFC → Quantum Modulation: Send H_t 4. Quantum Modulation → PFC: Return modulation_factor 5. PFC → Zenoh: Publish {route_probs, H_t, modulation_factor} 6. Zenoh → Downstream Nodes: Broadcast 7. Downstream Nodes → PFC: Spike/ack (if necessary)

sequenceDiagram
    participant PFC as PFC
    participant QM as Quantum Modulation
    participant Zenoh as Zenoh
    participant Nodes as Downstream Nodes

    PFC->>PFC: Calculate route_probs
    PFC->>PFC: H_t = entropy(route_probs)
    PFC->>QM: Send H_t
    QM-->>PFC: modulation_factor
    PFC->>Zenoh: Publish {route_probs, H_t, modulation_factor}
    Zenoh-->>Nodes: Broadcast
    Nodes-->>PFC: Spikes/ack (as needed)

5. Distributed communication and timing

  • Zenoh Pub/Sub: Asynchronous, low-latency routing. Nodes are loosely coupled and dynamically discovered.
  • PTP time synchronization: Make spike timestamps consistent across all nodes.
  • Executive control & recovery: ExecutiveControlEngine provides graceful degradation / retry / failover / replan.

Detailed configuration for 24 nodes: EvoSpikeNet's full brain simulation consists of a hierarchical structure of 24 nodes, with each node responsible for a specialized function.

  • Rank 0: PFC Control

  • PFC-0: Prefrontal Cortex - Central control, routing decisions, Q-PFC feedback

  • Rank 1-9: Visual Processing (visual processing layer)

  • Vision-1: Low-level visual feature extraction (edge detection)
  • Vision-2: Intermediate visual processing (shape recognition)
  • Vision-3: High-level visual integration (object recognition)
  • Vision-4: Visual attention control
  • Vision-5: Visual motor coordination
  • Vision-6: Visual memory coordination
  • Vision-7: Visual predictive processing
  • Vision-8: Visual anomaly detection

  • Vision-9: Visual feedback

  • Rank 10-11: Motor Control (Basic motor control layer)

  • Motor-10: Motor plan

  • Motor-11: Movement execution/feedback

  • Rank 12-15: Spatial Processing (Spatial perception/generation layer) 🎯 Feature 13 implementation completed

  • Spatial-12 (Where): Dorsal parietal lobe pathway - Spatial position/distance recognition, depth estimation, coordinate transformation
  • Spatial-13 (What): Visual cortex/temporal cortex - visual scene generation, object recognition, scene understanding
  • Spatial-14 (Integration): Occipito-Parietal Junction - What-Where integration, world model building, spatial reasoning

  • Spatial-15 (Attention): Fronto-orbital area - Spatial attention control, saccade planning, task-driven attention

  • Rank 16-22: Auditory & Language (Auditory/Language Processing Layer)

  • Auditory-16: Audio feature extraction
  • Language-17: Language understanding
  • Language-18: Language generation
  • Language-19: Semantic analysis
  • Language-20: Contextual processing
  • Language-21: Dialogue management

  • Language-22: Verbal memory consolidation

  • Rank 23-30: Memory & Integration (memory/integration layer)

  • Episodic-23: Episodic memory (chronological experience)
  • Semantic-24: Semantic Memory (Knowledge Base)
  • MemoryIntegrator-25: Memory Integration
  • RAG-26: Retrieval-Augmented Generation (Information Retrieval)
  • RAG-27: Multimodal integration
  • RAG-28: Reasoning support
  • RAG-29: Learning adaptation
  • RAG-30: Whole system integration

Distributed brain simulation configuration diagram:

graph TD
    subgraph "Rank 0: PFC Control"
        PFC0["PFC-0<br/>Prefrontal Cortex<br/>Central Control"]
    end
    subgraph "Rank 1-9: Visual Processing"
        VIS1["Vision-1<br/>Low-level Features"] --> VIS2["Vision-2<br/>Shape Recognition"] --> VIS3["Vision-3<br/>Object Recognition"] --> VIS4["Vision-4<br/>Attention Control"] --> VIS5["Vision-5<br/>Visuo-Motor Coord"] --> VIS6["Vision-6<br/>Memory Link"] --> VIS7["Vision-7<br/>Prediction"] --> VIS8["Vision-8<br/>Anomaly Detection"] --> VIS9["Vision-9<br/>Feedback"]
    end
    subgraph "Rank 10-11: Motor Control"
        MOT10["Motor-10<br/>Planning"] --> MOT11["Motor-11<br/>Execution & Feedback"]
    end
    subgraph "Rank 12-15: Spatial Processing 🎯"
        SPWHERE["Spatial-12 (Where)<br/>Dorsal Parietal<br/>Position & Depth"] --> SPWHAT["Spatial-13 (What)<br/>Visual/Temporal<br/>Scene Generation"] --> SPINT["Spatial-14 (Integ)<br/>Occipitoparietal<br/>What-Where Fusion"] --> SPATT["Spatial-15 (Attention)<br/>Frontal Eye Fields<br/>Saccade Planning"]
    end
    subgraph "Rank 16-22: Auditory & Language"
        AUD16["Auditory-16<br/>Audio Features"] --> LANG17["Language-17<br/>Understanding"] --> LANG18["Language-18<br/>Generation"] --> LANG19["Language-19<br/>Semantics"] --> LANG20["Language-20<br/>Context"] --> LANG21["Language-21<br/>Dialogue"] --> LANG22["Language-22<br/>Integration"]
    end
    subgraph "Rank 23-30: Memory & Integration"
        EPI23["Episodic-23<br/>Episodic Memory"] --> SEM24["Semantic-24<br/>Semantic Memory"] --> MINT25["MemoryIntegrator-25<br/>Integration"] --> RAG26["RAG-26<br/>Retrieval"] --> RAG27["RAG-27<br/>Multimodal"] --> RAG28["RAG-28<br/>Reasoning"] --> RAG29["RAG-29<br/>Adaptation"] --> RAG30["RAG-30<br/>System Integration"]
    end

    PFC0 --> VIS1
    PFC0 --> MOT10
    PFC0 --> SPWHERE
    PFC0 --> AUD16
    PFC0 --> EPI23

    VIS9 --> SPWHERE
    SPATT --> PFC0
    MOT11 --> PFC0
    LANG22 --> PFC0
    RAG30 --> PFC0

5.5 Spatial recognition/generation system: Where-What route integration 🎯

Neuroanatomical basis

EvoSpikeNet's spatial processing nodes (Rank 12-15) implement the two main pathways of visual-spatial processing in the brain:

Where pathway (dorsal pathway): - Anatomical pathway: visual cortex V1 → V2 → V5(MT) → dorsal parietal lobe (LIP, MIP, VIP, 7a) → fronto-orbital area - Functions: Spatial position, object movement, eye movement control, maintenance of spatial working memory - Neuron type: Neurons that code in retinocentric or oculocentric coordinates

What pathway (ventral pathway): - Anatomical pathway: visual cortex V1 → V2 → V4 → IT (inferior temporal cortex) → hippocampal formation → occipito-parietal junction - Functions: object identification, scene understanding, shape recognition, semantic information processing - Neuron type: Neuron that encodes object categories and semantic properties

Integration points and calculations: - Occipito-Parietal Junction (OPA, RSC, TPJ): Integration of What and Where information occurs - Mathematical model: Multimodal connections are expressed as additive or multiplicative integration (see below)

Detailed explanation by node

Rank 12: SpatialWhereNode - Dorsal parietal pathway (Where processing)

Brain area: Dorsal parietal cortex (LIP, MIP, VIP), dorsal part of superior temporal sulcus (STS)

Neurophysiological properties: - Visual field representation: Coded in retinocentric coordinates, gradually converted to oculocentric coordinates - Spatio-temporal dynamics: synaptic weights are remapped in real time according to the animal's eye movements - Phase synchronization: The surrounding neuron population changes the degree of synchronization according to changes in the gaze point (γ band oscillation: 30-100Hz)

Calculation Task:

\[\begin{aligned} \text{Retinal coords:} \quad s_{\text{ret}}(t) &= [x_{\text{image}} - x_{\text{center}}, y_{\text{image}} - y_{\text{center}}]\\ \text{Depth map:} \quad D(x,y) &= F_{\text{CNN}}(I_{\text{RGB}}, I_{\text{stereo}})\\ \text{Allocentric coords:} \quad s_{\text{world}}(t) &= R(\theta_{\text{head}}, \phi_{\text{eye}}) \cdot [s_{\text{ret}}, D(x,y)]^T \end{aligned}\]

Here, \(R\) is the rotation matrix, \(\theta_{\text{head}}, \phi_{\text{eye}}\) are the postures of the head and eyeballs.

Major components: - CoordinateTransformer: Dynamically transforms three coordinate systems (retina center ⇌ eye center ⇌ head center ⇌ world) - DepthEstimationNetwork: Monocular depth estimation (MiDaS/LeReS-like architecture), output: 1×H×W depth map - OpticalFlowNetwork: Motion detection between consecutive frames (optical flow) - SpatialMemoryBuffer: Keeps the spatial history of the past 30 frames (1 second @30FPS)

Zenoh Communication (PubSub): - Publish: spikes/spatial/where/depth (depth map, 30Hz), spikes/spatial/where/coordinates (3D coordinates, 30Hz), spikes/spatial/where/optical_flow (optical flow, 30Hz) - Subscribe: spikes/vision/features (from Vision-9), spikes/pfc/spatial_attention (top-down attention)

Performance Goals: - Processing latency: <50ms (Actual measurement: 47ms) - Thru: 30 fps (720p images) - Spike sparsity: only 5-15% of the input is active

Rank 13: SpatialWhatNode - Visual/temporal cortex (What generation)

Brain area: Upper visual cortex (V1, V2, V4, V5), Inferotemporal cortex (IT), Facial area (FFA)

Neurophysiological properties: - Hierarchical processing: Step-by-step advancement from simple cells in V1 to complex cells in V2, and then to advanced feature neurons in IT - Invariant representation: A representation that is robust to object transformations (scale, rotation) and illumination changes is gradually formed. - Ensemble Coding: Object categories are coded using distributed representations of hundreds to thousands of neurons.

Calculation Task: - Object recognition: Probability estimation of 100+ classes with ResNet/ViT backbone - Scene graph analysis: Extract object relationships in images as structured representations - Visual generation: convert linguistic description or semantic information to 3D voxel grid

\[P(\text{class} | I) = \text{softmax}(W_{\text{classifier}} \cdot f_{\text{backbone}}(I))\]

Here \(f_{\text{backbone}}\) is the trained CNN feature, \(W_{\text{classifier}}\) is the classification layer

Major components: - ObjectRecognitionLayer: Visual backbone (ResNet-50/Vision Transformer) + classification head - SceneGraphGenerator: Object detection + relationship estimation (graph neural network) - SpatialVAEDecoder: Generates 256×256×256 voxel grid from semantic information - TemporalPredictor: Predict the next frame from frame sequence (t+1 frame generation)

Zenoh communication: - Publish: spikes/spatial/what/scene_graph (JSON format, 10Hz), spikes/spatial/what/voxel_grid (3D representation, 10Hz) - Subscribe: spikes/language/spatial_description (language description), spikes/vision/object_embeddings (Vision-9)

Performance Goals: - Processing latency: <30ms (Actual measurement: 28ms) - Object recognition accuracy: 85-92% (ImageNet-100 subset) - Generation quality: Perceptual distance <0.2 (LPIPS)

Rank 14: SpatialIntegrationNode - Occipito-Parietal Junction (What-Where Integration)

Brain Region: Occipito-Parietal Junction (OPA, RSC), Temporoparietal Junction (TPJ), Superior Temporal Sulcus (STS)

Neurophysiological properties: - Multisensory integration: Area where visual, tactile, auditory, and vestibular information is integrated. - Viewpoint-invariant representation: The same scene seen from different viewpoints suggests their interaction through neuron activity (viewpoint conversion mechanism) - World model formation: The world representation in an allocentric (world-centered) coordinate system is maintained throughout the parietal cortex.

Calculation Task: - What-Where combination: Integration of object category (What) and spatial location (Where) - World model update: Build a consistent 3D world representation from past frame integrations - Inference: Determining relationships and graspability between objects

\[\text{FusedRep} = \text{MultiHeadAttn}(Q=\text{what}, K=\text{where}, V=\text{where})\]

Fusion mechanism:

\[W(t) = \text{WorldModel}(D_t, C_1, C_2, \ldots, C_T; \Theta_{\text{world}})\]

where \(D_t\) is the depth map at frame \(t\), \(C\) is the object segmentation, and \(\Theta_{\text{world}}\) is the world model parameter.

Major components: - MultiModalSpatialFusion: Integrate What and Where with 8-headed self-attention - WorldModelIntegrator: Dynamically update 3D world grid from past 30 frame history - SpatialReasoningEngine: Inference from world model (containment, reachability, visibility) - PerspectiveTransformer: Conversion to multiple perspective representation (from self-centered to other's perspective)

Zenoh communication: - Publish: spikes/spatial/integration/world_model (voxel grid, 10Hz), spikes/spatial/integration/reasoning (reasoning result JSON, 10Hz), spikes/spatial/integration/perspective (egocentric view, 30Hz) - Subscribe: spikes/spatial/where/coordinates, spikes/spatial/what/voxel_grid, spikes/vision/semantic_segmentation

Performance Goals: - Processing latency: <50ms (Actual measurement: 48ms) - World model resolution: 256³ voxels @10Hz - Inference accuracy: relationship recognition 90%+ accuracy

Rank 15: SpatialAttentionControlNode - Fronto-orbital area (attentional control/saccade planning)

Brain areas: fronto-orbital field (FEF), anterior supplementary eye field (SEF), dorsal anterior cingulate cortex (dACC)

Neurophysiological properties: - Reward Mediation: Fronto-orbital area neurons show activity encoded in reward amount/reward probability and drive eye movement target selection - Saccade planning: Motor readiness potential appears 50-100ms before the start of eye movement, latency from fixation point to target point is 150-250ms - Bottom-up/top-down integration: Descending inhibitory signals from brain regions and saliency signatures of the gazed object are integrated.

Calculation Task: - Attention weight calculation: Weighted combination of task-driven signals + bottom-up saliency - Focus selection: Select the highest priority processing target from the attention weight distribution - Saccade planning: Generation of eye movement target coordinates, velocity, and latency

Mathematical model of attentional integration:

\[A_{\text{combined}}(x,y,t) = w_{\text{bottom}} \cdot S_{\text{saliency}}(x,y) + w_{\text{top}} \cdot T_{\text{task}}(t)\]
\[\text{Focus} = \arg\max_{(x,y)} A_{\text{combined}}(x,y,t)\]

Here \(w_{\text{bottom}}, w_{\text{top}}\) are dynamically adjustable weights (change depending on task or motivation)

Major components: - SpatialAttentionController: Integration of task-driven and saliency (temperature parameter variable) - FocusSelector: Determine focus position by argmax or sampling of attention weights - SaliencyDetector: Center bias + gradient-based saliency map generation - SaccadePlanner: Conversion from focus position to saccade target (with latency model)

Zenoh communication: - Publish: spikes/spatial/attention/weights (attention map, 30Hz), spikes/spatial/attention/saliency (saliency map, 30Hz), spikes/spatial/attention/saccade (motor command, ~10Hz) - Subscribe: spikes/pfc/spatial_task (task signal), spikes/spatial/integration/world_model

Performance Goals: - Processing latency: <30ms (Actual measurement: 25ms) - Saccade planning accuracy: within ±3 degrees (entire visual field) - Attention shift response time: <100ms

Feature 13 End-to-end behavior of the integration

Typical spatial cognitive tasks (e.g. "Pick up the red mug on the table"):

  1. t=0-10ms (Rank 12 - Where): Camera input → depth estimation + coordinate transformation → target position calculation in allocentric coordinate system (x=0.3m, y=0.5m, z=0.8m)

  2. t=10-25ms (Rank 13 - What): Image segmentation → object recognition (“red cylindrical container” accuracy: 0.92) + scene graph generation (on_table, reachable)

  3. t=25-50ms (Rank 14 - Integration): What-Where fusion → unified expression "red mug on the table" → reachability determination (reachability score: 0.95)

  4. t=50-75ms (Rank 15 - Attention): Task “Grab the mug” → Spatial attention weight calculation → Saccade planning to the mug position (Target: +15° up, +5° right, latency: 200ms)

  5. t=75-120ms (Rank 0 - PFC): Aggregate all results → Distribute motor command to Motor rank (10-11) → Arm system moves to mag position

Total Throughput: End-to-end processing time: 120-150ms (equivalent to the brain's typical cognitive-behavioral cycle)

6. Long-term memory system

  • EpisodicMemoryNode: Stores time series experience and FAISS cosine search (<5ms typical).
  • SemanticMemoryNode: Stores concepts and knowledge with importance.
  • MemoryIntegratorNode: Integrate episodic/semantic results and update index for RAG/PFC.
  • Performance goals: >1000 qps, save <10ms, retrieve <5ms (see docs/DISTRIBUTED_BRAIN_SYSTEM.md).

7. Simulation cycle (concept)

  1. Sensing/Encoding: Camera/Microphone/Environment → Spiking Encoder → Spiking Features.
  2. Cognition: Modality integration with Spiking LM + Hybrid RAG.
  3. Decision Making: PFC integrates route_probs with ChronoSpikeAttention and prepares gates with Q-PFC while referring to Brain Language representation.
  4. Memory Coordination: PFC invokes episodic/semantic retrieval and receives integrated context.
  5. Action: Planners and controllers issue motor commands, safety mechanisms and monitors protect upper limits.
  6. Learn/Adapt: Run STDP/metaplasticity, Flower-based federated updates as needed.

7.1 Detailed movement sequences (e.g. object recognition tasks)

Below is a step-by-step explanation of the internal processing of a typical task of "recognizing and describing objects in images."

Time t=0: Input received - Camera sensor: Get RGB image (640×480) - Visual encoder: convert to spike train with TAS-Encoding - Example: Edge detection → early spike firing - Delivered to visual module via Zenoh

Time t=10ms: Feature extraction - Vision module: Generate feature maps with spiking CNN - Number of spikes: 5-15% of input (sparse) - Output: 512-dimensional vector (with time axis)

Time t=30ms: Modality integration - Spiking LM: convert visual features into linguistic tokens - ChronoSpikeAttention: considers temporal dependence (e.g. movement trajectory) - RAG: Search for similar images of "cat" from memory

Time t=50ms: PFC decision making - PFC: route_probs = [0.7, 0.2, 0.1] (language module priority) - Entropy calculation: H_t = 0.88 bit (medium confidence) - Q-PFC: α_t = 0.65 (utilization mode) - Zenoh distribution: Broadcast decision results to all nodes

Time t=70ms: Language generation - Language module: Generate "This is a cat" with Transformer - Brain Language: Integrating internal representations into higher-order concepts

Time t=100ms: Memory storage - Episodic memory: {time: t, content: "Cat image recognition", context: "Object recognition task"} - Semantic memory: updated importance of "cat" concept

Time t=120ms: Output - UI: Display recognition results - Log: Records all spikes and decision history

Overall cycle time: 120-200ms (equivalent to human reaction time)

7.2 Parallel processing and synchronization

  • Asynchronous processing: Each node operates independently (e.g. visual and auditory in parallel)
  • Synchronization point: When PFC is determined and when Zenoh is distributed
  • Timeout: Default behavior (graceful degradation) when node response is delayed
  • Record: Record spikes/membrane potential/control signals to HDF5 with SimulationRecorder (optional).

8. Measurement and readout

  • Spike metrics: firing rate, raster, burstiness, synchrony (phase coupling of EntangledSynchronyLayer).
  • Control indicators: entropy transition, modulation_factor, routing temperature.
  • Timing: E2E latency, Zenoh per-hop delay, encoder delay.
  • Health: Node heartbeat, selected load balancing strategy, failover events.

9. Experimental example (neuroscientific consistency)

  • Attention modulation: Change the input ambiguity and observe \(H_t\) and modulation_factor; comparison with the PFC gain control hypothesis.
  • Memory Retrieval: Insert episodic traces and measure retrieval delay/accuracy at semantic vs. episodic nodes.
  • Spike time series plasticity: enable STDP in specific layers and assess receptive field drift and stability under homeostasis.
  • Distributed robustness: Stop/delay nodes and observe ExecutiveControl recovery path and behavior degradation.

10. Execution method (concise)

  • Web UI (full stack): Access http://localhost:8050 after sudo ./scripts/run_frontend_cpu.sh or sudo ./scripts/run_frontend_gpu.sh.
  • Distributed brain script: python examples/run_zenoh_distributed_brain.py --node-id pfc-0 --module-type pfc --connect tcp/127.0.0.1:7447 (distributes decisions in Zenoh).
  • Data recording: Obtain spikes/membrane potential with SimulationRecorder following the example in docs/SIMULATION_RECORDING_README.md.

11. Notes for Neuroscience Review

  • Formulas follow general form, parameters can be adjusted in node/configuration files.
  • Time is PTP synchronized and spike times can be compared between nodes.
  • Output can be observed in real time on the UI, and logs are structured JSON and can be analyzed offline.

12. Anticipated Q&A from neuroscientists

Q1. Why did you choose the spiking neuron model? Are conventional artificial neural networks insufficient?

A1: There are three main reasons.

  1. Explicit representation of time information: Because the timing of spikes carries information, processing on the time axis of ``when'' is possible naturally. It is useful for tasks that require millisecond accuracy, such as visual motion detection and audio phoneme boundary detection.

  2. Energy efficiency: Since calculation and communication occur only when a spike occurs, when the activity is sparse (approximately 5-15% of all neurons), power consumption can be reduced to less than 1/8 compared to conventional models. Particularly suitable for continuous operation in edge devices.

  3. Biological Validity: Biological learning rules such as STDP can be directly applied, and neuroscience findings can be directly reflected in implementation. This also serves as a hypothesis testing platform for cognitive neuroscience.

Q2. How do you use the three models LIF, Izhikevich, and EntangledSynchrony?

A2: I use them differently depending on the requirements of the task.

  • LIF: Layer that emphasizes computational cost (visual encoder, first stage processing). Simple and fast. Select when real-time performance is the top priority.

  • Izhikevich: A layer that requires a balance between biological plausibility and computational cost (PFC, memory consolidation). Used in areas where various patterns such as burst firing are thought to contribute to cognitive processing.

  • EntangledSynchrony: A layer where cooperation between distributed nodes is important (multimodal integration, long-term memory retrieval). Information binding is achieved through phase synchronization.

In fact, the DNA evolution process automatically searches for the optimal combination for each task.

Q3. How does the exponential decay of ChronoSpikeAttention correspond biologically?

A3: Addresses multiple neurophysiological phenomena.

  1. Postsynaptic potential decay: EPSP/IPSP decays exponentially (time constant 5-20ms). ChronoSpikeAttention's \(\tau\) imitates this.

  2. Working memory decay: Behavioral experiments have confirmed that the accuracy of working memory in the prefrontal cortex decreases exponentially as retention time increases.

  3. Theta wave phase encoding: It is known that within the theta waves (4-8Hz) of the hippocampus, encoding is known in which the more the phase advances, the more past information is attenuated, and is structurally similar.

Q4. What does cognitive entropy correspond to in terms of neuroscience?

A4: Corresponds to multiple studies on uncertainty representation in PFC.

  • fMRI study: Activity in the dorsolateral prefrontal cortex (DLPFC) correlates with decision uncertainty (Volz et al., 2005; Hsu et al., 2005). Our entropy \(H_t\) models this uncertainty signal.

  • Single-neuron recording: Monkey PFC neurons have increased firing rates when the probability distribution between choices is flat (high entropy) (Kennerley et al., 2011).

  • Computational correspondence: Mathematically equivalent to "epistemic uncertainty" in the exploration/exploitation trade-off of reinforcement learning.

Q5. Does the Q-PFC quantum circuit require a real quantum computer?

A5: No, it can be fully simulated on a classical computer.

  • Current implementation: Single-qubit \(R_y\) gate → Z measurement is realized by several rows of matrix operations (computational cost: several μs).

  • Imitation of quantum behavior: The essence is stochasticity due to the measurement probability \(P(|0\rangle) = \cos^2(\theta/2)\). No true quantum superposition or entanglement required.

  • Future scalability: Once real quantum devices become available, more complex modulation patterns (multi-qubit gates, quantum entanglement) can be implemented, but are not required for current functionality.

Q6. Is it not possible to learn complex tasks using STDP alone?

A6: That's right. Therefore, we have introduced Meta-STDP.

Standard STDP Limitations: - Due to fixed parameters, it is difficult to adapt according to the task - Trade-off between stability and short-term adaptation in long-term learning - Efficiency under energy constraints is unknown

Meta-STDP solution: - Learn STDP parameters itself in outer loop - Integrated optimization of task requirements, energy constraints, and stability - Results: 75% reduction in new task adaptation time, 40% reduction in energy

Use with other learning rules: - Also uses back propagation (Transformer head, etc.) if necessary - Hybrid learning combines the biological validity of STDP with the efficiency of gradient methods

Q7. To what extent does the 24-node configuration correspond to the structure of biological brains?

A7: Abstraction with emphasis on functional correspondence.

Correspondence: - Rank 0 (PFC) → dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC) - Rank 1-9 (visual) → V1, V2, V4, MT, IT hierarchical processing - Rank 10-11 (motor) → primary motor cortex, premotor cortex, cerebellum - Rank 12-15 (spatial cognition) → dorsal parietal lobe (LIP, MIP), occipito-parietal junction (OPA, TPJ), visual cortex, fronto-orbital area - Rank 16-22 (auditory/language) → A1, A2, STG auditory cortex, Broca's area, Wernicke's area

Simplified points: - Subcortical structures such as the basal ganglia and thalamus are not directly modeled, but are implicitly integrated into the PFC modulation mechanism. - Neuromodulators (dopamine, serotonin, etc.) are abstracted as modulation coefficients and metaparameters - Each node is compressed to the equivalent of tens of thousands to tens of thousands of neurons, compared to tens of billions of neurons in a real brain

Future expansion: Designed to allow implementation of more detailed anatomical correspondence (basal ganglia model, thalamic loop, etc.).

Q8. How can spike synchronization be guaranteed in a distributed environment?

A8: Time synchronization is based on PTP (Precision Time Protocol).

Technical details: - Synchronization accuracy: Time error of less than 1μs between nodes - Spike timestamp: 64bit high precision timestamp added to all spikes - Zenoh distribution: Asynchronous Pub/Sub but guarantees causal order with timestamps

Role of EntangledSynchronyLayer: - If PTP synchronization alone is not sufficient (e.g. due to network delay fluctuations), phase coupling will spontaneously restore synchronization - Modeling the phenomenon in which neural populations synchronize through self-organization even in biological brains without accurate time information

Actual performance: - 8-node environment: Spike timing error < 2ms (95%ile) - 24 node environment: Same < 5ms (95%ile) - Sufficient for typical time scales of cognitive tasks (100-500ms)

Q9. How is the distinction between episodic memory and semantic memory realized in implementation?

A9: Distinguished by memory structure and retrieval method.

Episodic Memory (EpisodicMemoryNode): - Structure: Saved as a chronological sequence of events{time: t1, context: C1, content: E1} {time: t2, context: C2, content: E2} ...- Search: Filter by time range or context similarity → FAISS nearest neighbor - Supported: Hippocampal episodic memory (retains "when and where" information)

Semantic Memory (SemanticMemoryNode): - Structure: Saved as a relationship graph between concepts{concept: "cat", related: ["animal", "pet", "mammal"], vector: [0.1, 0.3, ...], importance: 0.8}- Search: Integrated score of vector similarity + graph distance - Correspondence: Semantic memory in the neocortex (general knowledge of "what you know")

Integration (MemoryIntegratorNode): - Receive both search results and integrate temporal context and semantic associations - Example: The recollection "I saw the cat yesterday" is a combination of episodic (yesterday) and semantic (the concept of a cat)

Q10. Can this system be used to test neuroscience hypotheses?

A10: Yes, we designed it with that as one of our main purposes.

Specific verification example:

  1. Computational model of attention:
  2. Manipulating \(\tau\) of ChronoSpikeAttention → How does the attention window affect behavior?

  3. Change PFC routing temperature → Model top-down attention control

  4. Neural basis of metacognition:

  5. Comparing cognitive entropy and human subjective certainty

  6. Make Q-PFC modulation pathological (e.g. abnormally high/low \(\alpha_t\)) to reproduce decision-making impairments

  7. Memory Consolidation:

  8. Manipulate the strength of episodic/semantic memory connections

  9. Simulation of memory consolidation during sleep (system integration theory)

  10. Learning and plasticity:

  11. Relationship between Meta-STDP parameters and learning curve
  12. Modeling of sensitive periods

Advantages: - Parameters can be manipulated freely, allowing experiments that are impossible for humans. - Complete recording and analysis of all neuron activities - Fully guaranteed reproducibility (DNA + random number seed fixed)

Constraints: - It is only a computational model and does not reproduce all biological details. - Positioned as a stage of hypothesis generation and preliminary verification, final verification should be performed through biological experiments

Q11. Are the calculation cost and real-time performance at a practical level?

A11: Depends on the task and hardware, but is sufficient for many practical scenarios.

Benchmark (24 nodes, GPU x 8 environment): - Visual object recognition: 15-30 FPS (real-time video processing possible) - Voice recognition: Latency 50-150ms (interactive dialogue possible) - Multimodal integration: Latency 80-200ms (equivalent to human reaction time)

Scalability: - Fully linear scale: 2x the number of nodes → 2x the processing speed - Confirmed to work with more than 100 nodes

Energy efficiency: - GPU implementation: 60-70% power consumption compared to conventional CNN - Neuromorphic chips (future): Possibility of further 1/100 reduction

Constraints: - Initial learning takes time (DNA evolution: 8-12 hours) - Tasks that require ultra-fast response (<10ms) are currently difficult.

Q12. Is the implementation code publicly available? Is it possible to use it for research?

A12: Yes, it is open source (MIT License) and scheduled to be released at the end of March 2026.

Repository: https://github.com/moonlight-technologies/EvoSpikeNet

License conditions: - Academic research/educational use: Completely free (MIT License) - Personal Project: Free - Commercial use: Requires Enterprise Commercial License (see README for details)

Documentation: - API specification: auto-generated (OpenAPI 3.0, Swagger UI) - Tutorial: Provided in Jupyter Notebook format - Implementation details: comprehensive documentation in docs/ directory

Community: - Questions/Discussions: GitHub Discussions - Bug report: GitHub Issues - Slack channel for developers (invitation only)

Quote: Please cite the following when using in your paper:``` Aoki, M. (2026). EvoSpikeNet: A Hierarchical Distributed Spiking Neural Network with Quantum-Modulated Prefrontal Cortex. [詳細は実際の論文情報に置き換え] 

---

## Advanced brain simulation system

EvoSpikeNet now features a comprehensive brain simulation system that models neural circuits, synaptic plasticity, brain region integration, and behavior generation with biologically realistic dynamics.

### Core components

#### 1. NeuralCircuitModeler
The implementation class name is `NeuralCircuitModeler`. Initialize using the configuration data class `NeuralCircuitConfig`.

```python
# Imports and minimal usage examples tailored to your implementation
from evospikenet.brain_simulation import NeuralCircuitModeler, NeuralCircuitConfig
import numpy as np

# Create configuration
config = NeuralCircuitConfig(
    num_neurons=1000,
    connection_probability=0.1,
    excitatory_ratio=0.8
)

# Initialize modeler
circuit_modeler = NeuralCircuitModeler(config)

# Example of simulating 1 time step
input_current = np.zeros(config.num_neurons)
spikes, membrane = circuit_modeler.simulate_timestep(input_current, timestep=0, dt=1.0)

# Neural circuit setup
simulator.setup_circuit(
    connection_probability=0.1,
    synaptic_weights_init='normal',
    neuron_parameters={
        'resting_potential': -65.0,
        'threshold': -50.0,
        'reset_potential': -70.0
    }
)

# Simulation execution
simulation_time = 1000  # ms
input_stimuli = torch.randn(circuit_size['excitory'], simulation_time)

results = simulator.run_simulation(
    input_stimuli=input_stimuli,
    simulation_time=simulation_time,
    record_spikes=True,
    record_membrane=True
)

# Analysis of results
spike_times = results['spike_times']
membrane_potentials = results['membrane_potentials']
print(f"平均発火率: {simulator.calculate_firing_rate(spike_times):.2f} Hz")

Main features: - Multiple neuron models (LIF, Izhikevich, AdEx) - Realistic synaptic dynamics - Spike timing analysis - Circuit topology modeling

2. Synaptic Plasticity Simulator

A SynapticPlasticitySimulator is implemented to implement STDP, LTP/LTD, and homeostatic plasticity mechanisms.

from evospikenet.brain_simulation import (
  SynapticPlasticitySimulator,
  NeuralCircuitModeler,
  NeuralCircuitConfig
)
import numpy as np

# Create the circuit modeler and then initialize the plasticity simulator
config = NeuralCircuitConfig(num_neurons=1000, connection_probability=0.1)
circuit = NeuralCircuitModeler(config)
plasticity = SynapticPlasticitySimulator(circuit)

# Example of applying STDP
updated_weights = plasticity.apply_plasticity('stdp', learning_rate=0.01)
        'excitatory_inhibitory': {'rule': 'anti_stdp', 'tau_plus': 10.0, 'tau_minus': 10.0},
        'inhibitory_excitatory': {'rule': 'homeostatic', 'target_rate': 5.0}
    }
)

# Applying plasticity during simulation
for t in range(simulation_time):
    # Obtain pre- and postsynaptic spikes
    pre_spikes = circuit.get_presynaptic_spikes(t)
    post_spikes = circuit.get_postsynaptic_spikes(t)

    # Update synapse weights
    weight_updates = plasticity_manager.update_weights(
        pre_spikes=pre_spikes,
        post_spikes=post_spikes,
        current_weights=synapse_weights
    )

    # Applying updates
    synapse_weights += weight_updates

# Analysis of plasticity effects
plasticity_stats = plasticity_manager.get_plasticity_statistics()
print(f"シナプス強化: {plasticity_stats['potentiation_rate']:.2%}")
print(f"恒常性スケーリング: {plasticity_stats['scaling_factor']:.3f}")

Main features: - Multiple plasticity rules - homeostatic regulation - metaplasticity - Weight stabilization

3. BrainRegionIntegrator

Integrates multiple brain regions with hierarchical connectivity and information flow.

<!-- from evospikenet.brain_simulation import BrainRegionIntegrator -->

region_integrator = BrainRegionIntegrator(
    regions_config={
        'visual_cortex': {'neurons': 1000, 'specialization': 'vision'},
        'motor_cortex': {'neurons': 800, 'specialization': 'motor'},
        'prefrontal_cortex': {'neurons': 600, 'specialization': 'executive'},
        'hippocampus': {'neurons': 400, 'specialization': 'memory'}
    }
)

# Setting up inter-region connections
region_integrator.setup_connectivity(
    connections=[
        {'from': 'visual_cortex', 'to': 'prefrontal_cortex', 'strength': 0.8},
        {'from': 'prefrontal_cortex', 'to': 'motor_cortex', 'strength': 0.9},
#### 4. BehavioralOutputGenerator
実装上のクラス名は `BehavioralOutputGenerator` です。神経活動の辞書を渡して行動パラメータを生成します。

```python
from evospikenet.brain_simulation import BehavioralOutputGenerator
import numpy as np

behavioral_generator = BehavioralOutputGenerator()

# Prepare dummy neural activity
neural_activity = {
  'motor': np.zeros(100),
  'visual': np.zeros(100)
}
context = {'task': 'reach'}

output = behavioral_generator.generate_behavior(neural_activity, context)
print(output)
print(f"{interaction['from']} -> {interaction['to']}: {interaction['information_flow']:.3f}") 
**Main features:**
- Multidisciplinary integration
- Hierarchical processing
- Feedback loop
- Information flow analysis



# Example of behavior interpretation
if behavior_output.get('behavior_type') == 'reach':
    # `parameters` include the reached position, speed, etc.
    motor_params = behavior_output.get('parameters', {})
    # Please implement the process to pass to robot control on the user side.
    # robot.execute_motor_parameters(motor_params)```

**主な機能:**
- 運動制御生成
- 意思決定
- 行動シーケンシング
- パフォーマンス評価

#### 5. Neural Dynamics Monitoring
専用クラス `NeuralDynamicsTracker` は実装されていません。リアルタイム監視や解析は、`ValidationFramework` や各種レコーダ（将来的な実装）を組み合わせて行います。

```python
from evospikenet.brain_simulation import ValidationFramework

# Example of history acquisition from validation framework
validation = ValidationFramework()
history = validation.get_validation_history()

# Implement real-time monitoring by combining recorders and statistical processing as necessary```
        activity=current_activity,
        time_point=t,
        context={'stimulus_onset': t == stimulus_time}
    )

# Analysis of tracked dynamics
dynamics_analysis = dynamics_tracker.analyze_dynamics()

# vibration activity
for band, power in dynamics_analysis['oscillations'].items():
    print(f"{band} パワー: {power:.2f}")

# neural avalanche
avalanche_stats = dynamics_analysis['avalanches']
print(f"アバランシェサイズ分布指数: {avalanche_stats['exponent']:.2f}")

# functional connectivity
connectivity_matrix = dynamics_analysis['connectivity']

Main features: - Real-time dynamics tracking - Vibration analysis - Avalanche criticality - Functional connectivity

6. Simulation Composition (Integration Pattern)

This implementation does not provide a single BrainSimulationManager. Instead, configure the simulation by combining multiple components, as shown below.

from evospikenet.brain_simulation import (
  NeuralCircuitModeler, NeuralCircuitConfig,
  SynapticPlasticitySimulator, BrainRegionIntegrator, BrainRegionConfig
)

# Minimum configuration example of circuit modeler + plasticity + area integration
config = NeuralCircuitConfig(num_neurons=1000, connection_probability=0.05)
circuit = NeuralCircuitModeler(config)
plasticity = SynapticPlasticitySimulator(circuit)
integrator = BrainRegionIntegrator()
integrator.add_region(BrainRegionConfig(region_name='prefrontal', num_neurons=500))

# From here, connect each component and proceed with the simulation manually.
region_config={ 'visual_cortex': {'neurons': 2000, 'input_modality': 'vision'}, 'motor_cortex': {'neurons': 1500, 'output_modality': 'motor'}, 'prefrontal_cortex': {'neurons': 1000, 'function': 'executive'}, 'hippocampus': {'neurons': 800, 'function': 'memory'} } )

Execute brain simulation with behavioral output

simulation_results = simulation_manager.run_brain_simulation( input_sequence=experiment_inputs, simulation_duration=10000, # ms record_behavior=True, analysis_intervals=[1000, 5000, 10000] )

Analysis of simulation results

behavioral_analysis = simulation_manager.analyze_behavioral_output( results=simulation_results )

neural_analysis = simulation_manager.analyze_neural_dynamics( results=simulation_results )

Generate simulation report

report = simulation_manager.generate_simulation_report() print(f"Simulation completed: {report['duration']} ms") print(f"Generated behavior: {report['behavior_count']}") print(f"Detected neural oscillations: {len(report['oscillations'])}")```

主な機能: - 統合された脳シミュレーションインターフェース - 多スケール統合 - 包括的な分析 - 行動神経科学研究

Integration example

Cognitive task simulation

単一の BrainSimulationManager は存在しないため、領域ごとのモジュールを組み合わせて認知課題を構成します。先の「Simulation Composition」節を参照してください。

# Example: Create a NeuralCircuitModeler for each region and combine it with a behavior generator and validation framework.
# Implement cognitive tasks. Please manually configure the actual task implementation according to your use case.```

#### Motor learning simulation
運動学習も同様に、既存コンポーネントを組み合わせて実装します。`BrainSimulationManager` が無いため、
領域別の `NeuralCircuitModeler` と可塑性シミュレータを組み合わせて学習ループを構築してください。

```python
# Example: Apply SynapticPlasticitySimulator to motor_circuit and evaluate learning, etc.```
    learning_curve.append(trial_result['error'])

    # Application of motor learning
    motor_sim.update_motor_learning(trial_result)

# Learning analysis
learning_analysis = motor_sim.analyze_motor_learning(learning_curve)
print(f"学習率: {learning_analysis['learning_rate']:.3f}")
print(f"漸近パフォーマンス: {learning_analysis['asymptote']:.2f}")

Configuration options

brain_simulation:
  neural_circuits:
    neuron_model: izhikevich
    circuit_size: 10000
    connectivity:
      type: sparse
      probability: 0.1
      weight_distribution: lognormal

  synaptic_plasticity:
    rules_enabled: [stdp, homeostatic, metaplastic]
    learning_rate: 0.01
    homeostasis:
      target_rate: 10.0
      time_constant: 10000

  brain_regions:
    regions:
      - name: visual_cortex
        neurons: 2000
        function: sensory
      - name: motor_cortex
        neurons: 1500
        function: motor
      - name: prefrontal_cortex
        neurons: 1000
        function: executive

  behavioral_generation:
    motor_control:
      type: population_coding
      degrees_of_freedom: 7
    decision_making:
      type: evidence_accumulation
      options: 4

  neural_dynamics:
    tracking:
      sampling_rate: 1000
      analysis_window: 100
    metrics: [firing_rate, synchrony, oscillations, avalanches]

  integration:
    feedback_loops: true
    cross_modal: true
    hierarchical: true

Sensor connection plugin

Distributed brain simulation makes it easier to incorporate raw data from external sensors, We provide a common interface. LiDAR, stereo/infrared camera, Environmental sensors (temperature, humidity, wind speed, CO₂, etc.), millimeter wave radar, etc. It can be added in the form of a plugin and fed to the brain model through nodes.

The plugin is in the evospikenet.sensor_integration package. Inherit the SensorDriver abstract class and register it with SensorManager. This allows you to implement new device drivers without touching the core code. This makes it easy to replace experimental equipment and expand functionality.

For detailed implementation steps, please refer to Sensor Driver Development Guide.

example:

from evospikenet.sensor_integration import SensorManager, SensorType, SensorInfo

info = SensorInfo(sensor_type=SensorType.LIDAR, name="hokuyo-utm30")
driver = SensorManager.create_driver(SensorType.LIDAR, info=info)
driver.connect()
driver.start_stream()
sample = driver.read_sample()
  • Separate device-dependent code using plug-in method
  • Easy to add/update sensor types
  • Possible to verify using dummy driver in unit test

Best Practices

  1. Model selection: Select the appropriate neuron model for your research question
  2. Parameter tuning: Validate parameters against biological data
  3. Scale Management: Start with small circuits and gradually scale up
  4. Verification: Compare simulation results with experimental data
  5. Analysis: Use multiple analysis methods for comprehensive understanding
  6. Reproducibility: Document all parameters and random seeds

Research Application

  • Cognitive Neuroscience: Working Memory, Attention, Decision Making
  • Motor control: Skill learning, motor adaptation, coordination
  • Neurodevelopment: Critical periods, synaptic pruning, and network formation
  • Neurological diseases: Modeling of Parkinson's disease, epilepsy, and schizophrenia
  • Brain-machine interface: Neural decoding, prosthetic limb control
  • Computational Psychiatry: Reward learning, motivation, and addiction

troubleshooting

Common issues: - Instability: adjust neuron parameters or reduce connection strength - No activity: Check input stimulus and threshold settings - Memory issues: reduce circuit size or use sparse connections - Slow simulation: optimize integration method or reduce recording

Debug mode:python simulation_manager.enable_debug_mode() simulation_manager.log_neural_activity() simulation_manager.enable_performance_monitoring()

Performance optimization

  • Sparse connections: Use sparse matrices for large circuits
  • Vectorization: Leverage GPU acceleration for parallel calculations
  • Adaptive timestep: Use adaptive integration for stiff equations
  • Memory Management: Implemented memory efficient data structures
  • Parallel processing: Distribute simulation across multiple cores/GPUs