Skip to main content الرئيسية المنشئون hiyenwong ai_collection ember-autonomous-cognitive-behaviour-learned-spiking
ember-autonomous-cognitive-behaviour-learned-spiking EMBER: Autonomous cognitive behaviour from learned spiking neural network dynamics. Self-organizing SNN agents with intrinsic motivation, curiosity, and goal-directed behavior emerging from plastic recurrent connectivity without external reward shaping. Keywords: autonomous cognition, intrinsic motivation, SNN agents, emergent behavior, self-organization, curiosity-driven learning.
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/hiyenwong/ai_collection --skill ember-autonomous-cognitive-behaviour-learned-spikingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المهن ذات الصلة SOC
استنادا إلى تصنيف SOC المهني
name ember-autonomous-cognitive-behaviour-learned-spiking description EMBER: Autonomous cognitive behaviour from learned spiking neural network dynamics. Self-organizing SNN agents with intrinsic motivation, curiosity, and goal-directed behavior emerging from plastic recurrent connectivity without external reward shaping. Keywords: autonomous cognition, intrinsic motivation, SNN agents, emergent behavior, self-organization, curiosity-driven learning.
EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics
EMBER (Emergent Mind Through Biological Event-driven Responses) - self-organizing autonomous agents where complex cognitive behaviors emerge from learned spiking neural network dynamics through intrinsic motivation, curiosity, and plastic recurrent connectivity without external reward engineering.
Metadata
Source : arXiv:2604.12167v1Authors : [Authors from paper]Published : 2026-04-14Category : Neural and Evolutionary Computing (cs.NE), Artificial Intelligence (cs.AI)
Core Methodology
Key Innovation
EMBER presents a paradigm shift from reward-engineered to self-organizing autonomous agents. Rather than designing reward functions for specific behaviors, EMBER agents develop cognitive capabilities through:
Intrinsic Motivation : Curiosity, novelty-seeking, and competence drivesPlastic Recurrent Connectivity : Self-organizing RNN/SNN with local learning rulesEmergent Goal-Directedness : Goals arise from internal state rather than external specification
Technical Framework
1. Intrinsic Motivation Architecture
Novelty-Based Motivation:
R_novelty(s) = -log p(s | history)
High novelty reward for unexpected states.
Competence-Based Motivation:
R_competence(s, a) = ||s_target - s_actual|| - ||s_target - s_predicted||
Reward for successfully controlling outcomes.
Information Gain Motivation:
R_info = H(s_future) - H(s_future | a)
Reward for actions that reduce uncertainty.
2. Spiking Recurrent Network with Plasticity
Input neurons: Sensory encoding
Recurrent excitatory pool: Working memory, sequence processing
Inhibitory interneurons: Gain control, competition
Motor output: Action selection
Hebbian plasticity: "Neurons that fire together wire together"
Homeostatic regulation: Maintains firing rate stability
Meta-plasticity: Learning rate modulation by success
3. Self-Organized Behavior Generation
Goals as attractor states in recurrent dynamics
No explicit goal encoding - goals are high-value internal states
Goal switching through attractor basin hopping
Intrinsic motivation guides exploration
Learned predictions guide exploitation
Balance through uncertainty-weighted sampling
Key Findings
1. Emergent Cognitive Behaviors
Foraging : Agents self-organize efficient resource gatheringTool Use : Spontaneous discovery of environmental affordancesSocial Behaviors : Communication and coordination in multi-agent scenariosPlanning : Multi-step action sequences emerge from predictive dynamics
2. Robustness to Environment Changes
Adapts to novel environments without retraining
Generalizes across task domains
Resilient to sensor/actuator failures
3. Scalability
Behavior complexity scales with network size
Modular architecture enables hierarchical cognition
Parallel exploration in multi-agent settings
Implementation Guide
Prerequisites
Python 3.8+
snnTorch or Norse for SNN simulation
Gymnasium/OpenAI Gym for environments
PyTorch for network components
Step-by-Step Implementation Step 1: Intrinsic Motivation Module
import torch
import torch.nn as nn
import numpy as np
class IntrinsicMotivationModule :
"""
Compute intrinsic rewards for autonomous exploration
"""
def __init__ (self, state_dim, memory_size=10000 , novelty_decay=0.99 ):
self .state_dim = state_dim
self .memory = []
self .memory_size = memory_size
self .novelty_decay = novelty_decay
self .forward_model = self ._build_forward_model()
self .state_density = OnlineKernelDensity(state_dim)
def _build_forward_model (self ):
"""Build forward dynamics model"""
return nn.Sequential(
nn.Linear(self .state_dim + self .action_dim, 256 ),
nn.ReLU(),
nn.Linear(256 , self .state_dim)
)
def compute_novelty (self, state ):
"""
Compute novelty reward as negative log likelihood
Args:
state: Current state (batch, state_dim)
Returns:
novelty: Scalar novelty score
"""
prob = self .state_density.estimate(state)
novelty = -torch.log(prob + 1e-10 )
return novelty
def compute_prediction_error (self, state, action, next_state ):
"""
Compute prediction error for competence motivation
Args:
state: Current state
action: Action taken
next_state: Resulting state
Returns:
error: Prediction error (higher = more learning opportunity)
"""
input_sa = torch.cat([state, action], dim=-1 )
predicted_next = self .forward_model(input_sa)
error = torch.mean((next_state - predicted_next) ** 2 , dim=-1 )
return error
def compute_information_gain (self, state, action ):
"""
Estimate information gain from taking action
Args:
state: Current state
action: Candidate action
Returns:
info_gain: Expected information gain
"""
n_samples = 10
entropies = []
for _ in range (n_samples):
next_state = self .forward_model(torch.cat([state, action]))
next_state += torch.randn_like(next_state) * 0.1
prob = self .state_density.estimate(next_state)
entropy = -torch.log(prob + 1e-10 )
entropies.append(entropy)
info_gain = torch.mean(torch.stack(entropies))
return info_gain
def compute_intrinsic_reward (self, state, action, next_state ):
"""
Compute total intrinsic reward
Args:
state: Current state
action: Action taken
next_state: Next state
Returns:
reward: Intrinsic reward scalar
components: Dict of reward components
"""
novelty = self .compute_novelty(next_state)
pred_error = self .compute_prediction_error(state, action, next_state)
info_gain = self .compute_information_gain(state, action)
total_reward = novelty + 0.5 * pred_error + 0.3 * info_gain
components = {
'novelty' : novelty.item(),
'prediction_error' : pred_error.item(),
'information_gain' : info_gain.item()
}
self .update_memory(next_state)
return total_reward, components
def update_memory (self, state ):
"""Update state memory"""
self .memory.append(state.detach().cpu())
if len (self .memory) > self .memory_size:
self .memory.pop(0 )
self .state_density.update(state)
class OnlineKernelDensity :
"""
Online kernel density estimator for novelty computation
"""
def __init__ (self, dim, bandwidth=0.1 ):
self .dim = dim
self .bandwidth = bandwidth
self .samples = []
self .max_samples = 5000
def update (self, sample ):
"""Add new sample"""
self .samples.append(sample.detach().cpu().numpy())
if len (self .samples) > self .max_samples:
self .samples = self .samples[-self .max_samples:]
def estimate (self, query ):
"""
Estimate probability density at query point
Args:
query: (batch, dim) query points
Returns:
prob: (batch,) estimated probabilities
"""
if len (self .samples) < 100 :
return torch.ones(query.shape[0 ], device=query.device) * 0.5
query_np = query.detach().cpu().numpy()
samples_np = np.array(self .samples)
probs = []
for q in query_np:
distances = np.linalg.norm(samples_np - q, axis=1 )
kernel_vals = np.exp(-distances**2 / (2 * self .bandwidth**2 ))
prob = np.mean(kernel_vals)
probs.append(prob)
return torch.tensor(probs, device=query.device, dtype=torch.float32)
Step 2: Recurrent SNN with Plastic Connectivity
import snntorch as snn
from snntorch import surrogate
class PlasticRecurrentSNN (nn.Module):
"""
Recurrent SNN with biologically plausible plasticity
"""
def __init__ (self, input_size, hidden_size, output_size,
recurrent_size=256 , beta=0.9 ):
super ().__init__()
self .input_size = input_size
self .hidden_size = hidden_size
self .recurrent_size = recurrent_size
self .input_encoder = nn.Linear(input_size, recurrent_size)
self .rec_exc = snn.Leaky(beta=beta, init_hidden=True )
self .rec_inh = snn.Leaky(beta=beta, init_hidden=True )
self .w_ee = nn.Parameter(torch.randn(recurrent_size, recurrent_size) * 0.01 )
self .w_ei = nn.Parameter(torch.randn(recurrent_size, recurrent_size) * 0.01 )
self .w_ie = nn.Parameter(torch.randn(recurrent_size, recurrent_size) * 0.01 )
self .readout = nn.Linear(recurrent_size, output_size)
self .surrogate = surrogate.fast_sigmoid(slope=25 )
self .A_plus = 0.01
self .A_minus = 0.01
self .tau_stdp = 20.0
def forward (self, x, time_steps=100 , return_spikes=True ):
"""
Forward pass through recurrent SNN
Args:
x: Input (batch, input_features)
time_steps: Number of simulation steps
return_spikes: Return full spike trains
Returns:
output: Action logits
spikes: Spike trains (if return_spikes=True)
"""
batch_size = x.shape[0 ]
input_current = self .input_encoder(x)
mem_exc = self .rec_exc.init_leaky()
mem_inh = self .rec_inh.init_leaky()
spikes_exc = []
spikes_inh = []
for t in range (time_steps):
if t == 0 :
rec_input_exc = input_current
else :
prev_exc = spikes_exc[-1 ] if spikes_exc else torch.zeros_like(mem_exc)
prev_inh = spikes_inh[-1 ] if spikes_inh else torch.zeros_like(mem_inh)
rec_input_exc = (torch.matmul(prev_exc, self .w_ee.t()) -
torch.matmul(prev_inh, self .w_ie.t()) +
input_current)
spk_exc, mem_exc = self .rec_exc(rec_input_exc, mem_exc)
inh_input = torch.matmul(spk_exc, self .w_ei.t())
spk_inh, mem_inh = self .rec_inh(inh_input, mem_inh)
spikes_exc.append(spk_exc)
spikes_inh.append(spk_inh)
spike_trains_exc = torch.stack(spikes_exc, dim=1 )
spike_trains_inh = torch.stack(spikes_inh, dim=1 )
rates = spike_trains_exc.mean(dim=1 )
output = self .readout(rates)
if return_spikes:
return output, (spike_trains_exc, spike_trains_inh)
return output
def apply_stdp (self, spike_trains, reward_signal ):
"""
Apply reward-modulated STDP to recurrent weights
Args:
spike_trains: (batch, time, neurons) spike trains
reward_signal: Scalar reward value
"""
batch_size, time_steps, n_neurons = spike_trains.shape
with torch.no_grad():
for b in range (batch_size):
for i in range (n_neurons):
for j in range (n_neurons):
if i == j:
continue
times_i = torch.where(spike_trains[b, :, i] > 0 )[0 ].float ()
times_j = torch.where(spike_trains[b, :, j] > 0 )[0 ].float ()
if len (times_i) == 0 or len (times_j) == 0 :
continue
delta_t = times_i.unsqueeze(1 ) - times_j.unsqueeze(0 )
if delta_t > 0 :
dw = self .A_plus * torch.exp(-delta_t / self .tau_stdp)
else :
dw = -self .A_minus * torch.exp(delta_t / self .tau_stdp)
self .w_ee.data[i, j] += reward_signal * dw.mean()
self .w_ee.data = torch.clamp(self .w_ee.data, min =0 )
self .w_ei.data = torch.clamp(self .w_ei.data, min =0 )
self .w_ie.data = torch.clamp(self .w_ie.data, min =0 )
class EMBERAgent :
"""
EMBER autonomous agent with self-organized behavior
"""
def __init__ (self, state_dim, action_dim, env,
network_size=256 , learning_rate=0.001 ):
self .state_dim = state_dim
self .action_dim = action_dim
self .env = env
self .motivation = IntrinsicMotivationModule(state_dim)
self .network = PlasticRecurrentSNN(
state_dim, network_size, action_dim, network_size
)
self .optimizer = torch.optim.Adam(
self .network.parameters(), lr=learning_rate
)
self .buffer = []
self .buffer_size = 10000
def select_action (self, state, epsilon=0.1 ):
"""
Select action using network output
Args:
state: Current state
epsilon: Exploration rate
Returns:
action: Selected action
"""
state_t = torch.FloatTensor(state).unsqueeze(0 )
if np.random.random() < epsilon:
return self .env.action_space.sample()
with torch.no_grad():
action_logits, _ = self .network(state_t)
action_probs = torch.softmax(action_logits, dim=-1 )
action = torch.multinomial(action_probs, 1 ).item()
return action
def train_step (self, batch_size=32 ):
"""
Training step with intrinsic motivation
Args:
batch_size: Number of transitions to sample
Returns:
loss: Training loss
"""
if len (self .buffer) < batch_size:
return None
batch = np.random.choice(self .buffer, batch_size, replace=False )
states = torch.FloatTensor([t[0 ] for t in batch])
actions = torch.LongTensor([t[1 ] for t in batch])
next_states = torch.FloatTensor([t[2 ] for t in batch])
intrinsic_rewards = []
for s, a, ns in zip (states, actions, next_states):
a_onehot = torch.zeros(self .action_dim)
a_onehot[a] = 1
r, _ = self .motivation.compute_intrinsic_reward(
s.unsqueeze(0 ), a_onehot.unsqueeze(0 ), ns.unsqueeze(0 )
)
intrinsic_rewards.append(r)
intrinsic_rewards = torch.stack(intrinsic_rewards)
action_logits, (spikes_exc, spikes_inh) = self .network(states)
action_probs = torch.softmax(action_logits, dim=-1 )
selected_probs = action_probs.gather(1 , actions.unsqueeze(1 ))
loss = -(torch.log(selected_probs) * intrinsic_rewards.detach()).mean()
self .optimizer.zero_grad()
loss.backward()
self .optimizer.step()
self .network.apply_stdp(spikes_exc, intrinsic_rewards.mean().item())
return loss.item()
def run_episode (self, max_steps=1000 , render=False ):
"""
Run one episode
Args:
max_steps: Maximum episode length
render: Whether to render environment
Returns:
total_reward: Sum of intrinsic rewards
episode_length: Number of steps
"""
state = self .env.reset()
total_intrinsic_reward = 0
for step in range (max_steps):
if render:
self .env.render()
action = self .select_action(state)
next_state, env_reward, done, info = self .env.step(action)
state_t = torch.FloatTensor(state).unsqueeze(0 )
next_state_t = torch.FloatTensor(next_state).unsqueeze(0 )
action_t = torch.zeros(self .action_dim)
action_t[action] = 1
intrinsic_r, components = self .motivation.compute_intrinsic_reward(
state_t, action_t.unsqueeze(0 ), next_state_t
)
total_intrinsic_reward += intrinsic_r.item()
self .buffer.append((state, action, next_state))
if len (self .buffer) > self .buffer_size:
self .buffer.pop(0 )
if step % 4 == 0 :
self .train_step()
state = next_state
if done:
break
return total_intrinsic_reward, step + 1
def train (self, n_episodes=1000 , eval_interval=100 ):
"""
Train agent for multiple episodes
Args:
n_episodes: Number of training episodes
eval_interval: Episodes between evaluations
"""
for episode in range (n_episodes):
reward, length = self .run_episode()
if episode % 10 == 0 :
print (f"Episode {episode} : Intrinsic Reward={reward:.2 f} , Length={length} " )
if episode % eval_interval == 0 :
eval_reward, eval_length = self .evaluate()
print (f" [Eval] Reward={eval_reward:.2 f} , Length={eval_length} " )
def evaluate (self, n_episodes=5 ):
"""
Evaluate agent performance
Args:
n_episodes: Number of evaluation episodes
Returns:
avg_reward: Average intrinsic reward
avg_length: Average episode length
"""
rewards = []
lengths = []
for _ in range (n_episodes):
r, l = self .run_episode(render=False )
rewards.append(r)
lengths.append(l)
return np.mean(rewards), np.mean(lengths)
Applications
1. Autonomous Robotics
Self-motivated exploration robots
Adaptive navigation without task specification
Lifelong learning agents
2. Artificial Life Simulation
Virtual creatures with emergent behavior
Ecosystems with self-organized interactions
Evolutionary robotics
3. Cognitive Neuroscience Models
Study of intrinsic motivation in biological systems
Computational models of curiosity
Goal-directed behavior emergence
4. Educational AI
Self-motivated learning systems
Adaptive tutoring agents
Curiosity-driven knowledge acquisition
Pitfalls
1. Exploration-Exploitation Balance
Issue : Pure intrinsic motivation can lead to endless explorationMitigation : Gradually shift to external rewards or competence-based goals
2. Emergence Unpredictability
Issue : Behaviors may not align with designer intentionsMitigation : Constrain environment, provide safe exploration spaces
3. Computational Cost
Issue : Kernel density estimation is expensiveMitigation : Use approximate methods, limit memory size
4. Local Minima in Motivation
Issue : Agent may get stuck in locally interesting patternsMitigation : Multiple motivation sources, meta-learning
Related Skills
intrinsic-motivation-rl
self-organizing-transformer
neuromodulated-synaptic-plasticity
brain-inspired-snn-pattern-analysis
References @article{2026ember,
title={EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics},
journal={arXiv preprint arXiv:2604.12167},
year={2026}
}
