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c3ed182 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 | """Autonomous Neural Evolution: Transaction-Safe Graph-Topology Mutation.
Enables SYNAPSE to dynamically grow, remove, split, duplicate, and reorganise
neural circuits based on task performance and metabolic constraints.
All mutations are transaction-safe with atomic preview, parent digest verification,
and rollback receipts.
"""
from __future__ import annotations
import copy
import math
import uuid
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
from .stateio import digest
SCHEMA = 'synapse-topology-mutation-v1'
@dataclass
class MutationReceipt:
mutation_id: str
mutation_type: str
parent_digest: str
resulting_digest: str
details: dict
undo_state: dict
def to_dict(self) -> dict:
return {
'mutation_id': self.mutation_id,
'mutation_type': self.mutation_type,
'parent_digest': self.parent_digest,
'resulting_digest': self.resulting_digest,
'details': self.details,
}
class TopologyEvolutionEngine:
"""Manages structural mutations on an IntegratedAgent's connectome."""
def __init__(self, metabolic_penalty_per_neuron: float = 0.05, metabolic_penalty_per_synapse: float = 0.01):
self.metabolic_penalty_per_neuron = metabolic_penalty_per_neuron
self.metabolic_penalty_per_synapse = metabolic_penalty_per_synapse
self.mutation_history: list[dict] = []
self._undo_stack: dict[str, dict] = {}
def add_neuron(self, agent, neuropil: int = 0) -> MutationReceipt:
"""Add a new neuron to the specified neuropil with bounded connectivity."""
parent_digest = digest(agent.export_state())
undo_state = copy.deepcopy(agent.export_state())
undo_graph = {
'role': agent.graph.role.copy(),
'origin': agent.graph.origin.copy(),
'cell_type': agent.graph.cell_type.copy(),
'names': list(agent.graph.names),
'graft_pre': agent.graph.graft_pre.copy(),
'graft_post': agent.graph.graft_post.copy(),
'brain_n': agent.brain.n,
}
mutation_id = uuid.uuid4().hex
new_idx = agent.brain.n
agent.brain.n += 1
agent.graph.role = np.append(agent.graph.role, 'central')
agent.graph.origin = np.append(agent.graph.origin, neuropil)
agent.graph.cell_type = np.append(agent.graph.cell_type, 'interneuron')
agent.graph.names.append(f'new_{new_idx}')
# Expand rate and state vectors
with torch.no_grad():
agent.brain.r = torch.cat([agent.brain.r, torch.zeros(1)])
agent.brain.activity_ema = torch.cat([agent.brain.activity_ema, torch.zeros(1)])
agent.brain.elig = torch.cat([agent.brain.elig, torch.zeros(1)])
agent.brain.heb = torch.cat([agent.brain.heb, torch.zeros(1)])
agent.evoked = torch.cat([agent.evoked, torch.zeros(1)])
# Connect new neuron with 2 random input and 2 random output graft edges
rng = np.random.default_rng()
possible_peers = list(range(max(0, new_idx - 32), new_idx))
if len(possible_peers) >= 2:
in_peers = rng.choice(possible_peers, size=2, replace=False)
out_peers = rng.choice(possible_peers, size=2, replace=False)
new_pres = list(agent.graph.graft_pre)
new_posts = list(agent.graph.graft_post)
new_weights = list(agent.brain.graft_w.detach().cpu().numpy())
for p in in_peers:
new_pres.append(int(p))
new_posts.append(new_idx)
new_weights.append(float(rng.normal(0.0, 0.1)))
for p in out_peers:
new_pres.append(new_idx)
new_posts.append(int(p))
new_weights.append(float(rng.normal(0.0, 0.1)))
agent.graph.graft_pre = np.array(new_pres, dtype=np.int64)
agent.graph.graft_post = np.array(new_posts, dtype=np.int64)
agent.brain.graft_w = nn.Parameter(torch.tensor(new_weights, dtype=torch.float32))
resulting_digest = digest(agent.export_state())
details = {
'new_neuron_index': new_idx,
'neuropil': neuropil,
'total_neurons': agent.brain.n,
'new_synapses': 4,
'metabolic_delta': self.metabolic_penalty_per_neuron + 4 * self.metabolic_penalty_per_synapse
}
receipt = MutationReceipt(mutation_id, 'add_neuron', parent_digest, resulting_digest, details, undo_state)
self._undo_stack[mutation_id] = {'agent_state': undo_state, 'graph_state': undo_graph}
self.mutation_history.append(receipt.to_dict())
return receipt
def split_neuron(self, agent, node_id: int) -> MutationReceipt:
"""Split a bottleneck neuron into two specialized sister neurons."""
if not (0 <= node_id < agent.brain.n):
raise ValueError(f'Invalid node_id {node_id}')
parent_digest = digest(agent.export_state())
undo_state = copy.deepcopy(agent.export_state())
mutation_id = uuid.uuid4().hex
neuropil = int(agent.graph.origin[node_id]) if node_id < len(agent.graph.origin) else 0
sister_id = agent.brain.n
agent.brain.n += 1
agent.graph.role = np.append(agent.graph.role, str(agent.graph.role[node_id]))
agent.graph.origin = np.append(agent.graph.origin, neuropil)
agent.graph.cell_type = np.append(agent.graph.cell_type, str(agent.graph.cell_type[node_id]))
agent.graph.names.append(f'sister_{node_id}')
with torch.no_grad():
agent.brain.r = torch.cat([agent.brain.r, torch.zeros(1)])
agent.brain.activity_ema = torch.cat([agent.brain.activity_ema, torch.zeros(1)])
agent.brain.elig = torch.cat([agent.brain.elig, torch.zeros(1)])
agent.brain.heb = torch.cat([agent.brain.heb, torch.zeros(1)])
agent.evoked = torch.cat([agent.evoked, torch.zeros(1)])
# Split incoming edges between original and sister; duplicate outgoing edges
new_pres = list(agent.graph.graft_pre)
new_posts = list(agent.graph.graft_post)
new_weights = list(agent.brain.graft_w.detach().cpu().numpy())
for i in range(len(agent.graph.graft_pre)):
pre = int(agent.graph.graft_pre[i])
post = int(agent.graph.graft_post[i])
w = float(new_weights[i])
if post == node_id and i % 2 == 1:
# Divert half of incoming connections to sister
new_posts[i] = sister_id
elif pre == node_id:
# Duplicate outgoing connection for sister
new_pres.append(sister_id)
new_posts.append(post)
new_weights.append(w * 0.5)
agent.graph.graft_pre = np.array(new_pres, dtype=np.int64)
agent.graph.graft_post = np.array(new_posts, dtype=np.int64)
agent.brain.graft_w = nn.Parameter(torch.tensor(new_weights, dtype=torch.float32))
resulting_digest = digest(agent.export_state())
details = {
'original_node': node_id,
'sister_node': sister_id,
'total_neurons': agent.brain.n,
'total_grafts': len(agent.graph.graft_pre)
}
receipt = MutationReceipt(mutation_id, 'split_neuron', parent_digest, resulting_digest, details, undo_state)
self._undo_stack[mutation_id] = undo_state
self.mutation_history.append(receipt.to_dict())
return receipt
def duplicate_subcircuit(self, agent, node_ids: list[int]) -> MutationReceipt:
"""Duplicate a specialist subnetwork to create parallel processing channels."""
if not node_ids or any(n < 0 or n >= agent.brain.n for n in node_ids):
raise ValueError('Invalid node_ids for subcircuit duplication')
parent_digest = digest(agent.export_state())
undo_state = copy.deepcopy(agent.export_state())
mutation_id = uuid.uuid4().hex
id_map = {}
for old_id in node_ids:
new_id = agent.brain.n
id_map[old_id] = new_id
agent.brain.n += 1
agent.graph.role = np.append(agent.graph.role, str(agent.graph.role[old_id]))
agent.graph.origin = np.append(agent.graph.origin, int(agent.graph.origin[old_id]))
agent.graph.cell_type = np.append(agent.graph.cell_type, str(agent.graph.cell_type[old_id]))
agent.graph.names.append(f'dup_{old_id}')
with torch.no_grad():
pad_len = len(node_ids)
agent.brain.r = torch.cat([agent.brain.r, torch.zeros(pad_len)])
agent.brain.activity_ema = torch.cat([agent.brain.activity_ema, torch.zeros(pad_len)])
agent.brain.elig = torch.cat([agent.brain.elig, torch.zeros(pad_len)])
agent.brain.heb = torch.cat([agent.brain.heb, torch.zeros(pad_len)])
agent.evoked = torch.cat([agent.evoked, torch.zeros(pad_len)])
new_pres = list(agent.graph.graft_pre)
new_posts = list(agent.graph.graft_post)
new_weights = list(agent.brain.graft_w.detach().cpu().numpy())
node_set = set(node_ids)
for i in range(len(agent.graph.graft_pre)):
pre = int(agent.graph.graft_pre[i])
post = int(agent.graph.graft_post[i])
w = float(new_weights[i])
if pre in node_set and post in node_set:
# Internal edge duplicated
new_pres.append(id_map[pre])
new_posts.append(id_map[post])
new_weights.append(w)
elif pre in node_set:
# Outgoing edge duplicated
new_pres.append(id_map[pre])
new_posts.append(post)
new_weights.append(w * 0.5)
elif post in node_set:
# Incoming edge duplicated
new_pres.append(pre)
new_posts.append(id_map[post])
new_weights.append(w * 0.5)
agent.graph.graft_pre = np.array(new_pres, dtype=np.int64)
agent.graph.graft_post = np.array(new_posts, dtype=np.int64)
agent.brain.graft_w = nn.Parameter(torch.tensor(new_weights, dtype=torch.float32))
resulting_digest = digest(agent.export_state())
details = {
'duplicated_nodes': len(node_ids),
'id_map': id_map,
'total_neurons': agent.brain.n,
'total_grafts': len(agent.graph.graft_pre)
}
receipt = MutationReceipt(mutation_id, 'duplicate_subcircuit', parent_digest, resulting_digest, details, undo_state)
self._undo_stack[mutation_id] = undo_state
self.mutation_history.append(receipt.to_dict())
return receipt
def prune_synapses(self, agent, threshold: float = 0.02) -> MutationReceipt:
"""Prune functionally dormant synapses below activity threshold with metabolic credit."""
parent_digest = digest(agent.export_state())
undo_state = copy.deepcopy(agent.export_state())
mutation_id = uuid.uuid4().hex
with torch.no_grad():
w = agent.brain.graft_w.detach().cpu().numpy()
keep_mask = np.abs(w) >= threshold
if not np.any(keep_mask):
# Always keep at least the top 10% strongest connections
top_k = max(1, len(w) // 10)
keep_indices = np.argsort(np.abs(w))[-top_k:]
keep_mask = np.zeros(len(w), dtype=bool)
keep_mask[keep_indices] = True
pruned_count = int(np.sum(~keep_mask))
agent.graph.graft_pre = agent.graph.graft_pre[keep_mask]
agent.graph.graft_post = agent.graph.graft_post[keep_mask]
agent.brain.graft_w = nn.Parameter(torch.tensor(w[keep_mask], dtype=torch.float32))
resulting_digest = digest(agent.export_state())
details = {
'pruned_synapses': pruned_count,
'retained_synapses': int(np.sum(keep_mask)),
'threshold': threshold,
'metabolic_credit': pruned_count * self.metabolic_penalty_per_synapse
}
receipt = MutationReceipt(mutation_id, 'prune_synapses', parent_digest, resulting_digest, details, undo_state)
self._undo_stack[mutation_id] = undo_state
self.mutation_history.append(receipt.to_dict())
return receipt
def rollback(self, agent, mutation_id: str) -> dict:
"""Rollback a structural mutation using its saved undo state."""
if mutation_id not in self._undo_stack:
raise KeyError(f'Mutation {mutation_id} not found in undo stack')
saved = self._undo_stack.pop(mutation_id)
if isinstance(saved, dict) and 'graph_state' in saved:
undo_state = saved['agent_state']
gs = saved['graph_state']
agent.graph.role = gs['role'].copy()
agent.graph.origin = gs['origin'].copy()
agent.graph.cell_type = gs['cell_type'].copy()
agent.graph.names = list(gs['names'])
agent.graph.graft_pre = gs['graft_pre'].copy()
agent.graph.graft_post = gs['graft_post'].copy()
agent.brain.n = gs['brain_n']
else:
undo_state = saved
agent.import_state(undo_state)
return {
'status': 'ok',
'rolled_back': mutation_id,
'restored_digest': digest(agent.export_state()),
'scope': 'transaction-safe graph topology mutation rollback'
}
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