finding

active

finding:cultured-neural-networks-can-learn-to-navigate-a-maze-via-closed-loop-feedback

Cultured neural networks can learn to navigate a maze via closed-loop feedback.

From DeMarse et al. (2001) and Bakkum et al. (2007), demonstrating learning in hybrid systems.

Source paper

extracted_from

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms

(2023) · Clawson, Wesley P. · Levin, Michael

Neighborhood — ranked by edge-count

Thinkers (1)

thinker

Thomas B. DeMarse
authored
Developed early hybrot/animat systems with cultured neurons.

Claims (1)

claim

Beings with brains are, in an important sense, also collective intelligences.
supports
Extends collective intelligence concept to conventional brains.

Communities (4)

community

Bioelectric morphogenesis & anatomical intelligence
members_of
Levin-led research showing bioelectric signals encode and control anatomical goal states in living systems.
Substrate-agnostic behavioral inference of cognition
members_of
Cognition and sentience attributed solely via observable behavior, not neural substrate or species.
Bioelectric networks as morphogenetic cognition
members_of
Membrane potential dynamics coordinate multicellular behavior, anatomical memory, and agency scaling across biological levels—studied via melanocyte fate, biofilm oscillations, and xenobotic morphology experiments (Levin et al. 2015–2023).
Bioelectric computation and distributed agency
members_of
Systems where biological electrical signaling enables cognitive capabilities across scales, from cells to organisms, separating hardware from information content.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Neural cultures learn to control virtual and robotic bodies in closed-loop systemsfinding0.800
Linear representation hypothesis: neural networks represent meaningful concepts as directions in their activation spaces.hypothesis0.774
Foundation for interpreting features as linear directions.
Learning neural networks can enable 'chunking' and rescale problem-solving to higher organizational levels, a mechanism intrinsic to transitions in individuality.hypothesis0.764
Cultured neural tissue controls virtual animat behavior via learned associationsfinding0.762
Demonstrates neural culture can learn relationships between its activity and sensory feedback without evolutionary training.
Neural networks show substantial alignment with biological representations in the brain, driven by shared task and data constraintsclaim0.755
Extends convergence argument to brain-machine alignment
Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.755
The paper's central thesis statement, presented prominently after the abstract
Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.752
Central claim about the power of connectionism.
Cognition and learning are substrate-independent; principles familiar in artificial neural networks can be implemented by any network of signals with suitable weighted inputs.claim0.752
Central thesis enabling unification of neural, developmental, ecological, and social networks as instances of collective intelligence.