The Architecture of a Biologically Plausible Language Organ (2306.15364v1)

Abstract: We present a simulated biologically plausible language organ, made up of stylized but realistic neurons, synapses, brain areas, plasticity, and a simplified model of sensory perception. We show through experiments that this model succeeds in an important early step in language acquisition: the learning of nouns, verbs, and their meanings, from the grounded input of only a modest number of sentences. Learning in this system is achieved through Hebbian plasticity, and without backpropagation. Our model goes beyond a parser previously designed in a similar environment, with the critical addition of a biologically plausible account for how language can be acquired in the infant's brain, not just processed by a mature brain.

• The paper introduces a computational model that simulates early language acquisition using Hebbian plasticity without backpropagation.
• It details a network architecture with stylized neurons and synapses that mimic brain-like excitatory-inhibitory balance via a k-cap operation.
• Experimental results show efficient differentiation of nouns and verbs with scalable learning and enhanced tutoring potential.

Overview of "The Architecture of a Biologically Plausible Language Organ"

This paper presents a computational model simulating a biologically plausible language organ, constructed from stylized neurons and synapses and incorporating basic brain-like properties such as plasticity and sensory perception. The authors aim to demonstrate the model's ability to execute an early stage of language acquisition: learning nouns, verbs, and their meanings from grounded language input. The model employs Hebbian plasticity, eschewing backpropagation, to achieve learning.

Contributions and Methodology

The authors detail their approach to addressing the bridging problem, which involves creating computational models that traverse the gap between neuron-level activities and cognitive functions like language acquisition. The model extends the Assembly Calculus framework, integrating neurally stylized components conducive to realistic brain-like systems.

Key elements of the model include:

• Network Architecture: Areas resembling brain regions, connected via stylized synapses, simulate neural computation. Within this architecture, the neurons fire in synchrony based on the $k$-cap operation, representing excitatory-inhibitory balance.
• Learning Mechanism: Hebbian plasticity serves as the learning principle, reinforcing synapses when pre- and post-synaptic neurons consecutively fire, aligning with observed neural learning patterns.
• Simulated Language Learning: The model translates sensory input into representations of nouns and verbs, structured such that specific brain-like areas reflect parts of speech and semantic delineations.

Experimental Results

The experimental setup exposes the model to a constructed language environment, where sentences comprise nouns and intransitive verbs with varying subject-verb orders. Noteworthy findings include:

• Acquisition Capability: The model successfully differentiates nouns and verbs, forming stable neural assemblies linked to appropriate phonological and semantic representations.
• Scalability and Efficiency: Learning efficiency, gauged by the number of required training sentences, is reported to scale linearly with the lexicon size. Adjustments to parameters like plasticity coefficient $\beta$ affect the rate of learning convergence.
• Tutoring Enhancements: Individual word tutoring shows potential in reducing training times, indicating supplementary learning pathways.

Implications and Future Directions

The authors propose that this model lays groundwork in understanding genuine brain-like language processing, offering insights transferrable to AI development, particularly in human-like learning attributes. Future work could extend beyond initial stages of language acquisition to include:

• Incorporating Functional Words: To explore accelerated learning pathways and syntactic bootstrapping mechanisms.
• Handling Abstract Concepts: Expanding semantic representation frameworks to accommodate abstract linguistic items.
• Syntax and Multilingual Capabilities: Addressing complex language structures and extending the model to encompass multilingual scenarios.

Conclusion

This work contributes to computational neuroscience by outlining a feasible pathway for simulating early language acquisition processes in a biologically plausible context. While focusing on foundational stages, the model serves as a basis for further exploration into more sophisticated language processing phenomena, potentially informing both theoretical understandings and practical AI applications.