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Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex (1511.00083v2)

Published 31 Oct 2015 in q-bio.NC and cs.AI

Abstract: Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hundreds of unique patterns of cellular activity, even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where some of the patterns recognized by a neuron lead to action potentials and define the classic receptive field of the neuron, whereas the majority of the patterns recognized by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential. We then present a network model based on neurons with these properties and show that the network learns a robust model of time-based sequences. Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue. We further propose that cellular layers in the neocortex implement variations of the same sequence memory algorithm to achieve different aspects of inference and behavior. The neuron and network models we introduce are robust over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. The sequence capacity of the network scales linearly with the number of synapses on each neuron. Thus neurons need thousands of synapses to learn the many temporal patterns in sensory stimuli and motor sequences.

Citations (391)

Summary

  • The paper demonstrates that thousands of excitatory synapses enable neurons to recognize complex patterns and predict future activity.
  • It introduces a neuron model distinguishing proximal, basal, and apical dendrites for receptive fields, sequence transitions, and top-down expectation.
  • The study highlights that sparse distributed representations allow each neuron to learn hundreds of unique patterns, scaling sequence capacity with synapse count.

Sequence Memory in Neocortex: Understanding Neuronal Synapses and Predictions

The paper by Jeff Hawkins and Subutai Ahmad presents a comprehensive theory pertaining to the functionality of the neocortex, which comprises neurons with thousands of synapses. The central question addressed is why neurons have such a high count of synapses, particularly the significance of distal synapses and their role in memory and cortical processing.

Summary of Findings

The researchers propose that neurons, equipped with active dendrites and thousands of excitatory synapses, recognize patterns of cellular activity robustly. In this model, the thousands of synapses are not merely redundant, but instead allow a single neuron to recognize numerous patterns within large scale neural networks, even amidst high noise levels and variabilities. The paper posits that, rather remarkably, the majority of pattern recognition by neurons functions as predictions rather than resulting in immediate action potentials. This is achievable through non-linear dendritic properties, which enable neurons to predict upcoming neural activities through a slight depolarization effect rather than immediate neural firing.

Model of Neocortical Functionality

Hawkins and Ahmad introduce a neuron model in which synapses on various dendrite zones contribute differently to neuron behavior: proximal synapses define the basic receptive field, basal dendrites encode sequence transitions, and apical synapses impart a top-down expectation. The paper proposes that the entire network of neurons orchestrated within this model can learn time-based sequences essential for cognitive functions such as sensory processing, inference, and behavior.

The proposed network, implementing these neuronal properties, demonstrates capability in creating a robust sequence memory model. Importantly, a distinctive feature is its use of sparse distributed representations—ensuring each neuron learns hundreds of unique patterns. The sequence capacity scales linearly with the synapse count, emphasizing the necessity of numerous synapses for encoding temporal patterns in sensory-motor sequences.

Implications and Future Directions

This model suggests a universal algorithmic principle underlying the structure and function of the neocortex. With such insights, this paper provides a foundational understanding that could inspire numerous developments in artificial intelligence, particularly in sequence learning and prediction mechanisms. The theoretical constructs could significantly impact the design of neural architectures in AI systems, emphasizing the potential of sparse representations and localized learning rules in building robust, adaptable networks.

Furthermore, this research holds promise for elucidating cognitive functions' biological roots, providing avenues for exploring neurological disorders where these processes may be disrupted. As computational models evolve, integrating biophysically detailed neuron models with larger network frameworks could yield a deeper understanding of cognitive functions and learning.

Theoretical and Practical Applications

This work opens new research pathways into understanding how the neocortical structure relates to its function, suggesting that sequence memory and prediction might be a validating hypothesis for a universal computational model of the cortex. From the potential development of artificial systems capable of mimicking human cognitive abilities to informed therapeutic interventions in neurodegenerative diseases, the paper lays a foundational framework for future exploration and application.

In conclusion, by illustrating both the unique properties and substantial capabilities of neocortical neurons and networks, Hawkins and Ahmad contribute significantly to the deepening of our understanding of brain function and its application to artificial intelligence. Their theory highlights the essential role of sequence memory as a cornerstone of cognitive processes, reshaping our understanding of neural architectures both natural and artificial.