Neuromorphic Online Clustering and Its Application to Spike Sorting (2506.12555v1)

Abstract: Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. Features and capabilities of neuromorphic dendrites are demonstrated via a benchmark drawn from experimental neuroscience: spike sorting. Spike sorting takes inputs from electrical probes implanted in neural tissue, detects voltage spikes (action potentials) emitted by neurons, and attempts to sort the spikes according to the neuron that emitted them. Many spike sorting methods form clusters based on the shapes of action potential waveforms, under the assumption that spikes emitted by a given neuron have similar shapes and will therefore map to the same cluster. Using a stream of synthetic spike shapes, the accuracy of the proposed dendrite is compared with the more compute-intensive, offline k-means clustering approach. Overall, the dendrite outperforms k-means and has the advantage of requiring only a single pass through the input stream, learning as it goes. The capabilities of the neuromorphic dendrite are demonstrated for a number of scenarios including dynamic changes in the input stream, differing neuron spike rates, and varying neuron counts.

• The paper presents a novel neuromorphic framework employing active dendrites for online clustering, achieving superior real-time spike sorting compared to traditional k-means.
• It utilizes dynamic template updating and probabilistic search to adapt to input variations, ensuring rapid convergence in simulated neural environments.
• The study demonstrates efficient single-pass learning and energy efficiency, paving the way for advanced brain-computer interfaces and neuromorphic architectures.

Neuromorphic Online Clustering and Its Application to Spike Sorting

The paper "Neuromorphic Online Clustering and Its Application to Spike Sorting" introduces a novel approach to implementing neuromorphic dendrites for online clustering, emphasizing its application to spike sorting. Active dendrites are proposed as a basis for biologically plausible neural networks that incorporate flexibility, adaptability, and energy efficiency akin to the biological brain. This work shifts from conventional spiking neuron formulations to a machine learning-based framework facilitating dynamic online clustering. The core utility of neuromorphic dendrites is demonstrated through simulations utilizing benchmark scenarios derived from experimental neuroscience.

Neuromorphic Dendrites and Spike Sorting

Active dendrites underpin biologically plausible neural networks that can dynamically adapt and learn online. These dendrites mimic the function of biological counterparts, where synaptic weights are adjusted to detect similar input patterns, effectively forming clusters. This paper emphasizes using neuromorphic dendrites (nD) as elemental computing units that conduct online clustering through streaming feature vectors. The dendrites output cluster identifiers based on inferring pattern similarity.

Spike sorting is central to this paper, leveraging nD’s online clustering to differentiate neuronal spikes captured via electrode arrays. This process of shape sorting is essential in neuroscience applications, especially BCIs, to map neuron-to-spike associations in real time. The simulated experiments, compared against an offline k-means baseline, highlight nD's superior performance in online clustering and its suitability for real-time applications.

Inference and Update Mechanisms

The nD model operates by processing feature vectors composed of components with integer values. Clustering is achieved by matching these against learned templates representing dendritic segments. Inference is conducted via a maximization step that identifies the template with the closest match, resulting in an output cluster identifier (CId). This is immediately followed by updating the templates: increasing weights corresponding to input values in the winning template and adjusting other weights based on capture and backoff parameters. This mechanism provides rapid convergence, balancing the stability/plasticity trade-off crucial in adaptive systems.

Simulation Framework

Synthetic benchmarks play a pivotal role in illustrating nD’s capabilities due to the challenge of validating spike sorting with experimentally acquired neural signals. The paper models synthetic neurons and varies spike shapes to observe dendritic clustering performance. Benchmarks are designed with controlled deviations from canonical neuron shapes, allowing precise performance metrics. This setup aids in exploring parameter settings and understanding dendrite-based clustering's efficacy compared to traditional k-means clustering.

Performance and Adaptability

The offline k-means method was designated a baseline for assessing nD performance. Under ideal initialized conditions, both methods approached maximum theoretical accuracy. However, nD surpassed k-means, particularly in scenarios with minor instance deviations, due to its online adaptive capabilities. The probabilistic search method showcased comparable accuracy with reduced computational demands, demonstrating the design's robustness.

In more realistic simulations with randomized initial centroid settings, the neuromorphic dendrites performed favorably against conventional k-means. This reflects the inherent adaptability of the neuromorphic method, which persists despite variability in spiking rates and changes in neuron counts.

Implications and Future Directions

The neuromorphic dendrite (nD) framework provides a robust alternative to traditional clustering algorithms like k-means, especially when dealing with data streams requiring real-time processing, as demonstrated by its application to spike sorting. Its configuration allows for a decrement-based learning mechanism analogous to synaptic plasticity models, accommodating shifts in input patterns. A significant advantage comes from nD's single-pass learning process, contrasting with the multiple required iterations of traditional methods such as k-means, which shows increased computational efficiency, crucial for neuromorphic applications that mimic brain-like operations.

Looking forward, the research raises critical implications for the design of future computing architectures and applications in neuroscience. The online adaptability of the neuromorphic dendritic model offers potential for advancing real-time data processing tasks, such as in brain-computer interfaces (BCIs) and other neural interfacing technologies. Future developments may extend this framework to higher abstraction layers, such as neuromorphic neurons and columns, aiming to achieve more complex brain-like computation with minimal energy consumption.

Conclusion

This paper contributes to the progressing field of neuromorphic computing by detailing a machine learning-based framework for implementing biologically inspired active dendrites. Through rigorous simulation on synthetic spike sorting benchmarks, the paper demonstrates that neuromorphic dendrites surpass traditional k-means clustering in efficiency by requiring only a single pass through input data. This characteristic highlights their potential in real-time applications, such as BCIs, where on-the-fly learning at reduced computational costs can have significant implications. The work establishes a foundation for further research on subsequent abstraction layers in neuromorphic architectures, with the potential to impact various fields within AI and computational neuroscience by enhancing our understanding of neural processing and improving artificial systems' efficiency.