From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition
- URL: http://arxiv.org/abs/2505.03510v2
- Date: Mon, 04 Aug 2025 14:37:42 GMT
- Title: From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition
- Authors: Ludovico Iannello, Luca Ciampi, Gabriele Lagani, Fabrizio Tonelli, Eleonora Crocco, Lucio Maria Calcagnile, Angelo Di Garbo, Federico Cremisi, Giuseppe Amato,
- Abstract summary: We introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC)<n>This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons.<n>Results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks.
- Score: 3.342739594466204
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC). This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons, rather than being modeled by traditional artificial computational units. The neuronal activity is recorded using a multi-electrode array (MEA), which enables high-throughput recording of neural signals. In our approach, inputs are introduced into the network through a subset of the MEA electrodes, while the remaining electrodes capture the resulting neural activity. This generates a nonlinear mapping of the input data to a high-dimensional biological feature space, where distinguishing between data becomes more efficient and straightforward, allowing a simple linear classifier to perform pattern recognition tasks effectively. To evaluate the performance of our proposed system, we present an experimental study that includes various input patterns, such as positional codes, bars with different orientations, and a digit recognition task. The results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks, paving the way for further exploration of biologically-inspired computing systems, with potential applications in neuromorphic engineering and bio-hybrid computing.
Related papers
- Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition [2.222098162797332]
This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spiking information from surface electromyography (sEMG) data in an event-driven manner.<n>The network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN)<n>The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6% and 80.3%.
arXiv Detail & Related papers (2025-03-10T17:18:14Z) - Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks.
In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
arXiv Detail & Related papers (2024-09-17T04:48:45Z) - Design and development of opto-neural processors for simulation of
neural networks trained in image detection for potential implementation in
hybrid robotics [0.0]
Living neural networks offer advantages of lower power consumption, faster processing, and biological realism.
This work proposes a simulated living neural network trained indirectly by backpropagating STDP based algorithms using precision activation by optogenetics.
arXiv Detail & Related papers (2024-01-17T04:42:49Z) - The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron.
Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters.
We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
arXiv Detail & Related papers (2023-06-14T13:34:13Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with
Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations.
The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
arXiv Detail & Related papers (2022-01-19T09:26:34Z) - Evolving spiking neuron cellular automata and networks to emulate in
vitro neuronal activity [0.0]
We produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro.
Our models were able to produce a level of network-wide synchrony.
The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.
arXiv Detail & Related papers (2021-10-15T17:55:04Z) - A biologically plausible neural network for multi-channel Canonical
Correlation Analysis [12.940770779756482]
Cortical pyramidal neurons receive inputs from multiple neural populations and integrate these inputs in separate dendritic compartments.
We seek a multi-channel CCA algorithm that can be implemented in a biologically plausible neural network.
For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local.
arXiv Detail & Related papers (2020-10-01T16:17:53Z) - Recurrent Neural Network Learning of Performance and Intrinsic
Population Dynamics from Sparse Neural Data [77.92736596690297]
We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics.
We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model.
Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
arXiv Detail & Related papers (2020-05-05T14:16:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.