The GAIN Model: A Nature - Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model Gage K. R. Hooper Independent Researcher Future Aerospace Engineering Student, Embry - Riddle Aeronautical University May 3 1, 2025 1 Abstract While many neural networks focus on layers to process information, the GAIN model uses a grid - based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and im prove their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient. Early models of SSNs, such as the Hodgkin - Huxley model (1952), were detailed and capable of replicating the exact dynamics of neuronal spiking, considering every ion channel, but it was too computationally inefficie nt. A computational model that can simulate the function of neurons. The activation of neurons determined by its action potential when a neuron's difference between interior and exterior voltages (membrane potential) rapidly increases and decreases. In response to limitations seen in these models, Eugene Izhikevich (2003) introduced a spiking neural network model, achieving a balance between biological plausibility and computational efficiency (See Appendix A). The Izhikevich model can reproduce neuron behaviors while remaining computationally lightweight, resulting in it being widely adopted for large - scale simulations.

The paper proposes a mechanism for explaining Bayesian inference and network plasticity in the brain using an algorithm very similar to Stochastic Gradient Langevin Dynamics. Clarity: The paper is well written. Even though my background is machine learning and not neuroscience, I was able to follow most of the paper. Originality: The mechanism itself is well studied in the machine learning literature where it is called Stochastic Gradient Langevin Dynamics (SGLD) (see Ref[1] and analysis in Ref[2]). This is also well known in physics where it is usually called the Langevin equation with noisy force (see e.g.

Summary Recurrent networks of leaky integrate-and-fire neurons with (spike frequency) adaptation are trained with backpropagation-through-time (adapted to spiking neurons) to perform digit recognition (temporal MNIST), speech recognition (TIMIT), learning to learn simple regression tasks and learning to find a goal location in simple navigation tasks. The performances on temporal MNIST and TIMIT are similar to the one of LSTM-networks. The simple regression and navigation task demonstrate that connection weights exist that allow to solve simple tasks using the short-term memory of spiking neurons with adaptation, without the need of ongoing synaptic plasticity. Quality The selection of tasks is interesting, the results are convincing and the supplementary information seems to provide sufficient details to reproduce them. But the writing could be improved significantly.

This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, addressing the challenges of temporal locality and biological plausibility in training spiking neural networks. Our approach leverages the inherent connection between backpropagation through time and STDP, offering a computationally efficient alternative that maintains the ability to capture long-range dependencies. We evaluate BIM on language modeling, speech recognition, and biomedical signal analysis tasks, demonstrating competitive performance against traditional methods while adhering to biological learning principles. Results show improved energy efficiency and potential for neuromorphic hardware implementation. BIM not only advances the field of biologically plausible machine learning but also provides insights into the mechanisms of temporal information processing in biological neural networks.

There is a lot of fear about robots replacing human. But maybe it should be the machines worrying about us. Swedish scientists have created the world's first'living computer' that is made out of human brain tissue. It composes of 16 organoids, or clumps of brain cells that were grown in a lab, which send information between each other. They work much like a traditional computer chip - sending and receiving signals through their neurons that act like circuits.

In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization method cannot be used. In this paper, we study a stochastic model for supervised learning in BNNs. We show that a (continuous) gradient step occurs approximately when each learning opportunity is processed by many local updates. This result suggests that stochastic gradient descent may indeed play a role in optimizing BNNs.

The pyloric Central Pattern Generator of the crustacean stomatogastric ganglion is a well-defined biological neural network. This 14-neuron network is modulated by many inputs. These inputs reconfigure the network to produce multiple output patterns by three simple mechanisms: 1) detennining which cells are active; 2) modulating the synaptic efficacy; 3) changing the intrinsic response properties of individual neurons. The importance of modifiable intrinsic response properties of neurons for network function and modulation is discussed.

We present a neural network simulation which we implemented on the massively parallel Connection Machine 2. In contrast to previous work, this simulator is based on biologically realistic neu(cid:173) rons with nontrivial single-cell dynamics, high connectivity with a structure modelled in agreement with biological data, and preser(cid:173) vation of the temporal dynamics of spike interactions. We simulate neural networks of 16,384 neurons coupled by about 1000 synapses per neuron, and estimate the performance for much larger systems. Communication between neurons is identified as the computation(cid:173) ally most demanding task and we present a novel method to over(cid:173) come this bottleneck. The simulator has already been used to study the primary visual system of the cat.

Recently, significant progress has been made regarding the statistical understanding of artificial neural networks (ANNs). ANNs are motivated by the functioning of the brain, but differ in several crucial aspects. In particular, the locality in the updating rule of the connection parameters in biological neural networks (BNNs) makes it biologically implausible that the learning of the brain is based on gradient descent. In this work, we look at the brain as a statistical method for supervised learning. The main contribution is to relate the local updating rule of the connection parameters in BNNs to a zero-order optimization method. It is shown that the expected values of the iterates implement a modification of gradient descent.