Single Circuit in V1 Capable of Switching Contexts during Movement Using an Inhibitory Population as a Switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.

Entropy considerations in improved circuits for a biologically-inspired random pulse computer

We present five novel or modified circuits intended for building a universal computer based on random pulse computing (RPC) paradigm, a biologically-inspired way of computation in which variable is represented by a frequency of a random pulse train (RPT) rather than by a logic state. For the first time we investigate operation of RPC circuits from the point of entropy. In particular, we introduce entropy budget criterion (EBC) to reliably predict whether it is even possible to create a deterministic circuit for a given mathematical operation and show its relevance to numerical precision of calculations. Based on insights gained from the EBC, unlike in the previous art, where randomness is obtained from electronics noise or a pseudorandom shift register while processing circuitry is deterministic, in our approach both variable generation and signal processing rely on the random flip-flop (RFF) whose randomness is derived from a fundamentally random quantum process. This approach offers an advantage in higher precision, better randomness of the output and conceptual simplicity of circuits.

Semantic Reasoning of Product Biologically Inspired Design Based on BERT

Bionic reasoning is a significant process in product biologically inspired design (BID), in which designers search for creatures and products that are matched for design. Several studies have tried to assist designers in bionic reasoning, but there are still limits. Designers’ bionic reasoning thinking in product BID is vague, and there is a lack of fuzzy semantic search methods at the sentence level. This study tries to assist designers’ bionic semantic reasoning in product BID. First, experiments were conducted to determine the designer’s bionic reasoning thinking in top-down and bottom-up processes. Bionic mapping relationships, including affective perception, form, function, material, and environment, were obtained. Second, the bidirectional encoder representations from transformers (BERT) pretraining model was used to calculate the semantic similarity of product description sentences and biological sentences so that designers could choose the high-ranked results to finish bionic reasoning. Finally, we used a product BID example to show the bionic semantic reasoning process and verify the feasibility of the method.

Nanobot: Artificial Intelligence, Drug Delivery and Diagnostic Approach

The design, construction, and programming of robots with overall dimensions of less than a few micrometres, as well as the programmable assembly of nanoscale items, are all part of nanorobotics. Nanobots are the next generation of medication delivery systems, as well as the ultimate nanoelectromechanical systems. Nano bioelectronics are used as the foundation for manufacturing integrated system devices with embedded nano biosensors and actuators in the nanorobot architectural paradigm, which aids in medical target identification and drug delivery. Nanotechnology advances have made it possible to create nanosensors and actuators using nano bioelectronics and biologically inspired devices. The creation of nanobots is fascinated by both top-down and bottom-up approaches. The qualities, method of synthesis, mechanism of action, element, and application of nanobots for the treatment of nervine disorders, wound healing, cancer diagnosis study, and congenital disease were highlighted in this review. This method gives you a lot of control over the situation and helps with sickness diagnosis.

Multi-Frequency Image Completion via a Biologically-Inspired Sub-Riemannian Model with Frequency and Phase

We present a novel cortically-inspired image completion algorithm. It uses five-dimensional sub-Riemannian cortical geometry, modeling the orientation, spatial frequency and phase-selective behavior of the cells in the visual cortex. The algorithm extracts the orientation, frequency and phase information existing in a given two-dimensional corrupted input image via a Gabor transform and represents those values in terms of cortical cell output responses in the model geometry. Then, it performs completion via a diffusion concentrated in a neighborhood along the neural connections within the model geometry. The diffusion models the activity propagation integrating orientation, frequency and phase features along the neural connections. Finally, the algorithm transforms the diffused and completed output responses back to the two-dimensional image plane.

ARTFLOW: A Fast, Biologically Inspired Neural Network That Learns Optic Flow Templates for Self-Motion Estimation

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.

A Neurodynamic Model of Saliency Prediction in V1

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.