Leveraging Nature’S Preprocessing Capabilities

Nature has produced elegant and efficient solutions to complex information processing problems over billions of years of evolution. Living organisms have developed sophisticated capabilities to sense, interpret, and respond to their environments in real-time. Understanding and mimicking biology’s methods can inspire more capable, adaptable, and energy-efficient computing systems.

Biological systems leverage massively parallel architectures, stochastic representations, and iterative development processes to preprocess diverse sensory data into useful internal models. Their neural networks self-organize to identify statistical regularities, prime internal states based on context, and focus computational resources on salient stimuli.

As engineers strive to develop artificial intelligence that matches human-level performance in real-world environments, biology offers a existence proof and a rich source of design principles. By studying how natural selection has tuned living systems to preprocess and extract meaning from raw environmental signals, we can create better algorithms and hardware for sensing, learning, control, and decision-making.

Benefits of Bio-Inspired Computation

Engineering systems that implement biologically-inspired information processing mechanisms have a number of key advantages over conventional, human-designed approaches:

• Inherent parallelism for high-throughput and fault-tolerant computation
• Low-power operation via sparse, event-driven processing
• Adaptive “life-long” learning in dynamic, open-ended environments
• Self-organization, development, and evolution for automated design improvements
• Seamless fusion of computation, communication, and control
• Custom silicon implementations of bio-inspired architectures

In contrast with the precise, centralized, clocked control in conventional digital logic, biological substrates exploit distributed emergent coordination for robust performance despite component noise and failures. This allows efficient computation with slow, variable, and unreliable elements while tolerating extreme dynamic range signals.

By emulating biology’s massively parallel, asynchronous, and adaptive style of information processing, engineers can create systems that handle complex real-world environments requiring dynamic, resilient, and power-efficient responses exceeding the capabilities of current artificial intelligence.

Key Principles Behind Natural Information Processing

A few overarching concepts recur across the diverse computational mechanisms present in biological systems:

• Adaptation – Evolutionary algorithms slowly adjust system structure while learning mechanisms update connection weights/properties during an individual’s lifetime for context-appropriate responses.
• Hierarchies – Nested modular architectures allow reuse of low-level circuits in higher-level subsystems, enabling abstraction to manage complexity.
• Asynchrony – Decentralized coordination via propagating signals enables robust performance without a central clock or precise timing.
• Stochasticity – Noisy computation propagates uncertainty while allowing escape from local optima and illusions of false certainty.
• Embodiment – Tight sensorimotor loops enable grounded internal representations aligned with external reality via physical interaction.

These concepts are guiding growing efforts to improve artificial intelligence by grounding them in biologically-plausible architectures. Neuromorphic engineering translates insights from neuroscience into customized hardware for spike-based computation and learning.

Evolution’s Optimization and Control Algorithms

Brains essentially function as prediction engines, constantly matching sensory inputs against learned models to guide adaptive behavior. Biological neural networks self-organize to efficiently represent the hierarchical and temporal structure in natural signals.

For example, early visual areas detect oriented edges, while higher levels represent increasingly complex shape primitives. Recurrent circuits capture dynamical patterns, encoding expectations and detecting anomalies to focus attention. Through evolution, nature has discovered efficient algorithms we can port to machine intelligence.

In particular, recent work highlights biology’s mastery of two key facets of intelligence – discerning patterns in sparse, noisy data and making robust decisions under uncertainty. Studying neuronal dynamics and circuits underpinning sensory perception and decision-making in animals provides guiding principles for developing more capable statistical learning mechanisms.

Sparse Approximate Inference

Making sense of incomplete, irregular sensory data by filling in missing details is akin to the algorithmic challenge of inferring hidden causes from limited indirect measurements. Bayesian inference formalizes this process, but exact solutions are often computationally intractable.

Fortunately, biology employs efficient approximations leveraging population codes and spike timing to represent uncertainty and belief distributions. Evidence accumulation over these neural representations drives probabilistic inference, prediction, attention, and choice behaviors. Implementing similar schemes in artificial systems enables robust perception and decision-making.

Active Sensing & Motor Control

Biological systems cannot passively wait for complete information but must actively gather missing data relevant to current goals through physical sensing and interaction. Closed sensorimotor feedback loops provide internal ground truth signals for adapting neural controllers and refining movements.

This embodied cognition paradigm recognizes information as arising from goal-driven reciprocal coupling between organisms and their environments. Careful coordination between sensory acquisition, neural information processing, and motor actions characterizes biological intelligence missing in today’s AI systems.

Learning to physically probe and manipulate the world like animals promises more efficient and relevant learning. Self-supervised robotic platforms are thus gaining traction as development environments for generally intelligent algorithms.

Mimicking Biology in Computer Systems

As neuroscience continues uncovering efficient computations evolved in biological neural networks, computer engineers are working to port them into artificial systems comprised of conventional silicon or novel nanotechnologies.

This neuromorphic design concept targets custom hardware tailored for spike-based neural processing and plasticity mechanisms. Hybrid systems integrating biological components with electronics also hold promise for leveraging nature’s complex preprocessing wetware.

Bio-Inspired Hardware Design

Rather than simulating biology on rigid serial von Neumann architectures, we can translate key principles into parallel non-von Neumann systems better matched to the mechanisms and dynamics of neural computation.

For example, memristor crossbar arrays enable low-power dot product engines to efficiently implement matrix vector multiplications underlying deep learning algorithms. Mixing analog computational primitives with digital routing logic provides reconfigurable event-driven dataflow architectures for spiking neural networks.

Embedded spike timing dependent plasticity rules then endow these devices with lifelong adaptation capabilities surpassing conventional GPU/TPU/FPGA solutions. Algorithm co-design methodologies that tailor learning systems to exploit the physics underlying novel nano-electronic devices promise extremely efficient implementations.

Software Models of Biological Systems

In tandem with specialized hardware, software models of biologically grounded algorithms are improving robot perception, control, and decision skills – particularly in open-ended learning settings.

For instance, developmental robotics employs computational cognitive architectures seeking to mimic the postsynaptic learning rules, neuronal dynamics, and cortical/subcortical structures underlying animal intelligence. Such systems demonstrate more efficient incremental learning of complex behavioral repertoires.

Renewed interest in integrating active vision and hierarchical sensory processing into neural network models is also evidenced by growing work on spatially invariant multilayer sparse coding, predictive coding, and attention mechanisms for perception and control.

Hybrid Computational/Biological Systems

Rather than solely mimic biology, we can directly harness nature’s tried and tested solutions as part of engineered systems. Recent advances in neurotechnology enable two-way communication with living neural tissue integrated into bio-hybrid machines.

Bidirectional brain-computer interfaces now demonstrate real-time decoding of motor intents from, and tactile feedback to, cortical neuronal ensembles. Researchers have also shown that coupled CPU-brain systems can outperform either substrate alone at reinforcement learning control tasks.

Integrating biological and artificial intelligence combines the strengths of both while compensating individual weaknesses. Work in this area also promises new insights into natural cognition to further improve computational models.

Frontiers and Challenges in Bio-Inspired Computing

While active research integrates neuroscience insights into artificial systems, many open questions remain on how to best leverage nature’s innovations:

Integrating Diverse Biological Insights

Work across biology at different scales must inform neuromorphic computing – from biomolecular mechanisms behind stochastic single neuron dynamics up to systems neuroscience discoveries on ensemble coding/computations.

Learning rules adapted from synaptic plasticity research can train spiking networks that leverage findings on dendritic and neuronal nonlinearities. Actual soma/axon morphologies and brain connectomes provide architectural blueprints for structured neuromorphic chips.

Cross-disciplinary collaboration will be key to successfully unifying these diverse insights into complete computational theories of neural information processing amendable to hardware realization.

Scaling Up Bio-Inspired Systems

While small neuromorphic devices have shown promise, considerable innovation is still needed to scale up hardware neural networks to the dimensions required for complex real-world intelligence. This includes both expanding standalone systems as well as networking specialized modules into larger architectures.

With transistors no longer shrinking appreciably, 3D integration and photonic communication hold promise for dense scalable spike-based computing platforms. Algorithm-hardware co-design to tailor learning mechanisms to emergent device physics is also part of this solution.

Neuromorphic designs must utilize developmental and evolutionary paradigms exhibiting such scaling in biology – thereby growing complexity from simpler precursors rather than engineering monolithic structures.

Towards Lifelike Artificial Intelligence

While loosely bio-inspired AI has achieved notable recent success on narrow tasks, reproducing general human/animal intelligence requires grounding models in empirically supported theories of cortical algorithms and circuits.

This mandates celebrating biological complexity in computational models rather than abstracting it away. For instance, approximating neuronal voltage traces rather than spikes, incorporating biophysically detailed dendrites and short-term plasticity dynamics, and modeling cortical laminar circuit motifs will better match neural network functionality.

Developing more aligned embodied cognitive architectures combining evolutionary and developmental growth processes provides another pathway towards artificial general intelligence with lifelike robustness, adaptability, and intuition.

Conclusion: Partnering With Nature for Better Technology

Understanding efficient biological information processing and harnessing such principles in artificial systems holds tremendous promise. Natural evolution has maximized sensorimotor control, decision-making, and learning capabilities within stringent size, weight, power and complexity constraints.

By studying and implementing nature’s solutions instead of solely engineering ad hoc human-designed algorithms, we can create more capable real-world AI on practical hardware platforms. This bio-inspired approach leveraging nature’s preprocessed sensing pipelines promises continued advances in machine intelligence towards more flexible automation and augmented human cognition.