Research
Advancing the frontiers of memory-reasoning integration through interdisciplinary research in neuroscience, mathematics, and robotics.
Research directions and objectives are shared here at a high level. Specific architectures, training procedures, and implementation details that form the VOLTAIC platform are proprietary to Verkko Robotics Ltd and protected under the Verkko Research Programme.
Verkko Research Areas
Memory-Reasoning Integration
Active ResearchWe're developing novel neural architectures where memory storage and reasoning processes are unified within individual nodes, inspired by how biological neurons integrate past experiences with current processing.
Research Objectives
- Design memory manifolds that deform based on context and experience
- Implement real-time memory-reasoning loops in hardware
- Develop mathematical frameworks for adaptive memory structures
Biologically-Inspired Plasticity
OngoingOur research investigates how biological principles of neural plasticity can inform the design of adaptive systems that learn and evolve throughout their operational lifetime.
Research Objectives
- Model synaptic adaptation mechanisms in artificial systems
- Implement growth-based learning in spiking neural networks
- Create environmentally responsive plasticity rules
Neuromorphic Hardware Implementation
DevelopmentWe're investigating hardware substrates for efficient implementation of adaptive spiking systems, focusing on event-driven processing and energy efficiency at the edge.
Research Objectives
- Design execution pathways for sparse spiking workloads
- Optimize event-driven processing architectures
- Achieve ultra-low power consumption for edge deployment
Embodied Cognitive Systems
PrototypingInvestigating how physical deployment environments shape the requirements for on-device learning — where the model must adapt continuously to conditions that cannot be anticipated at training time.
Research Objectives
- Develop sensorimotor learning algorithms for continuous adaptation
- Create adaptive control systems for dynamic environments
- Demonstrate sustained real-world learning over extended horizons
Measured on neuromorphic silicon
Spike-native efficiency is not a projection. Published results on neuromorphic hardware already show inference in the microjoule to sub-millijoule range on keyword, gesture and vision workloads — an existence proof for the energy regime that event-driven architectures target.
Intel Loihi
0.24–0.31 µJ
per inference
0.8–1.3 ms latency on keyword and gesture workloads.
Intel Loihi 2
0.33–0.46 mJ
per inference
91–97% accuracy on vision-style classification tasks.
SpiNNaker 2
0.027 nJ
per spike
An energy floor for the spike event itself, independent of model.
BrainChip Akida
1.48 µJ
per inference
92% accuracy on keyword spotting in a commercial edge part.
Figures are reported by the respective hardware programmes and their published benchmark literature. They characterise the neuromorphic substrate rather than any Verkko system.
Open problems
Four questions define the neuromorphic frontier as we see it. None is solved, and we would rather publish the list than imply otherwise — they are also where collaboration is most useful to us.
Attention sparsification at scale
Carrying enforced sparsity through multi-head self-attention without the quality collapse that has defeated earlier attempts. This is the active step in the VOLTAIC roadmap.
On-device and online training
Learning in the field through ALIF surrogate gradients, inside the memory budget of a device that also has to keep running inference.
Cross-modal spike fusion at runtime
Fusing text, vision, audio and structured signals under one spike contract while they arrive, rather than aligning them offline.
Continuous learning in human–swarm context
Sustained adaptation across a population of agents sharing an environment with people, where what is worth remembering is not defined in advance.
Related Research Domains
Neuromorphic Computing
Brain-inspired computing architectures that process information using spikes and events
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Davies et al. (2018)
IEEE Micro
Intel's Loihi chip demonstrates large-scale neuromorphic computing with on-chip learning capabilities.
Training Deep Spiking Neural Networks Using Backpropagation
Lee et al. (2016)
Frontiers in Neuroscience
Breakthrough in training SNNs using backpropagation through time, enabling deeper architectures.
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
Indiveri & Liu (2015)
Proceedings of the IEEE
Comprehensive review of neuromorphic circuits for autonomous cognitive applications.
Synaptic Plasticity & Memory
How biological neural networks adapt and form memories through synaptic modifications
Synaptic Plasticity: Taming the Beast
Abbott & Nelson (2000)
Nature Neuroscience
Foundational work on understanding different forms of synaptic plasticity and their roles in learning.
The Self-Tuning Neuron: Synaptic Scaling of Excitatory Synapses
Turrigiano (2008)
Cell
Discovery of homeostatic plasticity mechanisms that maintain neural network stability.
Spike Timing-Dependent Plasticity: A Hebbian Learning Rule
Bi & Poo (1998)
Annual Review of Neuroscience
Seminal work establishing STDP as a fundamental learning mechanism in biological networks.
Manifold Learning & Geometry
Mathematical frameworks for learning on non-Euclidean structures and manifolds
Geometric Deep Learning: Going Beyond Euclidean Data
Bronstein et al. (2017)
IEEE Signal Processing Magazine
Comprehensive framework for extending deep learning to non-Euclidean domains.
Representation Learning on Graphs: Methods and Applications
Hamilton et al. (2017)
IEEE Data Engineering Bulletin
Survey of graph neural networks and their applications to complex structured data.
Neural Ordinary Differential Equations
Chen et al. (2018)
NeurIPS
Continuous-depth neural networks that treat network depth as a continuous dimension.
Embodied AI & Robotics
How physical embodiment affects learning and intelligence in artificial systems
How the Body Shapes the Way We Think
Pfeifer & Bongard (2006)
MIT Press
Foundational book on embodied intelligence and morphological computation.
Developmental Robotics: From Babies to Robots
Lungarella et al. (2003)
Network: Computation in Neural Systems
Framework for understanding how robots can develop intelligence through interaction.
The Sensorimotor Approach to Perception
O'Regan & Noë (2001)
Behavioral and Brain Sciences
Theory that perception emerges from sensorimotor contingencies and active exploration.