# Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks

## TL;DR

This paper explores an evolutionary pathway for neural networks, showing how they can develop from reactive to proactive and inductive behaviors through specific conditions, supported by in-vitro and in-silico experiments.

## Contribution

It identifies conditions enabling neural networks with spike-timing dependent plasticity to evolve predictive and inductive abilities from reactive strategies.

## Key findings

- Defined conditions for reactive to proactive transition
- Supported evolutionary steps with experimental evidence
- Extended conditions to general neural structures

## Abstract

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of unknown stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Through in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy. We extend these conditions to more general structures.