While the efficient coding framework provides valuable insights into how sensory information is encoded in neural networks with minimal metabolic cost, there are potential limitations in explaining the full diversity of neural response properties observed in biological networks. Some of these limitations include:
Temporal Dynamics: Efficient coding models often focus on static stimuli or simplified temporal dynamics, whereas biological neural networks exhibit complex temporal patterns and dynamics. Incorporating more realistic temporal dynamics, such as adaptation, facilitation, and oscillations, may be necessary to capture the full range of neural responses.
Nonlinear Interactions: Biological neural networks exhibit nonlinear interactions between neurons, such as dendritic processing, synaptic plasticity, and network feedback loops. Efficient coding models typically assume linear integration of inputs, which may not fully capture the nonlinearities present in biological systems.
Neuromodulation: Efficient coding models often do not explicitly consider the role of neuromodulatory systems in shaping neural responses. Neuromodulators can dynamically modulate network properties, such as synaptic strength, excitability, and plasticity, which can significantly impact information processing in the brain.
Sparse Coding: While efficient coding emphasizes the importance of sparse representations for reducing redundancy, biological networks also exhibit distributed and overlapping representations. Exploring the balance between sparse and distributed coding in neural networks is essential for a more comprehensive understanding of information processing.
By addressing these limitations and incorporating more biologically realistic features into the efficient coding framework, we can enhance its explanatory power and better capture the complexity of neural response properties observed in biological networks.